ML2-Lecture13
In [1]:
# Simple KNN on MNIST
from sklearn.cluster import KMeans
from sklearn.metrics import accuracy_score
from tensorflow.keras.datasets import mnist
import numpy as np
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x = np.concatenate((x_train, x_test))
y = np.concatenate((y_train, y_test))
x = x.reshape((x.shape[0], -1))
x = np.divide(x, 255.)
# 10 clusters
n_clusters = 10
# Runs in parallel 4 CPUs
kmeans = KMeans(n_clusters=n_clusters, n_init=20, n_jobs=4)
# Train K-Means.
y_pred_kmeans = kmeans.fit_predict(x)
# Evaluate the K-Means clustering accuracy.
accuracy = accuracy_score(y, y_pred_kmeans)
ERROR:root:Internal Python error in the inspect module.
Below is the traceback from this internal error.
KeyboardInterrupt
In [2]:
# Design autoencoder and decoder
from tensorflow.keras.layers import Input, Dense
from tensorflow.keras.models import Model
from tensorflow.keras.datasets import mnist
import numpy as np
# this is the size of our encoded representations
encoding_dim = 32 # 32 floats -> compression of factor 24.5, assuming the input is 784 floats
# this is our input placeholder
input_img = Input(shape=(784,))
# “encoded” is the encoded representation of the input
encoded = Dense(encoding_dim, activation=’relu’)(input_img)
# “decoded” is the lossy reconstruction of the input
decoded = Dense(784, activation=’sigmoid’)(encoded)
# this model maps an input to its reconstruction
autoencoder = Model(input_img, decoded)
In [3]:
# Let’s also create a separate encoder model:
# this model maps an input to its encoded representation
encoder = Model(input_img, encoded)
In [4]:
# create a placeholder for an encoded (32-dimensional) input
encoded_input = Input(shape=(encoding_dim,))
# retrieve the last layer of the autoencoder model
decoder_layer = autoencoder.layers[-1]
# create the decoder model
decoder = Model(encoded_input, decoder_layer(encoded_input))
In [5]:
# Insert model
autoencoder.compile(optimizer=’adam’, loss=’binary_crossentropy’)
In [6]:
(x_train, _), (x_test, _) = mnist.load_data()
# We will normalize all values between 0 and 1 and we will flatten the 28×28 images into vectors of size 784.
x_train = x_train.astype(‘float32’) / 255.
x_test = x_test.astype(‘float32’) / 255.
x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
print (x_train.shape)
print (x_test.shape)
(60000, 784)
(10000, 784)
In [7]:
# let’s train our autoencoder for 50 epochs:
autoencoder.fit(x_train, x_train, epochs=50, batch_size=256, shuffle=True, validation_data=(x_test, x_test))
Train on 60000 samples, validate on 10000 samples
Epoch 1/50
60000/60000 [==============================] – 6s 100us/sample – loss: 0.2786 – val_loss: 0.1933
Epoch 2/50
60000/60000 [==============================] – 5s 81us/sample – loss: 0.1725 – val_loss: 0.1539
Epoch 3/50
60000/60000 [==============================] – 5s 82us/sample – loss: 0.1444 – val_loss: 0.1338
Epoch 4/50
60000/60000 [==============================] – 4s 73us/sample – loss: 0.1287 – val_loss: 0.1215
Epoch 5/50
60000/60000 [==============================] – 4s 63us/sample – loss: 0.1184 – val_loss: 0.1132
Epoch 6/50
60000/60000 [==============================] – 4s 66us/sample – loss: 0.1112 – val_loss: 0.1072
Epoch 7/50
60000/60000 [==============================] – 4s 68us/sample – loss: 0.1062 – val_loss: 0.1031
Epoch 8/50
60000/60000 [==============================] – 4s 70us/sample – loss: 0.1024 – val_loss: 0.0999
Epoch 9/50
60000/60000 [==============================] – 4s 67us/sample – loss: 0.0997 – val_loss: 0.0975
Epoch 10/50
60000/60000 [==============================] – 4s 71us/sample – loss: 0.0977 – val_loss: 0.0960
Epoch 11/50
60000/60000 [==============================] – 4s 67us/sample – loss: 0.0964 – val_loss: 0.0947
Epoch 12/50
60000/60000 [==============================] – 4s 68us/sample – loss: 0.0955 – val_loss: 0.0940
Epoch 13/50
60000/60000 [==============================] – 4s 72us/sample – loss: 0.0949 – val_loss: 0.0934
Epoch 14/50
60000/60000 [==============================] – 4s 72us/sample – loss: 0.0945 – val_loss: 0.0932
Epoch 15/50
60000/60000 [==============================] – 5s 80us/sample – loss: 0.0942 – val_loss: 0.0929
Epoch 16/50
60000/60000 [==============================] – 4s 69us/sample – loss: 0.0940 – val_loss: 0.0927
Epoch 17/50
60000/60000 [==============================] – 4s 71us/sample – loss: 0.0938 – val_loss: 0.0925
Epoch 18/50
60000/60000 [==============================] – 5s 77us/sample – loss: 0.0937 – val_loss: 0.0925
Epoch 19/50
60000/60000 [==============================] – 4s 66us/sample – loss: 0.0936 – val_loss: 0.0923
Epoch 20/50
60000/60000 [==============================] – 4s 68us/sample – loss: 0.0935 – val_loss: 0.0923
Epoch 21/50
60000/60000 [==============================] – 4s 72us/sample – loss: 0.0934 – val_loss: 0.0921A: 0s – loss: 0.093
Epoch 22/50
60000/60000 [==============================] – 4s 70us/sample – loss: 0.0933 – val_loss: 0.0921
Epoch 23/50
60000/60000 [==============================] – 4s 62us/sample – loss: 0.0932 – val_loss: 0.0920
Epoch 24/50
60000/60000 [==============================] – 4s 63us/sample – loss: 0.0932 – val_loss: 0.0920
Epoch 25/50
60000/60000 [==============================] – 4s 62us/sample – loss: 0.0931 – val_loss: 0.0919
Epoch 26/50
60000/60000 [==============================] – 3s 58us/sample – loss: 0.0931 – val_loss: 0.0918
Epoch 27/50
60000/60000 [==============================] – 4s 70us/sample – loss: 0.0930 – val_loss: 0.0918
Epoch 28/50
60000/60000 [==============================] – 4s 70us/sample – loss: 0.0930 – val_loss: 0.0919
Epoch 29/50
60000/60000 [==============================] – 4s 73us/sample – loss: 0.0930 – val_loss: 0.0918
Epoch 30/50
60000/60000 [==============================] – 6s 98us/sample – loss: 0.0929 – val_loss: 0.0917
Epoch 31/50
60000/60000 [==============================] – 6s 100us/sample – loss: 0.0929 – val_loss: 0.0917
Epoch 32/50
60000/60000 [==============================] – 5s 78us/sample – loss: 0.0929 – val_loss: 0.0917
Epoch 33/50
60000/60000 [==============================] – 5s 77us/sample – loss: 0.0929 – val_loss: 0.0917
Epoch 34/50
60000/60000 [==============================] – 4s 73us/sample – loss: 0.0928 – val_loss: 0.0917
Epoch 35/50
60000/60000 [==============================] – 4s 67us/sample – loss: 0.0928 – val_loss: 0.0917
Epoch 36/50
60000/60000 [==============================] – 4s 70us/sample – loss: 0.0928 – val_loss: 0.0916
Epoch 37/50
60000/60000 [==============================] – 4s 72us/sample – loss: 0.0928 – val_loss: 0.0916
Epoch 38/50
60000/60000 [==============================] – 4s 69us/sample – loss: 0.0928 – val_loss: 0.0916
Epoch 39/50
60000/60000 [==============================] – 4s 70us/sample – loss: 0.0928 – val_loss: 0.0916
Epoch 40/50
60000/60000 [==============================] – 4s 60us/sample – loss: 0.0927 – val_loss: 0.0916
Epoch 41/50
60000/60000 [==============================] – 4s 73us/sample – loss: 0.0927 – val_loss: 0.0916
Epoch 42/50
60000/60000 [==============================] – 5s 76us/sample – loss: 0.0927 – val_loss: 0.0915
Epoch 43/50
60000/60000 [==============================] – 4s 73us/sample – loss: 0.0927 – val_loss: 0.0916
Epoch 44/50
60000/60000 [==============================] – 4s 68us/sample – loss: 0.0927 – val_loss: 0.0915
Epoch 45/50
60000/60000 [==============================] – 4s 62us/sample – loss: 0.0927 – val_loss: 0.0916
Epoch 46/50
60000/60000 [==============================] – 4s 64us/sample – loss: 0.0926 – val_loss: 0.0916
Epoch 47/50
60000/60000 [==============================] – 4s 63us/sample – loss: 0.0927 – val_loss: 0.0915
Epoch 48/50
60000/60000 [==============================] – 4s 62us/sample – loss: 0.0926 – val_loss: 0.0915
Epoch 49/50
60000/60000 [==============================] – 4s 60us/sample – loss: 0.0926 – val_loss: 0.0915: 0s – los
Epoch 50/50
60000/60000 [==============================] – 4s 66us/sample – loss: 0.0926 – val_loss: 0.0914
Out[7]:
In [8]:
# After 50 epochs, the autoencoder seems to reach a stable train/test loss value of about 0.09.
# We can try to visualize the reconstructed inputs and the encoded representations. We will use Matplotlib.
# encode and decode some digits
# note that we take them from the *test* set
encoded_imgs = encoder.predict(x_test)
decoded_imgs = decoder.predict(encoded_imgs)
In [9]:
# use Matplotlib
import matplotlib.pyplot as plt
n = 10 # how many digits we will display
plt.figure(figsize=(20, 4))
for i in range(n):
# display original
ax = plt.subplot(2, n, i + 1)
plt.imshow(x_test[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# display reconstruction
ax = plt.subplot(2, n, i + 1 + n)
plt.imshow(decoded_imgs[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
In [10]:
# deeper model
input_img = Input(shape=(784,))
encoded = Dense(128, activation=’relu’)(input_img)
encoded = Dense(64, activation=’relu’)(encoded)
encoded = Dense(32, activation=’relu’)(encoded)
decoded = Dense(64, activation=’relu’)(encoded)
decoded = Dense(128, activation=’relu’)(decoded)
decoded = Dense(784, activation=’sigmoid’)(decoded)
autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer=’adam’, loss=’binary_crossentropy’)
encoder = Model(input_img, encoded)
encoded_input = Input(shape=(encoding_dim,))
#decoder_layer = autoencoder.layers[-1]
#decoder = Model(encoded_input, decoder_layer(encoded_input))
autoencoder.fit(x_train, x_train, epochs=50, batch_size=256, shuffle=True, validation_data=(x_test, x_test))
Train on 60000 samples, validate on 10000 samples
Epoch 1/50
60000/60000 [==============================] – 7s 116us/sample – loss: 0.2474 – val_loss: 0.1673
Epoch 2/50
60000/60000 [==============================] – 6s 106us/sample – loss: 0.1491 – val_loss: 0.1351
Epoch 3/50
60000/60000 [==============================] – 9s 148us/sample – loss: 0.1298 – val_loss: 0.1230loss: 0.129
Epoch 4/50
60000/60000 [==============================] – 8s 132us/sample – loss: 0.1210 – val_loss: 0.1163: 0s –
Epoch 5/50
60000/60000 [==============================] – 8s 134us/sample – loss: 0.1154 – val_loss: 0.1124
Epoch 6/50
60000/60000 [==============================] – 8s 133us/sample – loss: 0.1114 – val_loss: 0.1081
Epoch 7/50
60000/60000 [==============================] – 8s 128us/sample – loss: 0.1080 – val_loss: 0.1050
Epoch 8/50
60000/60000 [==============================] – 7s 117us/sample – loss: 0.1052 – val_loss: 0.1028
Epoch 9/50
60000/60000 [==============================] – 7s 121us/sample – loss: 0.1030 – val_loss: 0.1007
Epoch 10/50
60000/60000 [==============================] – 7s 113us/sample – loss: 0.1010 – val_loss: 0.0991
Epoch 11/50
60000/60000 [==============================] – 7s 123us/sample – loss: 0.0993 – val_loss: 0.0976
Epoch 12/50
60000/60000 [==============================] – 8s 126us/sample – loss: 0.0979 – val_loss: 0.0965
Epoch 13/50
60000/60000 [==============================] – 7s 123us/sample – loss: 0.0968 – val_loss: 0.0953
Epoch 14/50
60000/60000 [==============================] – 6s 98us/sample – loss: 0.0958 – val_loss: 0.0944
Epoch 15/50
60000/60000 [==============================] – 6s 97us/sample – loss: 0.0949 – val_loss: 0.0940
Epoch 16/50
60000/60000 [==============================] – 6s 103us/sample – loss: 0.0942 – val_loss: 0.0934
Epoch 17/50
60000/60000 [==============================] – 6s 93us/sample – loss: 0.0935 – val_loss: 0.0925
Epoch 18/50
60000/60000 [==============================] – 6s 97us/sample – loss: 0.0929 – val_loss: 0.0918
Epoch 19/50
60000/60000 [==============================] – 5s 91us/sample – loss: 0.0923 – val_loss: 0.0919
Epoch 20/50
60000/60000 [==============================] – 5s 91us/sample – loss: 0.0918 – val_loss: 0.0906
Epoch 21/50
60000/60000 [==============================] – 6s 93us/sample – loss: 0.0913 – val_loss: 0.0903
Epoch 22/50
60000/60000 [==============================] – 6s 95us/sample – loss: 0.0908 – val_loss: 0.0897
Epoch 23/50
60000/60000 [==============================] – 5s 91us/sample – loss: 0.0904 – val_loss: 0.0895ETA: 3s – ETA: 2s
Epoch 24/50
60000/60000 [==============================] – 6s 99us/sample – loss: 0.0900 – val_loss: 0.0891
Epoch 25/50
60000/60000 [==============================] – 6s 97us/sample – loss: 0.0897 – val_loss: 0.0890
Epoch 26/50
60000/60000 [==============================] – 6s 93us/sample – loss: 0.0893 – val_loss: 0.0886
Epoch 27/50
60000/60000 [==============================] – 6s 95us/sample – loss: 0.0889 – val_loss: 0.0883
Epoch 28/50
60000/60000 [==============================] – 6s 95us/sample – loss: 0.0886 – val_loss: 0.0878
Epoch 29/50
60000/60000 [==============================] – 6s 92us/sample – loss: 0.0881 – val_loss: 0.0873
Epoch 30/50
60000/60000 [==============================] – 6s 94us/sample – loss: 0.0877 – val_loss: 0.0873
Epoch 31/50
60000/60000 [==============================] – 6s 92us/sample – loss: 0.0873 – val_loss: 0.0866
Epoch 32/50
60000/60000 [==============================] – 5s 92us/sample – loss: 0.0869 – val_loss: 0.0864
Epoch 33/50
60000/60000 [==============================] – 6s 96us/sample – loss: 0.0865 – val_loss: 0.0859
Epoch 34/50
60000/60000 [==============================] – 5s 91us/sample – loss: 0.0862 – val_loss: 0.0856
Epoch 35/50
60000/60000 [==============================] – 6s 93us/sample – loss: 0.0859 – val_loss: 0.0857
Epoch 36/50
60000/60000 [==============================] – 6s 94us/sample – loss: 0.0857 – val_loss: 0.0850
Epoch 37/50
60000/60000 [==============================] – 6s 96us/sample – loss: 0.0853 – val_loss: 0.0846
Epoch 38/50
60000/60000 [==============================] – 6s 93us/sample – loss: 0.0851 – val_loss: 0.0847
Epoch 39/50
60000/60000 [==============================] – 6s 98us/sample – loss: 0.0849 – val_loss: 0.0844
Epoch 40/50
60000/60000 [==============================] – 5s 84us/sample – loss: 0.0847 – val_loss: 0.0844
Epoch 41/50
60000/60000 [==============================] – 5s 84us/sample – loss: 0.0845 – val_loss: 0.0839
Epoch 42/50
60000/60000 [==============================] – 6s 98us/sample – loss: 0.0843 – val_loss: 0.0843
Epoch 43/50
60000/60000 [==============================] – 6s 99us/sample – loss: 0.0842 – val_loss: 0.0837
Epoch 44/50
60000/60000 [==============================] – 6s 93us/sample – loss: 0.0840 – val_loss: 0.0837
Epoch 45/50
60000/60000 [==============================] – 6s 96us/sample – loss: 0.0839 – val_loss: 0.0833
Epoch 46/50
60000/60000 [==============================] – 6s 92us/sample – loss: 0.0837 – val_loss: 0.0833
Epoch 47/50
60000/60000 [==============================] – 6s 98us/sample – loss: 0.0836 – val_loss: 0.0833
Epoch 48/50
60000/60000 [==============================] – 7s 111us/sample – loss: 0.0835 – val_loss: 0.0830
Epoch 49/50
60000/60000 [==============================] – 6s 94us/sample – loss: 0.0834 – val_loss: 0.0830
Epoch 50/50
60000/60000 [==============================] – 6s 97us/sample – loss: 0.0833 – val_loss: 0.0829
Out[10]:
In [11]:
encoded_imgs = encoder.predict(x_test)
decoded_imgs = decoder.predict(encoded_imgs)
import matplotlib.pyplot as plt
n = 10 # how many digits we will display
plt.figure(figsize=(20, 4))
for i in range(n):
# display original
ax = plt.subplot(2, n, i + 1)
plt.imshow(x_test[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# display reconstruction
ax = plt.subplot(2, n, i + 1 + n)
plt.imshow(decoded_imgs[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
In [13]:
import pandas as pd
# Regression example
def get_data():
#get train data
train_data_path =’C:/My Courses/Spring2020/ANLY535/Lecture13_Regression_Unsupervised/train.csv’
train = pd.read_csv(train_data_path)
#get test data
test_data_path =’C:/My Courses/Spring2020/ANLY535/Lecture13_Regression_Unsupervised/test.csv’
test = pd.read_csv(test_data_path)
return train , test
def get_combined_data():
#reading train data
train , test = get_data()
target = train.SalePrice
train.drop([‘SalePrice’],axis = 1 , inplace = True)
combined = train.append(test)
combined.reset_index(inplace=True)
combined.drop([‘index’, ‘Id’], inplace=True, axis=1)
return combined, target
#Load train and test data into pandas DataFrames
train_data, test_data = get_data()
#Combine train and test data to process them together
combined, target = get_combined_data()
In [14]:
combined.describe()
Out[14]:
MSSubClass LotFrontage LotArea OverallQual OverallCond YearBuilt YearRemodAdd MasVnrArea BsmtFinSF1 BsmtFinSF2 … GarageArea WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch PoolArea MiscVal MoSold YrSold
count 2919.000000 2433.000000 2919.000000 2919.000000 2919.000000 2919.000000 2919.000000 2896.000000 2918.000000 2918.000000 … 2918.000000 2919.000000 2919.000000 2919.000000 2919.000000 2919.000000 2919.000000 2919.000000 2919.000000 2919.000000
mean 57.137718 69.305795 10168.114080 6.089072 5.564577 1971.312778 1984.264474 102.201312 441.423235 49.582248 … 472.874572 93.709832 47.486811 23.098321 2.602261 16.062350 2.251799 50.825968 6.213087 2007.792737
std 42.517628 23.344905 7886.996359 1.409947 1.113131 30.291442 20.894344 179.334253 455.610826 169.205611 … 215.394815 126.526589 67.575493 64.244246 25.188169 56.184365 35.663946 567.402211 2.714762 1.314964
min 20.000000 21.000000 1300.000000 1.000000 1.000000 1872.000000 1950.000000 0.000000 0.000000 0.000000 … 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 2006.000000
25% 20.000000 59.000000 7478.000000 5.000000 5.000000 1953.500000 1965.000000 0.000000 0.000000 0.000000 … 320.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 4.000000 2007.000000
50% 50.000000 68.000000 9453.000000 6.000000 5.000000 1973.000000 1993.000000 0.000000 368.500000 0.000000 … 480.000000 0.000000 26.000000 0.000000 0.000000 0.000000 0.000000 0.000000 6.000000 2008.000000
75% 70.000000 80.000000 11570.000000 7.000000 6.000000 2001.000000 2004.000000 164.000000 733.000000 0.000000 … 576.000000 168.000000 70.000000 0.000000 0.000000 0.000000 0.000000 0.000000 8.000000 2009.000000
max 190.000000 313.000000 215245.000000 10.000000 9.000000 2010.000000 2010.000000 1600.000000 5644.000000 1526.000000 … 1488.000000 1424.000000 742.000000 1012.000000 508.000000 576.000000 800.000000 17000.000000 12.000000 2010.000000
8 rows × 36 columns
In [15]:
# Drop missing values
def get_cols_with_no_nans(df,col_type):
”’
Arguments :
df : The dataframe to process
col_type :
num : to only get numerical columns with no nans
no_num : to only get nun-numerical columns with no nans
all : to get any columns with no nans
”’
if (col_type == ‘num’):
predictors = df.select_dtypes(exclude=[‘object’])
elif (col_type == ‘no_num’):
predictors = df.select_dtypes(include=[‘object’])
elif (col_type == ‘all’):
predictors = df
else :
print(‘Error : choose a type (num, no_num, all)’)
return 0
cols_with_no_nans = []
for col in predictors.columns:
if not df[col].isnull().any():
cols_with_no_nans.append(col)
return cols_with_no_nans
# Call the function
num_cols = get_cols_with_no_nans(combined , ‘num’)
cat_cols = get_cols_with_no_nans(combined , ‘no_num’)
In [16]:
# How many columns we got
print (‘Number of numerical columns with no nan values :’,len(num_cols))
print (‘Number of nun-numerical columns with no nan values :’,len(cat_cols))
Number of numerical columns with no nan values : 25
Number of nun-numerical columns with no nan values : 20
In [17]:
import matplotlib.pyplot as plt
# Plot the variables
combined = combined[num_cols + cat_cols]
combined.hist(figsize = (12,10))
plt.show()
In [19]:
import seaborn as sb
# How many of features are correlated
train_data = train_data[num_cols + cat_cols]
train_data[‘Target’] = target
C_mat = train_data.corr()
fig = plt.figure(figsize = (15,15))
sb.heatmap(C_mat, vmax = .8, square = True)
plt.show()
# Looks like 15 correlated features
In [20]:
import numpy as np
# We will encode the categorical features using one hot encoding.
def oneHotEncode(df,colNames):
for col in colNames:
if( df[col].dtype == np.dtype(‘object’)):
dummies = pd.get_dummies(df[col],prefix=col)
df = pd.concat([df,dummies],axis=1)
#drop the encoded column
df.drop([col],axis = 1 , inplace=True)
return df
print(‘There were {} columns before encoding categorical features’.format(combined.shape[1]))
combined = oneHotEncode(combined, cat_cols)
print(‘There are {} columns after encoding categorical features’.format(combined.shape[1]))
There were 45 columns before encoding categorical features
There are 149 columns after encoding categorical features
In [21]:
# Split data to train and test
def split_combined():
global combined
train = combined[:1460]
test = combined[1460:]
return train , test
train, test = split_combined()
In [22]:
from tensorflow.keras.layers import Input, Dense
from tensorflow.keras import Sequential
# Use ‘relu’ as the activation function for the hidden layers
# Use a ‘normal’ initializer as the kernal_intializer
#Define the output layer with only one node
#Use ‘linear ’as the activation function for the output layer
NN_model = Sequential()
# The Input Layer :
NN_model.add(Dense(128, kernel_initializer=’normal’,input_dim = train.shape[1], activation=’relu’))
# The Hidden Layers :
NN_model.add(Dense(256, kernel_initializer=’normal’,activation=’relu’))
NN_model.add(Dense(256, kernel_initializer=’normal’,activation=’relu’))
NN_model.add(Dense(256, kernel_initializer=’normal’,activation=’relu’))
# The Output Layer :
NN_model.add(Dense(1, kernel_initializer=’normal’,activation=’linear’))
# Compile the network :
NN_model.compile(loss=’mean_absolute_error’, optimizer=’adam’, metrics=[‘mean_absolute_error’])
NN_model.summary()
Model: “sequential”
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_8 (Dense) (None, 128) 19200
_________________________________________________________________
dense_9 (Dense) (None, 256) 33024
_________________________________________________________________
dense_10 (Dense) (None, 256) 65792
_________________________________________________________________
dense_11 (Dense) (None, 256) 65792
_________________________________________________________________
dense_12 (Dense) (None, 1) 257
=================================================================
Total params: 184,065
Trainable params: 184,065
Non-trainable params: 0
_________________________________________________________________
In [25]:
from tensorflow.keras.callbacks import ModelCheckpoint
# Define a checkpoint to save the data
checkpoint_name = ‘Models/Weights-{epoch:03d}–{val_loss:.5f}.hdf5′
checkpoint = ModelCheckpoint(checkpoint_name, monitor=’val_loss’, verbose = 1, save_best_only = True, mode =’auto’)
callbacks_list = [checkpoint]
In [26]:
# Train the model
hist = NN_model.fit(train, target, epochs=500, batch_size=32, validation_split = 0.2, callbacks=callbacks_list)
WARNING:tensorflow:Falling back from v2 loop because of error: Failed to find data adapter that can handle input:
WARNING:tensorflow:Falling back from v2 loop because of error: Failed to find data adapter that can handle input:
Train on 1168 samples, validate on 292 samples
Epoch 1/500
928/1168 [======================>…….] – ETA: 0s – loss: 146051.1598 – mean_absolute_error: 146051.1562
Epoch 00001: val_loss improved from inf to 54584.22432, saving model to Models/Weights-001–54584.22432.hdf5
1168/1168 [==============================] – 0s 344us/sample – loss: 129288.7532 – mean_absolute_error: 129288.7422 – val_loss: 54584.2243 – val_mean_absolute_error: 54584.2188
Epoch 2/500
960/1168 [=======================>……] – ETA: 0s – loss: 54859.5047 – mean_absolute_error: 54859.5039
Epoch 00002: val_loss improved from 54584.22432 to 51313.87826, saving model to Models/Weights-002–51313.87826.hdf5
1168/1168 [==============================] – 0s 147us/sample – loss: 54104.6077 – mean_absolute_error: 54104.6055 – val_loss: 51313.8783 – val_mean_absolute_error: 51313.8750
Epoch 3/500
960/1168 [=======================>……] – ETA: 0s – loss: 49570.9777 – mean_absolute_error: 49570.9727
Epoch 00003: val_loss improved from 51313.87826 to 44461.62751, saving model to Models/Weights-003–44461.62751.hdf5
1168/1168 [==============================] – 0s 161us/sample – loss: 47747.1279 – mean_absolute_error: 47747.1250 – val_loss: 44461.6275 – val_mean_absolute_error: 44461.6289
Epoch 4/500
864/1168 [=====================>……..] – ETA: 0s – loss: 42580.9243 – mean_absolute_error: 42580.9297
Epoch 00004: val_loss improved from 44461.62751 to 44229.79570, saving model to Models/Weights-004–44229.79570.hdf5
1168/1168 [==============================] – 0s 167us/sample – loss: 42607.3988 – mean_absolute_error: 42607.4023 – val_loss: 44229.7957 – val_mean_absolute_error: 44229.7969
Epoch 5/500
864/1168 [=====================>……..] – ETA: 0s – loss: 41783.7640 – mean_absolute_error: 41783.7578
Epoch 00005: val_loss improved from 44229.79570 to 40252.58353, saving model to Models/Weights-005–40252.58353.hdf5
1168/1168 [==============================] – 0s 162us/sample – loss: 40177.6901 – mean_absolute_error: 40177.6875 – val_loss: 40252.5835 – val_mean_absolute_error: 40252.5898
Epoch 6/500
832/1168 [====================>………] – ETA: 0s – loss: 36664.8377 – mean_absolute_error: 36664.8398
Epoch 00006: val_loss improved from 40252.58353 to 38839.46896, saving model to Models/Weights-006–38839.46896.hdf5
1168/1168 [==============================] – 0s 155us/sample – loss: 37185.8862 – mean_absolute_error: 37185.8828 – val_loss: 38839.4690 – val_mean_absolute_error: 38839.4688
Epoch 7/500
1088/1168 [==========================>…] – ETA: 0s – loss: 35671.6534 – mean_absolute_error: 35671.6562
Epoch 00007: val_loss did not improve from 38839.46896
1168/1168 [==============================] – 0s 174us/sample – loss: 35468.6949 – mean_absolute_error: 35468.6953 – val_loss: 41244.3096 – val_mean_absolute_error: 41244.3086
Epoch 8/500
960/1168 [=======================>……] – ETA: 0s – loss: 36223.2536 – mean_absolute_error: 36223.2539
Epoch 00008: val_loss did not improve from 38839.46896
1168/1168 [==============================] – 0s 128us/sample – loss: 35467.3525 – mean_absolute_error: 35467.3516 – val_loss: 39716.6052 – val_mean_absolute_error: 39716.6016
Epoch 9/500
864/1168 [=====================>……..] – ETA: 0s – loss: 33103.5897 – mean_absolute_error: 33103.5938
Epoch 00009: val_loss improved from 38839.46896 to 36230.32885, saving model to Models/Weights-009–36230.32885.hdf5
1168/1168 [==============================] – 0s 155us/sample – loss: 33412.8778 – mean_absolute_error: 33412.8750 – val_loss: 36230.3288 – val_mean_absolute_error: 36230.3281
Epoch 10/500
800/1168 [===================>……….] – ETA: 0s – loss: 33714.7844 – mean_absolute_error: 33714.7891
Epoch 00010: val_loss did not improve from 36230.32885
1168/1168 [==============================] – 0s 154us/sample – loss: 33791.7134 – mean_absolute_error: 33791.7227 – val_loss: 38403.6402 – val_mean_absolute_error: 38403.6406
Epoch 11/500
864/1168 [=====================>……..] – ETA: 0s – loss: 33022.4662 – mean_absolute_error: 33022.4688
Epoch 00011: val_loss improved from 36230.32885 to 35154.81170, saving model to Models/Weights-011–35154.81170.hdf5
1168/1168 [==============================] – 0s 162us/sample – loss: 32276.3838 – mean_absolute_error: 32276.3828 – val_loss: 35154.8117 – val_mean_absolute_error: 35154.8125
Epoch 12/500
832/1168 [====================>………] – ETA: 0s – loss: 32966.8400 – mean_absolute_error: 32966.8398
Epoch 00012: val_loss improved from 35154.81170 to 34969.50337, saving model to Models/Weights-012–34969.50337.hdf5
1168/1168 [==============================] – 0s 158us/sample – loss: 32284.1195 – mean_absolute_error: 32284.1191 – val_loss: 34969.5034 – val_mean_absolute_error: 34969.5078
Epoch 13/500
800/1168 [===================>……….] – ETA: 0s – loss: 32445.4270 – mean_absolute_error: 32445.4277
Epoch 00013: val_loss did not improve from 34969.50337
1168/1168 [==============================] – 0s 150us/sample – loss: 32502.4418 – mean_absolute_error: 32502.4414 – val_loss: 35306.6573 – val_mean_absolute_error: 35306.6562
Epoch 14/500
992/1168 [========================>…..] – ETA: 0s – loss: 32602.0493 – mean_absolute_error: 32602.0469
Epoch 00014: val_loss did not improve from 34969.50337
1168/1168 [==============================] – 0s 126us/sample – loss: 32089.2049 – mean_absolute_error: 32089.2051 – val_loss: 35491.9555 – val_mean_absolute_error: 35491.9570
Epoch 15/500
992/1168 [========================>…..] – ETA: 0s – loss: 33364.7096 – mean_absolute_error: 33364.7070
Epoch 00015: val_loss improved from 34969.50337 to 34942.64686, saving model to Models/Weights-015–34942.64686.hdf5
1168/1168 [==============================] – 0s 146us/sample – loss: 33336.3233 – mean_absolute_error: 33336.3203 – val_loss: 34942.6469 – val_mean_absolute_error: 34942.6484
Epoch 16/500
896/1168 [======================>…….] – ETA: 0s – loss: 32827.0546 – mean_absolute_error: 32827.0586
Epoch 00016: val_loss did not improve from 34942.64686
1168/1168 [==============================] – 0s 141us/sample – loss: 32958.2473 – mean_absolute_error: 32958.2539 – val_loss: 35692.7600 – val_mean_absolute_error: 35692.7578
Epoch 17/500
960/1168 [=======================>……] – ETA: 0s – loss: 32168.2706 – mean_absolute_error: 32168.2734
Epoch 00017: val_loss improved from 34942.64686 to 34637.96613, saving model to Models/Weights-017–34637.96613.hdf5
1168/1168 [==============================] – 0s 151us/sample – loss: 31739.2685 – mean_absolute_error: 31739.2715 – val_loss: 34637.9661 – val_mean_absolute_error: 34637.9648
Epoch 18/500
928/1168 [======================>…….] – ETA: 0s – loss: 31629.4223 – mean_absolute_error: 31629.4219
Epoch 00018: val_loss improved from 34637.96613 to 34434.58695, saving model to Models/Weights-018–34434.58695.hdf5
1168/1168 [==============================] – 0s 148us/sample – loss: 31285.9358 – mean_absolute_error: 31285.9316 – val_loss: 34434.5870 – val_mean_absolute_error: 34434.5859
Epoch 19/500
1152/1168 [============================>.] – ETA: 0s – loss: 31619.4567 – mean_absolute_error: 31619.4512
Epoch 00019: val_loss did not improve from 34434.58695
1168/1168 [==============================] – 0s 171us/sample – loss: 31558.2611 – mean_absolute_error: 31558.2578 – val_loss: 34514.1792 – val_mean_absolute_error: 34514.1797
Epoch 20/500
992/1168 [========================>…..] – ETA: 0s – loss: 31615.1013 – mean_absolute_error: 31615.0996
Epoch 00020: val_loss did not improve from 34434.58695
1168/1168 [==============================] – 0s 194us/sample – loss: 31766.8224 – mean_absolute_error: 31766.8223 – val_loss: 35612.7412 – val_mean_absolute_error: 35612.7383
Epoch 21/500
800/1168 [===================>……….] – ETA: 0s – loss: 31296.6576 – mean_absolute_error: 31296.6543
Epoch 00021: val_loss did not improve from 34434.58695
1168/1168 [==============================] – 0s 156us/sample – loss: 31541.9622 – mean_absolute_error: 31541.9590 – val_loss: 34714.3458 – val_mean_absolute_error: 34714.3477
Epoch 22/500
960/1168 [=======================>……] – ETA: 0s – loss: 31077.8378 – mean_absolute_error: 31077.8359
Epoch 00022: val_loss did not improve from 34434.58695
1168/1168 [==============================] – 0s 211us/sample – loss: 31760.4514 – mean_absolute_error: 31760.4492 – val_loss: 34455.9745 – val_mean_absolute_error: 34455.9766
Epoch 23/500
800/1168 [===================>……….] – ETA: 0s – loss: 31816.3003 – mean_absolute_error: 31816.3008
Epoch 00023: val_loss did not improve from 34434.58695
1168/1168 [==============================] – 0s 144us/sample – loss: 32397.4864 – mean_absolute_error: 32397.4863 – val_loss: 35844.8263 – val_mean_absolute_error: 35844.8281
Epoch 24/500
1120/1168 [===========================>..] – ETA: 0s – loss: 34374.5712 – mean_absolute_error: 34374.5703
Epoch 00024: val_loss did not improve from 34434.58695
1168/1168 [==============================] – 0s 172us/sample – loss: 34152.1647 – mean_absolute_error: 34152.1680 – val_loss: 35064.5151 – val_mean_absolute_error: 35064.5156
Epoch 25/500
992/1168 [========================>…..] – ETA: 0s – loss: 31256.5544 – mean_absolute_error: 31256.5547
Epoch 00025: val_loss did not improve from 34434.58695
1168/1168 [==============================] – 0s 198us/sample – loss: 31294.7601 – mean_absolute_error: 31294.7578 – val_loss: 35378.4715 – val_mean_absolute_error: 35378.4727
Epoch 26/500
1120/1168 [===========================>..] – ETA: 0s – loss: 32053.9454 – mean_absolute_error: 32053.9434
Epoch 00026: val_loss did not improve from 34434.58695
1168/1168 [==============================] – 0s 170us/sample – loss: 31937.4984 – mean_absolute_error: 31937.4961 – val_loss: 35311.1510 – val_mean_absolute_error: 35311.1523
Epoch 27/500
1152/1168 [============================>.] – ETA: 0s – loss: 30768.8805 – mean_absolute_error: 30768.8789
Epoch 00027: val_loss improved from 34434.58695 to 34221.07906, saving model to Models/Weights-027–34221.07906.hdf5
1168/1168 [==============================] – 0s 193us/sample – loss: 30749.9133 – mean_absolute_error: 30749.9102 – val_loss: 34221.0791 – val_mean_absolute_error: 34221.0781
Epoch 28/500
928/1168 [======================>…….] – ETA: 0s – loss: 31505.5341 – mean_absolute_error: 31505.5332
Epoch 00028: val_loss did not improve from 34221.07906
1168/1168 [==============================] – 0s 137us/sample – loss: 31176.6792 – mean_absolute_error: 31176.6816 – val_loss: 35542.4461 – val_mean_absolute_error: 35542.4453
Epoch 29/500
960/1168 [=======================>……] – ETA: 0s – loss: 30760.9826 – mean_absolute_error: 30760.9863
Epoch 00029: val_loss did not improve from 34221.07906
1168/1168 [==============================] – 0s 131us/sample – loss: 32357.7644 – mean_absolute_error: 32357.7676 – val_loss: 37289.6147 – val_mean_absolute_error: 37289.6172
Epoch 30/500
960/1168 [=======================>……] – ETA: 0s – loss: 30659.6701 – mean_absolute_error: 30659.6738
Epoch 00030: val_loss improved from 34221.07906 to 34174.85713, saving model to Models/Weights-030–34174.85713.hdf5
1168/1168 [==============================] – 0s 165us/sample – loss: 31043.7623 – mean_absolute_error: 31043.7637 – val_loss: 34174.8571 – val_mean_absolute_error: 34174.8594
Epoch 31/500
928/1168 [======================>…….] – ETA: 0s – loss: 31993.4425 – mean_absolute_error: 31993.4355
Epoch 00031: val_loss did not improve from 34174.85713
1168/1168 [==============================] – 0s 135us/sample – loss: 32132.8707 – mean_absolute_error: 32132.8672 – val_loss: 38210.2826 – val_mean_absolute_error: 38210.2852
Epoch 32/500
928/1168 [======================>…….] – ETA: 0s – loss: 31176.0750 – mean_absolute_error: 31176.0742
Epoch 00032: val_loss improved from 34174.85713 to 34168.87623, saving model to Models/Weights-032–34168.87623.hdf5
1168/1168 [==============================] – 0s 153us/sample – loss: 31160.0680 – mean_absolute_error: 31160.0645 – val_loss: 34168.8762 – val_mean_absolute_error: 34168.8750
Epoch 33/500
928/1168 [======================>…….] – ETA: 0s – loss: 29967.7163 – mean_absolute_error: 29967.7207
Epoch 00033: val_loss did not improve from 34168.87623
1168/1168 [==============================] – 0s 144us/sample – loss: 30303.1479 – mean_absolute_error: 30303.1465 – val_loss: 34185.2633 – val_mean_absolute_error: 34185.2656
Epoch 34/500
928/1168 [======================>…….] – ETA: 0s – loss: 31211.2850 – mean_absolute_error: 31211.2832
Epoch 00034: val_loss did not improve from 34168.87623
1168/1168 [==============================] – 0s 190us/sample – loss: 30564.5954 – mean_absolute_error: 30564.5918 – val_loss: 35941.2848 – val_mean_absolute_error: 35941.2852
Epoch 35/500
896/1168 [======================>…….] – ETA: 0s – loss: 31326.6518 – mean_absolute_error: 31326.6465
Epoch 00035: val_loss improved from 34168.87623 to 33926.79778, saving model to Models/Weights-035–33926.79778.hdf5
1168/1168 [==============================] – 0s 187us/sample – loss: 31664.9680 – mean_absolute_error: 31664.9648 – val_loss: 33926.7978 – val_mean_absolute_error: 33926.7969
Epoch 36/500
832/1168 [====================>………] – ETA: 0s – loss: 30686.1593 – mean_absolute_error: 30686.1641
Epoch 00036: val_loss did not improve from 33926.79778
1168/1168 [==============================] – 0s 137us/sample – loss: 30427.0612 – mean_absolute_error: 30427.0684 – val_loss: 37164.5517 – val_mean_absolute_error: 37164.5508
Epoch 37/500
992/1168 [========================>…..] – ETA: 0s – loss: 30949.3627 – mean_absolute_error: 30949.3652
Epoch 00037: val_loss did not improve from 33926.79778
1168/1168 [==============================] – 0s 141us/sample – loss: 31191.0083 – mean_absolute_error: 31191.0098 – val_loss: 34985.1485 – val_mean_absolute_error: 34985.1484
Epoch 38/500
1120/1168 [===========================>..] – ETA: 0s – loss: 31733.9672 – mean_absolute_error: 31733.9648
Epoch 00038: val_loss did not improve from 33926.79778
1168/1168 [==============================] – 0s 173us/sample – loss: 31871.6424 – mean_absolute_error: 31871.6406 – val_loss: 36924.5239 – val_mean_absolute_error: 36924.5234
Epoch 39/500
704/1168 [=================>…………] – ETA: 0s – loss: 31212.5858 – mean_absolute_error: 31212.5879
Epoch 00039: val_loss did not improve from 33926.79778
1168/1168 [==============================] – 0s 152us/sample – loss: 31855.3273 – mean_absolute_error: 31855.3281 – val_loss: 37740.1763 – val_mean_absolute_error: 37740.1797
Epoch 40/500
928/1168 [======================>…….] – ETA: 0s – loss: 30945.6675 – mean_absolute_error: 30945.6660
Epoch 00040: val_loss did not improve from 33926.79778
1168/1168 [==============================] – 0s 134us/sample – loss: 30705.1351 – mean_absolute_error: 30705.1328 – val_loss: 34466.0439 – val_mean_absolute_error: 34466.0430
Epoch 41/500
1088/1168 [==========================>…] – ETA: 0s – loss: 29950.4824 – mean_absolute_error: 29950.4785
Epoch 00041: val_loss did not improve from 33926.79778
1168/1168 [==============================] – 0s 118us/sample – loss: 30177.2076 – mean_absolute_error: 30177.2012 – val_loss: 34769.0068 – val_mean_absolute_error: 34769.0117
Epoch 42/500
960/1168 [=======================>……] – ETA: 0s – loss: 30699.6488 – mean_absolute_error: 30699.6504
Epoch 00042: val_loss did not improve from 33926.79778
1168/1168 [==============================] – 0s 127us/sample – loss: 30846.3531 – mean_absolute_error: 30846.3535 – val_loss: 34094.0373 – val_mean_absolute_error: 34094.0391
Epoch 43/500
800/1168 [===================>……….] – ETA: 0s – loss: 30833.1138 – mean_absolute_error: 30833.1133
Epoch 00043: val_loss improved from 33926.79778 to 33620.62051, saving model to Models/Weights-043–33620.62051.hdf5
1168/1168 [==============================] – 0s 186us/sample – loss: 30160.7491 – mean_absolute_error: 30160.7461 – val_loss: 33620.6205 – val_mean_absolute_error: 33620.6172
Epoch 44/500
928/1168 [======================>…….] – ETA: 0s – loss: 30423.2086 – mean_absolute_error: 30423.2109
Epoch 00044: val_loss did not improve from 33620.62051
1168/1168 [==============================] – 0s 137us/sample – loss: 30346.2566 – mean_absolute_error: 30346.2598 – val_loss: 33712.9975 – val_mean_absolute_error: 33713.0000
Epoch 45/500
928/1168 [======================>…….] – ETA: 0s – loss: 30120.8768 – mean_absolute_error: 30120.8750
Epoch 00045: val_loss did not improve from 33620.62051
1168/1168 [==============================] – 0s 137us/sample – loss: 30052.9869 – mean_absolute_error: 30052.9863 – val_loss: 34923.5379 – val_mean_absolute_error: 34923.5430
Epoch 46/500
896/1168 [======================>…….] – ETA: 0s – loss: 30715.1831 – mean_absolute_error: 30715.1836
Epoch 00046: val_loss did not improve from 33620.62051
1168/1168 [==============================] – 0s 136us/sample – loss: 30152.1448 – mean_absolute_error: 30152.1465 – val_loss: 33687.2453 – val_mean_absolute_error: 33687.2461
Epoch 47/500
960/1168 [=======================>……] – ETA: 0s – loss: 28726.6753 – mean_absolute_error: 28726.6758
Epoch 00047: val_loss improved from 33620.62051 to 33556.97164, saving model to Models/Weights-047–33556.97164.hdf5
1168/1168 [==============================] – 0s 154us/sample – loss: 29347.2913 – mean_absolute_error: 29347.2910 – val_loss: 33556.9716 – val_mean_absolute_error: 33556.9727
Epoch 48/500
992/1168 [========================>…..] – ETA: 0s – loss: 29437.4860 – mean_absolute_error: 29437.4844
Epoch 00048: val_loss improved from 33556.97164 to 33212.72999, saving model to Models/Weights-048–33212.72999.hdf5
1168/1168 [==============================] – 0s 167us/sample – loss: 29653.4206 – mean_absolute_error: 29653.4180 – val_loss: 33212.7300 – val_mean_absolute_error: 33212.7344
Epoch 49/500
1056/1168 [==========================>…] – ETA: 0s – loss: 29558.0479 – mean_absolute_error: 29558.0508
Epoch 00049: val_loss improved from 33212.72999 to 32958.47983, saving model to Models/Weights-049–32958.47983.hdf5
1168/1168 [==============================] – 0s 202us/sample – loss: 29271.2239 – mean_absolute_error: 29271.2266 – val_loss: 32958.4798 – val_mean_absolute_error: 32958.4844
Epoch 50/500
928/1168 [======================>…….] – ETA: 0s – loss: 29020.3718 – mean_absolute_error: 29020.3730
Epoch 00050: val_loss did not improve from 32958.47983
1168/1168 [==============================] – 0s 131us/sample – loss: 29566.1944 – mean_absolute_error: 29566.1914 – val_loss: 33301.5072 – val_mean_absolute_error: 33301.5078
Epoch 51/500
960/1168 [=======================>……] – ETA: 0s – loss: 29935.9611 – mean_absolute_error: 29935.9629
Epoch 00051: val_loss did not improve from 32958.47983
1168/1168 [==============================] – 0s 135us/sample – loss: 29567.6416 – mean_absolute_error: 29567.6445 – val_loss: 33715.2049 – val_mean_absolute_error: 33715.2070
Epoch 52/500
992/1168 [========================>…..] – ETA: 0s – loss: 29351.2060 – mean_absolute_error: 29351.2051
Epoch 00052: val_loss did not improve from 32958.47983
1168/1168 [==============================] – 0s 142us/sample – loss: 29411.7621 – mean_absolute_error: 29411.7598 – val_loss: 32984.5581 – val_mean_absolute_error: 32984.5586
Epoch 53/500
864/1168 [=====================>……..] – ETA: 0s – loss: 28875.3048 – mean_absolute_error: 28875.3047
Epoch 00053: val_loss did not improve from 32958.47983
1168/1168 [==============================] – 0s 137us/sample – loss: 29359.7013 – mean_absolute_error: 29359.7012 – val_loss: 33345.1983 – val_mean_absolute_error: 33345.1992
Epoch 54/500
960/1168 [=======================>……] – ETA: 0s – loss: 29351.3622 – mean_absolute_error: 29351.3652
Epoch 00054: val_loss did not improve from 32958.47983
1168/1168 [==============================] – 0s 131us/sample – loss: 29708.5048 – mean_absolute_error: 29708.5098 – val_loss: 33342.4736 – val_mean_absolute_error: 33342.4727
Epoch 55/500
864/1168 [=====================>……..] – ETA: 0s – loss: 30128.0343 – mean_absolute_error: 30128.0293
Epoch 00055: val_loss improved from 32958.47983 to 32506.29752, saving model to Models/Weights-055–32506.29752.hdf5
1168/1168 [==============================] – 0s 164us/sample – loss: 30376.8040 – mean_absolute_error: 30376.8047 – val_loss: 32506.2975 – val_mean_absolute_error: 32506.2988
Epoch 56/500
864/1168 [=====================>……..] – ETA: 0s – loss: 30683.7264 – mean_absolute_error: 30683.7285
Epoch 00056: val_loss did not improve from 32506.29752
1168/1168 [==============================] – 0s 137us/sample – loss: 29405.5298 – mean_absolute_error: 29405.5312 – val_loss: 32537.0661 – val_mean_absolute_error: 32537.0645
Epoch 57/500
1024/1168 [=========================>….] – ETA: 0s – loss: 29667.8900 – mean_absolute_error: 29667.8887
Epoch 00057: val_loss did not improve from 32506.29752
1168/1168 [==============================] – 0s 123us/sample – loss: 29306.6489 – mean_absolute_error: 29306.6504 – val_loss: 33773.6228 – val_mean_absolute_error: 33773.6250
Epoch 58/500
992/1168 [========================>…..] – ETA: 0s – loss: 30040.3122 – mean_absolute_error: 30040.3086
Epoch 00058: val_loss did not improve from 32506.29752
1168/1168 [==============================] – 0s 129us/sample – loss: 29446.4411 – mean_absolute_error: 29446.4375 – val_loss: 34379.4880 – val_mean_absolute_error: 34379.4883
Epoch 59/500
1024/1168 [=========================>….] – ETA: 0s – loss: 30676.5307 – mean_absolute_error: 30676.5312
Epoch 00059: val_loss improved from 32506.29752 to 32277.23767, saving model to Models/Weights-059–32277.23767.hdf5
1168/1168 [==============================] – 0s 153us/sample – loss: 30515.0570 – mean_absolute_error: 30515.0586 – val_loss: 32277.2377 – val_mean_absolute_error: 32277.2363
Epoch 60/500
896/1168 [======================>…….] – ETA: 0s – loss: 29084.8131 – mean_absolute_error: 29084.8125
Epoch 00060: val_loss did not improve from 32277.23767
1168/1168 [==============================] – 0s 137us/sample – loss: 28844.5758 – mean_absolute_error: 28844.5781 – val_loss: 32773.5539 – val_mean_absolute_error: 32773.5547
Epoch 61/500
992/1168 [========================>…..] – ETA: 0s – loss: 29951.5143 – mean_absolute_error: 29951.5156
Epoch 00061: val_loss did not improve from 32277.23767
1168/1168 [==============================] – 0s 126us/sample – loss: 30196.1671 – mean_absolute_error: 30196.1719 – val_loss: 40293.1779 – val_mean_absolute_error: 40293.1797
Epoch 62/500
736/1168 [=================>…………] – ETA: 0s – loss: 30562.7425 – mean_absolute_error: 30562.7383
Epoch 00062: val_loss did not improve from 32277.23767
1168/1168 [==============================] – 0s 146us/sample – loss: 31323.2977 – mean_absolute_error: 31323.2949 – val_loss: 41055.6726 – val_mean_absolute_error: 41055.6719
Epoch 63/500
1024/1168 [=========================>….] – ETA: 0s – loss: 29702.0454 – mean_absolute_error: 29702.0449
Epoch 00063: val_loss improved from 32277.23767 to 31938.56095, saving model to Models/Weights-063–31938.56095.hdf5
1168/1168 [==============================] – 0s 145us/sample – loss: 29274.0273 – mean_absolute_error: 29274.0273 – val_loss: 31938.5609 – val_mean_absolute_error: 31938.5625
Epoch 64/500
1024/1168 [=========================>….] – ETA: 0s – loss: 28671.3094 – mean_absolute_error: 28671.3066
Epoch 00064: val_loss did not improve from 31938.56095
1168/1168 [==============================] – 0s 122us/sample – loss: 29012.6054 – mean_absolute_error: 29012.6035 – val_loss: 33052.3953 – val_mean_absolute_error: 33052.3984
Epoch 65/500
896/1168 [======================>…….] – ETA: 0s – loss: 28423.9443 – mean_absolute_error: 28423.9434
Epoch 00065: val_loss did not improve from 31938.56095
1168/1168 [==============================] – 0s 133us/sample – loss: 28776.9676 – mean_absolute_error: 28776.9648 – val_loss: 32233.9651 – val_mean_absolute_error: 32233.9648
Epoch 66/500
1024/1168 [=========================>….] – ETA: 0s – loss: 28338.2582 – mean_absolute_error: 28338.2617
Epoch 00066: val_loss did not improve from 31938.56095
1168/1168 [==============================] – 0s 123us/sample – loss: 28252.7958 – mean_absolute_error: 28252.7988 – val_loss: 32187.5245 – val_mean_absolute_error: 32187.5234
Epoch 67/500
896/1168 [======================>…….] – ETA: 0s – loss: 29512.4423 – mean_absolute_error: 29512.4414
Epoch 00067: val_loss improved from 31938.56095 to 31095.47790, saving model to Models/Weights-067–31095.47790.hdf5
1168/1168 [==============================] – 0s 156us/sample – loss: 29248.3397 – mean_absolute_error: 29248.3359 – val_loss: 31095.4779 – val_mean_absolute_error: 31095.4766
Epoch 68/500
928/1168 [======================>…….] – ETA: 0s – loss: 28059.1724 – mean_absolute_error: 28059.1719
Epoch 00068: val_loss did not improve from 31095.47790
1168/1168 [==============================] – 0s 145us/sample – loss: 27922.4711 – mean_absolute_error: 27922.4707 – val_loss: 31146.5256 – val_mean_absolute_error: 31146.5234
Epoch 69/500
1024/1168 [=========================>….] – ETA: 0s – loss: 27349.8297 – mean_absolute_error: 27349.8242
Epoch 00069: val_loss did not improve from 31095.47790
1168/1168 [==============================] – 0s 123us/sample – loss: 27752.3083 – mean_absolute_error: 27752.3027 – val_loss: 31126.7590 – val_mean_absolute_error: 31126.7578
Epoch 70/500
1024/1168 [=========================>….] – ETA: 0s – loss: 28541.9838 – mean_absolute_error: 28541.9844
Epoch 00070: val_loss did not improve from 31095.47790
1168/1168 [==============================] – 0s 120us/sample – loss: 28090.4146 – mean_absolute_error: 28090.4141 – val_loss: 31210.1716 – val_mean_absolute_error: 31210.1719
Epoch 71/500
1056/1168 [==========================>…] – ETA: 0s – loss: 28032.1433 – mean_absolute_error: 28032.1445
Epoch 00071: val_loss improved from 31095.47790 to 30943.30383, saving model to Models/Weights-071–30943.30383.hdf5
1168/1168 [==============================] – 0s 149us/sample – loss: 28028.6576 – mean_absolute_error: 28028.6602 – val_loss: 30943.3038 – val_mean_absolute_error: 30943.3008
Epoch 72/500
1056/1168 [==========================>…] – ETA: 0s – loss: 28022.7090 – mean_absolute_error: 28022.7070
Epoch 00072: val_loss did not improve from 30943.30383
1168/1168 [==============================] – 0s 124us/sample – loss: 27566.3818 – mean_absolute_error: 27566.3789 – val_loss: 31974.1039 – val_mean_absolute_error: 31974.1035
Epoch 73/500
1056/1168 [==========================>…] – ETA: 0s – loss: 28030.0553 – mean_absolute_error: 28030.0547
Epoch 00073: val_loss did not improve from 30943.30383
1168/1168 [==============================] – 0s 121us/sample – loss: 27858.1019 – mean_absolute_error: 27858.1035 – val_loss: 31921.6572 – val_mean_absolute_error: 31921.6543
Epoch 74/500
1056/1168 [==========================>…] – ETA: 0s – loss: 28027.9385 – mean_absolute_error: 28027.9395
Epoch 00074: val_loss did not improve from 30943.30383
1168/1168 [==============================] – 0s 122us/sample – loss: 27495.7247 – mean_absolute_error: 27495.7266 – val_loss: 31130.3252 – val_mean_absolute_error: 31130.3223
Epoch 75/500
896/1168 [======================>…….] – ETA: 0s – loss: 27746.9514 – mean_absolute_error: 27746.9551
Epoch 00075: val_loss did not improve from 30943.30383
1168/1168 [==============================] – 0s 129us/sample – loss: 27333.0311 – mean_absolute_error: 27333.0352 – val_loss: 32923.8868 – val_mean_absolute_error: 32923.8867
Epoch 76/500
1056/1168 [==========================>…] – ETA: 0s – loss: 26513.2771 – mean_absolute_error: 26513.2812
Epoch 00076: val_loss improved from 30943.30383 to 30483.19004, saving model to Models/Weights-076–30483.19004.hdf5
1168/1168 [==============================] – 0s 143us/sample – loss: 26839.5371 – mean_absolute_error: 26839.5410 – val_loss: 30483.1900 – val_mean_absolute_error: 30483.1875
Epoch 77/500
1024/1168 [=========================>….] – ETA: 0s – loss: 28108.3084 – mean_absolute_error: 28108.3105
Epoch 00077: val_loss did not improve from 30483.19004
1168/1168 [==============================] – 0s 121us/sample – loss: 27438.8765 – mean_absolute_error: 27438.8770 – val_loss: 32190.4570 – val_mean_absolute_error: 32190.4590
Epoch 78/500
992/1168 [========================>…..] – ETA: 0s – loss: 26856.4870 – mean_absolute_error: 26856.4883
Epoch 00078: val_loss did not improve from 30483.19004
1168/1168 [==============================] – 0s 127us/sample – loss: 26370.8781 – mean_absolute_error: 26370.8789 – val_loss: 30578.2842 – val_mean_absolute_error: 30578.2852
Epoch 79/500
992/1168 [========================>…..] – ETA: 0s – loss: 28218.4577 – mean_absolute_error: 28218.4551
Epoch 00079: val_loss did not improve from 30483.19004
1168/1168 [==============================] – 0s 124us/sample – loss: 28050.7267 – mean_absolute_error: 28050.7227 – val_loss: 31334.0960 – val_mean_absolute_error: 31334.0957
Epoch 80/500
992/1168 [========================>…..] – ETA: 0s – loss: 26415.0270 – mean_absolute_error: 26415.0273
Epoch 00080: val_loss did not improve from 30483.19004
1168/1168 [==============================] – 0s 128us/sample – loss: 25987.8397 – mean_absolute_error: 25987.8398 – val_loss: 35052.5748 – val_mean_absolute_error: 35052.5742
Epoch 81/500
960/1168 [=======================>……] – ETA: 0s – loss: 27135.9463 – mean_absolute_error: 27135.9492
Epoch 00081: val_loss improved from 30483.19004 to 29034.66757, saving model to Models/Weights-081–29034.66757.hdf5
1168/1168 [==============================] – 0s 151us/sample – loss: 26280.2503 – mean_absolute_error: 26280.2520 – val_loss: 29034.6676 – val_mean_absolute_error: 29034.6719
Epoch 82/500
896/1168 [======================>…….] – ETA: 0s – loss: 26352.3481 – mean_absolute_error: 26352.3457
Epoch 00082: val_loss improved from 29034.66757 to 28778.58505, saving model to Models/Weights-082–28778.58505.hdf5
1168/1168 [==============================] – 0s 156us/sample – loss: 26542.4242 – mean_absolute_error: 26542.4219 – val_loss: 28778.5851 – val_mean_absolute_error: 28778.5859
Epoch 83/500
1024/1168 [=========================>….] – ETA: 0s – loss: 26073.3654 – mean_absolute_error: 26073.3652
Epoch 00083: val_loss did not improve from 28778.58505
1168/1168 [==============================] – 0s 123us/sample – loss: 26029.8058 – mean_absolute_error: 26029.8047 – val_loss: 29064.1296 – val_mean_absolute_error: 29064.1270
Epoch 84/500
1024/1168 [=========================>….] – ETA: 0s – loss: 27103.3174 – mean_absolute_error: 27103.3184
Epoch 00084: val_loss did not improve from 28778.58505
1168/1168 [==============================] – 0s 124us/sample – loss: 26344.7847 – mean_absolute_error: 26344.7852 – val_loss: 29133.9880 – val_mean_absolute_error: 29133.9902
Epoch 85/500
896/1168 [======================>…….] – ETA: 0s – loss: 25699.0648 – mean_absolute_error: 25699.0664
Epoch 00085: val_loss did not improve from 28778.58505
1168/1168 [==============================] – 0s 131us/sample – loss: 25454.6394 – mean_absolute_error: 25454.6406 – val_loss: 29310.8457 – val_mean_absolute_error: 29310.8457
Epoch 86/500
1024/1168 [=========================>….] – ETA: 0s – loss: 24942.9625 – mean_absolute_error: 24942.9629
Epoch 00086: val_loss did not improve from 28778.58505
1168/1168 [==============================] – 0s 121us/sample – loss: 25408.4566 – mean_absolute_error: 25408.4551 – val_loss: 29321.1127 – val_mean_absolute_error: 29321.1133
Epoch 87/500
992/1168 [========================>…..] – ETA: 0s – loss: 25885.1266 – mean_absolute_error: 25885.1270
Epoch 00087: val_loss improved from 28778.58505 to 27940.99189, saving model to Models/Weights-087–27940.99189.hdf5
1168/1168 [==============================] – 0s 146us/sample – loss: 26428.4007 – mean_absolute_error: 26428.4004 – val_loss: 27940.9919 – val_mean_absolute_error: 27940.9922
Epoch 88/500
992/1168 [========================>…..] – ETA: 0s – loss: 25173.5539 – mean_absolute_error: 25173.5547
Epoch 00088: val_loss did not improve from 27940.99189
1168/1168 [==============================] – 0s 129us/sample – loss: 24957.8675 – mean_absolute_error: 24957.8672 – val_loss: 28118.0465 – val_mean_absolute_error: 28118.0469
Epoch 89/500
960/1168 [=======================>……] – ETA: 0s – loss: 24087.6130 – mean_absolute_error: 24087.6133
Epoch 00089: val_loss did not improve from 27940.99189
1168/1168 [==============================] – 0s 130us/sample – loss: 24655.8400 – mean_absolute_error: 24655.8398 – val_loss: 39633.4615 – val_mean_absolute_error: 39633.4609
Epoch 90/500
1056/1168 [==========================>…] – ETA: 0s – loss: 26635.5010 – mean_absolute_error: 26635.4980
Epoch 00090: val_loss did not improve from 27940.99189
1168/1168 [==============================] – 0s 121us/sample – loss: 26382.5081 – mean_absolute_error: 26382.5059 – val_loss: 33455.3237 – val_mean_absolute_error: 33455.3242
Epoch 91/500
992/1168 [========================>…..] – ETA: 0s – loss: 26464.6474 – mean_absolute_error: 26464.6406
Epoch 00091: val_loss did not improve from 27940.99189
1168/1168 [==============================] – 0s 123us/sample – loss: 25824.3840 – mean_absolute_error: 25824.3789 – val_loss: 27971.5628 – val_mean_absolute_error: 27971.5625
Epoch 92/500
928/1168 [======================>…….] – ETA: 0s – loss: 24951.2045 – mean_absolute_error: 24951.2012
Epoch 00092: val_loss did not improve from 27940.99189
1168/1168 [==============================] – 0s 135us/sample – loss: 24770.9550 – mean_absolute_error: 24770.9492 – val_loss: 28365.4914 – val_mean_absolute_error: 28365.4922
Epoch 93/500
928/1168 [======================>…….] – ETA: 0s – loss: 25305.6162 – mean_absolute_error: 25305.6172
Epoch 00093: val_loss did not improve from 27940.99189
1168/1168 [==============================] – 0s 130us/sample – loss: 25394.3419 – mean_absolute_error: 25394.3398 – val_loss: 29577.3414 – val_mean_absolute_error: 29577.3398
Epoch 94/500
1024/1168 [=========================>….] – ETA: 0s – loss: 24799.4147 – mean_absolute_error: 24799.4141
Epoch 00094: val_loss did not improve from 27940.99189
1168/1168 [==============================] – 0s 124us/sample – loss: 24473.5634 – mean_absolute_error: 24473.5625 – val_loss: 28135.3023 – val_mean_absolute_error: 28135.3008
Epoch 95/500
1024/1168 [=========================>….] – ETA: 0s – loss: 23834.3518 – mean_absolute_error: 23834.3555
Epoch 00095: val_loss did not improve from 27940.99189
1168/1168 [==============================] – 0s 126us/sample – loss: 23956.2569 – mean_absolute_error: 23956.2598 – val_loss: 29207.4493 – val_mean_absolute_error: 29207.4512
Epoch 96/500
992/1168 [========================>…..] – ETA: 0s – loss: 23637.2030 – mean_absolute_error: 23637.2012
Epoch 00096: val_loss improved from 27940.99189 to 26100.68311, saving model to Models/Weights-096–26100.68311.hdf5
1168/1168 [==============================] – 0s 146us/sample – loss: 23303.2630 – mean_absolute_error: 23303.2617 – val_loss: 26100.6831 – val_mean_absolute_error: 26100.6836
Epoch 97/500
1056/1168 [==========================>…] – ETA: 0s – loss: 22743.9552 – mean_absolute_error: 22743.9570
Epoch 00097: val_loss improved from 26100.68311 to 25760.82379, saving model to Models/Weights-097–25760.82379.hdf5
1168/1168 [==============================] – 0s 145us/sample – loss: 22953.2090 – mean_absolute_error: 22953.2109 – val_loss: 25760.8238 – val_mean_absolute_error: 25760.8242
Epoch 98/500
1024/1168 [=========================>….] – ETA: 0s – loss: 22534.2486 – mean_absolute_error: 22534.2520
Epoch 00098: val_loss did not improve from 25760.82379
1168/1168 [==============================] – 0s 122us/sample – loss: 22524.5306 – mean_absolute_error: 22524.5352 – val_loss: 25947.0678 – val_mean_absolute_error: 25947.0664
Epoch 99/500
1024/1168 [=========================>….] – ETA: 0s – loss: 25985.3834 – mean_absolute_error: 25985.3848
Epoch 00099: val_loss did not improve from 25760.82379
1168/1168 [==============================] – 0s 125us/sample – loss: 25665.2437 – mean_absolute_error: 25665.2441 – val_loss: 26430.8609 – val_mean_absolute_error: 26430.8613
Epoch 100/500
992/1168 [========================>…..] – ETA: 0s – loss: 24675.7551 – mean_absolute_error: 24675.7539
Epoch 00100: val_loss improved from 25760.82379 to 25423.93140, saving model to Models/Weights-100–25423.93140.hdf5
1168/1168 [==============================] – 0s 149us/sample – loss: 23900.3597 – mean_absolute_error: 23900.3574 – val_loss: 25423.9314 – val_mean_absolute_error: 25423.9297
Epoch 101/500
992/1168 [========================>…..] – ETA: 0s – loss: 23942.5267 – mean_absolute_error: 23942.5254
Epoch 00101: val_loss did not improve from 25423.93140
1168/1168 [==============================] – 0s 122us/sample – loss: 24057.9681 – mean_absolute_error: 24057.9648 – val_loss: 26359.0853 – val_mean_absolute_error: 26359.0840
Epoch 102/500
1056/1168 [==========================>…] – ETA: 0s – loss: 24639.4232 – mean_absolute_error: 24639.4258
Epoch 00102: val_loss improved from 25423.93140 to 25299.91856, saving model to Models/Weights-102–25299.91856.hdf5
1168/1168 [==============================] – 0s 144us/sample – loss: 24498.2606 – mean_absolute_error: 24498.2617 – val_loss: 25299.9186 – val_mean_absolute_error: 25299.9219
Epoch 103/500
864/1168 [=====================>……..] – ETA: 0s – loss: 24140.3747 – mean_absolute_error: 24140.3750
Epoch 00103: val_loss did not improve from 25299.91856
1168/1168 [==============================] – 0s 149us/sample – loss: 22928.1778 – mean_absolute_error: 22928.1777 – val_loss: 25388.9081 – val_mean_absolute_error: 25388.9102
Epoch 104/500
1056/1168 [==========================>…] – ETA: 0s – loss: 21198.7976 – mean_absolute_error: 21198.7949
Epoch 00104: val_loss did not improve from 25299.91856
1168/1168 [==============================] – 0s 120us/sample – loss: 21726.7578 – mean_absolute_error: 21726.7559 – val_loss: 25912.1047 – val_mean_absolute_error: 25912.1035
Epoch 105/500
1056/1168 [==========================>…] – ETA: 0s – loss: 21942.8060 – mean_absolute_error: 21942.8086
Epoch 00105: val_loss did not improve from 25299.91856
1168/1168 [==============================] – 0s 119us/sample – loss: 22327.4290 – mean_absolute_error: 22327.4297 – val_loss: 25741.3558 – val_mean_absolute_error: 25741.3555
Epoch 106/500
960/1168 [=======================>……] – ETA: 0s – loss: 21447.1472 – mean_absolute_error: 21447.1465
Epoch 00106: val_loss did not improve from 25299.91856
1168/1168 [==============================] – 0s 126us/sample – loss: 22595.6095 – mean_absolute_error: 22595.6074 – val_loss: 33114.9163 – val_mean_absolute_error: 33114.9141
Epoch 107/500
992/1168 [========================>…..] – ETA: 0s – loss: 25748.8987 – mean_absolute_error: 25748.8984
Epoch 00107: val_loss did not improve from 25299.91856
1168/1168 [==============================] – 0s 125us/sample – loss: 25241.7861 – mean_absolute_error: 25241.7852 – val_loss: 29168.2449 – val_mean_absolute_error: 29168.2461
Epoch 108/500
1024/1168 [=========================>….] – ETA: 0s – loss: 24116.7479 – mean_absolute_error: 24116.7480
Epoch 00108: val_loss did not improve from 25299.91856
1168/1168 [==============================] – 0s 123us/sample – loss: 23996.6789 – mean_absolute_error: 23996.6797 – val_loss: 27693.3996 – val_mean_absolute_error: 27693.3965
Epoch 109/500
1056/1168 [==========================>…] – ETA: 0s – loss: 22092.6867 – mean_absolute_error: 22092.6855
Epoch 00109: val_loss improved from 25299.91856 to 25195.78286, saving model to Models/Weights-109–25195.78286.hdf5
1168/1168 [==============================] – 0s 145us/sample – loss: 22208.5353 – mean_absolute_error: 22208.5352 – val_loss: 25195.7829 – val_mean_absolute_error: 25195.7852
Epoch 110/500
960/1168 [=======================>……] – ETA: 0s – loss: 22978.0640 – mean_absolute_error: 22978.0625
Epoch 00110: val_loss improved from 25195.78286 to 24048.99358, saving model to Models/Weights-110–24048.99358.hdf5
1168/1168 [==============================] – 0s 146us/sample – loss: 22305.4088 – mean_absolute_error: 22305.4082 – val_loss: 24048.9936 – val_mean_absolute_error: 24048.9941
Epoch 111/500
960/1168 [=======================>……] – ETA: 0s – loss: 20773.4979 – mean_absolute_error: 20773.4980
Epoch 00111: val_loss did not improve from 24048.99358
1168/1168 [==============================] – 0s 127us/sample – loss: 21048.8256 – mean_absolute_error: 21048.8262 – val_loss: 24187.5317 – val_mean_absolute_error: 24187.5312
Epoch 112/500
992/1168 [========================>…..] – ETA: 0s – loss: 21646.4073 – mean_absolute_error: 21646.4102
Epoch 00112: val_loss did not improve from 24048.99358
1168/1168 [==============================] – 0s 130us/sample – loss: 21354.6229 – mean_absolute_error: 21354.6230 – val_loss: 26335.3701 – val_mean_absolute_error: 26335.3691
Epoch 113/500
928/1168 [======================>…….] – ETA: 0s – loss: 21113.6827 – mean_absolute_error: 21113.6816
Epoch 00113: val_loss did not improve from 24048.99358
1168/1168 [==============================] – 0s 131us/sample – loss: 21619.5710 – mean_absolute_error: 21619.5703 – val_loss: 24102.6518 – val_mean_absolute_error: 24102.6543
Epoch 114/500
1120/1168 [===========================>..] – ETA: 0s – loss: 20842.2819 – mean_absolute_error: 20842.2812
Epoch 00114: val_loss did not improve from 24048.99358
1168/1168 [==============================] – 0s 118us/sample – loss: 20982.8759 – mean_absolute_error: 20982.8770 – val_loss: 24498.5087 – val_mean_absolute_error: 24498.5078
Epoch 115/500
992/1168 [========================>…..] – ETA: 0s – loss: 21414.8847 – mean_absolute_error: 21414.8828
Epoch 00115: val_loss did not improve from 24048.99358
1168/1168 [==============================] – 0s 126us/sample – loss: 20981.1840 – mean_absolute_error: 20981.1836 – val_loss: 24848.6483 – val_mean_absolute_error: 24848.6484
Epoch 116/500
1024/1168 [=========================>….] – ETA: 0s – loss: 20378.6135 – mean_absolute_error: 20378.6133
Epoch 00116: val_loss did not improve from 24048.99358
1168/1168 [==============================] – 0s 126us/sample – loss: 20829.3941 – mean_absolute_error: 20829.3945 – val_loss: 25249.4987 – val_mean_absolute_error: 25249.4961
Epoch 117/500
1024/1168 [=========================>….] – ETA: 0s – loss: 23928.0799 – mean_absolute_error: 23928.0762
Epoch 00117: val_loss did not improve from 24048.99358
1168/1168 [==============================] – 0s 122us/sample – loss: 23875.6426 – mean_absolute_error: 23875.6406 – val_loss: 24907.4303 – val_mean_absolute_error: 24907.4297
Epoch 118/500
1088/1168 [==========================>…] – ETA: 0s – loss: 21419.9300 – mean_absolute_error: 21419.9297
Epoch 00118: val_loss improved from 24048.99358 to 23949.07853, saving model to Models/Weights-118–23949.07853.hdf5
1168/1168 [==============================] – 0s 135us/sample – loss: 21298.5195 – mean_absolute_error: 21298.5215 – val_loss: 23949.0785 – val_mean_absolute_error: 23949.0781
Epoch 119/500
928/1168 [======================>…….] – ETA: 0s – loss: 21830.8555 – mean_absolute_error: 21830.8574
Epoch 00119: val_loss improved from 23949.07853 to 23623.56138, saving model to Models/Weights-119–23623.56138.hdf5
1168/1168 [==============================] – 0s 150us/sample – loss: 22368.5957 – mean_absolute_error: 22368.5957 – val_loss: 23623.5614 – val_mean_absolute_error: 23623.5625
Epoch 120/500
992/1168 [========================>…..] – ETA: 0s – loss: 20149.5036 – mean_absolute_error: 20149.5039
Epoch 00120: val_loss did not improve from 23623.56138
1168/1168 [==============================] – 0s 123us/sample – loss: 20081.4499 – mean_absolute_error: 20081.4512 – val_loss: 25220.7721 – val_mean_absolute_error: 25220.7715
Epoch 121/500
1024/1168 [=========================>….] – ETA: 0s – loss: 20658.0835 – mean_absolute_error: 20658.0840
Epoch 00121: val_loss did not improve from 23623.56138
1168/1168 [==============================] – 0s 122us/sample – loss: 20536.2754 – mean_absolute_error: 20536.2773 – val_loss: 25703.7220 – val_mean_absolute_error: 25703.7227
Epoch 122/500
1024/1168 [=========================>….] – ETA: 0s – loss: 21215.4172 – mean_absolute_error: 21215.4199
Epoch 00122: val_loss improved from 23623.56138 to 23430.09346, saving model to Models/Weights-122–23430.09346.hdf5
1168/1168 [==============================] – 0s 141us/sample – loss: 21068.8157 – mean_absolute_error: 21068.8184 – val_loss: 23430.0935 – val_mean_absolute_error: 23430.0918
Epoch 123/500
1024/1168 [=========================>….] – ETA: 0s – loss: 21721.4243 – mean_absolute_error: 21721.4199
Epoch 00123: val_loss did not improve from 23430.09346
1168/1168 [==============================] – 0s 122us/sample – loss: 21798.4437 – mean_absolute_error: 21798.4375 – val_loss: 24153.1636 – val_mean_absolute_error: 24153.1621
Epoch 124/500
1024/1168 [=========================>….] – ETA: 0s – loss: 19182.4017 – mean_absolute_error: 19182.4023
Epoch 00124: val_loss did not improve from 23430.09346
1168/1168 [==============================] – 0s 124us/sample – loss: 19862.6203 – mean_absolute_error: 19862.6191 – val_loss: 24546.9250 – val_mean_absolute_error: 24546.9258
Epoch 125/500
1120/1168 [===========================>..] – ETA: 0s – loss: 19825.7501 – mean_absolute_error: 19825.7480
Epoch 00125: val_loss improved from 23430.09346 to 22990.28673, saving model to Models/Weights-125–22990.28673.hdf5
1168/1168 [==============================] – 0s 199us/sample – loss: 20590.0403 – mean_absolute_error: 20590.0371 – val_loss: 22990.2867 – val_mean_absolute_error: 22990.2852
Epoch 126/500
992/1168 [========================>…..] – ETA: 0s – loss: 24256.8672 – mean_absolute_error: 24256.8691
Epoch 00126: val_loss did not improve from 22990.28673
1168/1168 [==============================] – 0s 123us/sample – loss: 25031.4101 – mean_absolute_error: 25031.4102 – val_loss: 24527.6775 – val_mean_absolute_error: 24527.6777
Epoch 127/500
1024/1168 [=========================>….] – ETA: 0s – loss: 23066.7787 – mean_absolute_error: 23066.7773
Epoch 00127: val_loss did not improve from 22990.28673
1168/1168 [==============================] – 0s 122us/sample – loss: 23180.3557 – mean_absolute_error: 23180.3555 – val_loss: 23835.9401 – val_mean_absolute_error: 23835.9395
Epoch 128/500
896/1168 [======================>…….] – ETA: 0s – loss: 21316.4180 – mean_absolute_error: 21316.4180
Epoch 00128: val_loss did not improve from 22990.28673
1168/1168 [==============================] – 0s 133us/sample – loss: 22259.3757 – mean_absolute_error: 22259.3730 – val_loss: 28891.5201 – val_mean_absolute_error: 28891.5234
Epoch 129/500
1024/1168 [=========================>….] – ETA: 0s – loss: 21610.1424 – mean_absolute_error: 21610.1426
Epoch 00129: val_loss did not improve from 22990.28673
1168/1168 [==============================] – 0s 123us/sample – loss: 21752.3701 – mean_absolute_error: 21752.3711 – val_loss: 23449.6869 – val_mean_absolute_error: 23449.6875
Epoch 130/500
992/1168 [========================>…..] – ETA: 0s – loss: 19681.9045 – mean_absolute_error: 19681.9082
Epoch 00130: val_loss did not improve from 22990.28673
1168/1168 [==============================] – 0s 128us/sample – loss: 19822.5543 – mean_absolute_error: 19822.5566 – val_loss: 23540.5572 – val_mean_absolute_error: 23540.5586
Epoch 131/500
896/1168 [======================>…….] – ETA: 0s – loss: 19941.5317 – mean_absolute_error: 19941.5312
Epoch 00131: val_loss did not improve from 22990.28673
1168/1168 [==============================] – 0s 140us/sample – loss: 19459.6975 – mean_absolute_error: 19459.6953 – val_loss: 24059.8980 – val_mean_absolute_error: 24059.8965
Epoch 132/500
1024/1168 [=========================>….] – ETA: 0s – loss: 20500.9478 – mean_absolute_error: 20500.9492
Epoch 00132: val_loss did not improve from 22990.28673
1168/1168 [==============================] – 0s 120us/sample – loss: 20422.8080 – mean_absolute_error: 20422.8105 – val_loss: 24255.8713 – val_mean_absolute_error: 24255.8711
Epoch 133/500
928/1168 [======================>…….] – ETA: 0s – loss: 20286.0744 – mean_absolute_error: 20286.0742
Epoch 00133: val_loss improved from 22990.28673 to 22736.08631, saving model to Models/Weights-133–22736.08631.hdf5
1168/1168 [==============================] – 0s 160us/sample – loss: 19879.3674 – mean_absolute_error: 19879.3652 – val_loss: 22736.0863 – val_mean_absolute_error: 22736.0859
Epoch 134/500
960/1168 [=======================>……] – ETA: 0s – loss: 19584.4340 – mean_absolute_error: 19584.4355
Epoch 00134: val_loss did not improve from 22736.08631
1168/1168 [==============================] – 0s 129us/sample – loss: 19960.3777 – mean_absolute_error: 19960.3789 – val_loss: 22768.2735 – val_mean_absolute_error: 22768.2734
Epoch 135/500
1088/1168 [==========================>…] – ETA: 0s – loss: 19306.6121 – mean_absolute_error: 19306.6113
Epoch 00135: val_loss did not improve from 22736.08631
1168/1168 [==============================] – 0s 117us/sample – loss: 19856.8466 – mean_absolute_error: 19856.8477 – val_loss: 25743.5274 – val_mean_absolute_error: 25743.5273
Epoch 136/500
992/1168 [========================>…..] – ETA: 0s – loss: 22888.0952 – mean_absolute_error: 22888.0918
Epoch 00136: val_loss improved from 22736.08631 to 22509.13206, saving model to Models/Weights-136–22509.13206.hdf5
1168/1168 [==============================] – 0s 143us/sample – loss: 21882.5016 – mean_absolute_error: 21882.5000 – val_loss: 22509.1321 – val_mean_absolute_error: 22509.1309
Epoch 137/500
1024/1168 [=========================>….] – ETA: 0s – loss: 21698.5780 – mean_absolute_error: 21698.5762
Epoch 00137: val_loss improved from 22509.13206 to 22075.88057, saving model to Models/Weights-137–22075.88057.hdf5
1168/1168 [==============================] – 0s 143us/sample – loss: 21297.3330 – mean_absolute_error: 21297.3320 – val_loss: 22075.8806 – val_mean_absolute_error: 22075.8828
Epoch 138/500
832/1168 [====================>………] – ETA: 0s – loss: 20204.9289 – mean_absolute_error: 20204.9277
Epoch 00138: val_loss did not improve from 22075.88057
1168/1168 [==============================] – 0s 139us/sample – loss: 19574.7814 – mean_absolute_error: 19574.7793 – val_loss: 25334.9347 – val_mean_absolute_error: 25334.9355
Epoch 139/500
1024/1168 [=========================>….] – ETA: 0s – loss: 20024.7159 – mean_absolute_error: 20024.7168
Epoch 00139: val_loss did not improve from 22075.88057
1168/1168 [==============================] – 0s 120us/sample – loss: 19933.3670 – mean_absolute_error: 19933.3672 – val_loss: 24447.5856 – val_mean_absolute_error: 24447.5840
Epoch 140/500
992/1168 [========================>…..] – ETA: 0s – loss: 20165.7422 – mean_absolute_error: 20165.7422
Epoch 00140: val_loss did not improve from 22075.88057
1168/1168 [==============================] – 0s 126us/sample – loss: 19826.8654 – mean_absolute_error: 19826.8672 – val_loss: 24421.6281 – val_mean_absolute_error: 24421.6289
Epoch 141/500
1024/1168 [=========================>….] – ETA: 0s – loss: 20413.7510 – mean_absolute_error: 20413.7520
Epoch 00141: val_loss did not improve from 22075.88057
1168/1168 [==============================] – 0s 132us/sample – loss: 20341.0662 – mean_absolute_error: 20341.0684 – val_loss: 22116.8315 – val_mean_absolute_error: 22116.8320
Epoch 142/500
896/1168 [======================>…….] – ETA: 0s – loss: 18369.8790 – mean_absolute_error: 18369.8809
Epoch 00142: val_loss did not improve from 22075.88057
1168/1168 [==============================] – 0s 142us/sample – loss: 19120.9927 – mean_absolute_error: 19120.9922 – val_loss: 23239.4618 – val_mean_absolute_error: 23239.4629
Epoch 143/500
1056/1168 [==========================>…] – ETA: 0s – loss: 19653.2905 – mean_absolute_error: 19653.2891
Epoch 00143: val_loss did not improve from 22075.88057
1168/1168 [==============================] – 0s 178us/sample – loss: 19486.1513 – mean_absolute_error: 19486.1523 – val_loss: 22572.4516 – val_mean_absolute_error: 22572.4531
Epoch 144/500
864/1168 [=====================>……..] – ETA: 0s – loss: 18582.9372 – mean_absolute_error: 18582.9375
Epoch 00144: val_loss did not improve from 22075.88057
1168/1168 [==============================] – 0s 144us/sample – loss: 19279.1903 – mean_absolute_error: 19279.1895 – val_loss: 22416.9828 – val_mean_absolute_error: 22416.9824
Epoch 145/500
896/1168 [======================>…….] – ETA: 0s – loss: 19888.5622 – mean_absolute_error: 19888.5625
Epoch 00145: val_loss did not improve from 22075.88057
1168/1168 [==============================] – 0s 139us/sample – loss: 20096.3535 – mean_absolute_error: 20096.3555 – val_loss: 22156.8480 – val_mean_absolute_error: 22156.8457
Epoch 146/500
896/1168 [======================>…….] – ETA: 0s – loss: 18793.9495 – mean_absolute_error: 18793.9492
Epoch 00146: val_loss improved from 22075.88057 to 21666.45960, saving model to Models/Weights-146–21666.45960.hdf5
1168/1168 [==============================] – 0s 163us/sample – loss: 18675.3499 – mean_absolute_error: 18675.3516 – val_loss: 21666.4596 – val_mean_absolute_error: 21666.4590
Epoch 147/500
864/1168 [=====================>……..] – ETA: 0s – loss: 19813.6294 – mean_absolute_error: 19813.6289
Epoch 00147: val_loss did not improve from 21666.45960
1168/1168 [==============================] – 0s 143us/sample – loss: 20417.8488 – mean_absolute_error: 20417.8496 – val_loss: 24471.1900 – val_mean_absolute_error: 24471.1895
Epoch 148/500
800/1168 [===================>……….] – ETA: 0s – loss: 24095.1317 – mean_absolute_error: 24095.1328
Epoch 00148: val_loss did not improve from 21666.45960
1168/1168 [==============================] – 0s 152us/sample – loss: 23418.1695 – mean_absolute_error: 23418.1699 – val_loss: 23902.6045 – val_mean_absolute_error: 23902.6055
Epoch 149/500
832/1168 [====================>………] – ETA: 0s – loss: 21772.1105 – mean_absolute_error: 21772.1113
Epoch 00149: val_loss did not improve from 21666.45960
1168/1168 [==============================] – 0s 148us/sample – loss: 20991.2372 – mean_absolute_error: 20991.2363 – val_loss: 25115.5517 – val_mean_absolute_error: 25115.5508
Epoch 150/500
1056/1168 [==========================>…] – ETA: 0s – loss: 20880.6542 – mean_absolute_error: 20880.6562
Epoch 00150: val_loss did not improve from 21666.45960
1168/1168 [==============================] – 0s 189us/sample – loss: 20671.5915 – mean_absolute_error: 20671.5938 – val_loss: 24414.5071 – val_mean_absolute_error: 24414.5078
Epoch 151/500
960/1168 [=======================>……] – ETA: 0s – loss: 19232.8300 – mean_absolute_error: 19232.8320
Epoch 00151: val_loss did not improve from 21666.45960
1168/1168 [==============================] – 0s 198us/sample – loss: 19354.8126 – mean_absolute_error: 19354.8125 – val_loss: 22437.7816 – val_mean_absolute_error: 22437.7812
Epoch 152/500
1088/1168 [==========================>…] – ETA: 0s – loss: 19520.2866 – mean_absolute_error: 19520.2852
Epoch 00152: val_loss did not improve from 21666.45960
1168/1168 [==============================] – 0s 170us/sample – loss: 19612.1080 – mean_absolute_error: 19612.1055 – val_loss: 22419.4082 – val_mean_absolute_error: 22419.4082
Epoch 153/500
960/1168 [=======================>……] – ETA: 0s – loss: 19849.0144 – mean_absolute_error: 19849.0117
Epoch 00153: val_loss did not improve from 21666.45960
1168/1168 [==============================] – 0s 137us/sample – loss: 20214.8688 – mean_absolute_error: 20214.8672 – val_loss: 24436.8417 – val_mean_absolute_error: 24436.8398
Epoch 154/500
896/1168 [======================>…….] – ETA: 0s – loss: 19952.9760 – mean_absolute_error: 19952.9746
Epoch 00154: val_loss did not improve from 21666.45960
1168/1168 [==============================] – 0s 142us/sample – loss: 19737.4842 – mean_absolute_error: 19737.4824 – val_loss: 22191.6927 – val_mean_absolute_error: 22191.6914
Epoch 155/500
832/1168 [====================>………] – ETA: 0s – loss: 18376.6402 – mean_absolute_error: 18376.6387
Epoch 00155: val_loss did not improve from 21666.45960
1168/1168 [==============================] – 0s 153us/sample – loss: 18201.7378 – mean_absolute_error: 18201.7363 – val_loss: 22312.0291 – val_mean_absolute_error: 22312.0293
Epoch 156/500
928/1168 [======================>…….] – ETA: 0s – loss: 19806.6617 – mean_absolute_error: 19806.6602
Epoch 00156: val_loss did not improve from 21666.45960
1168/1168 [==============================] – 0s 139us/sample – loss: 19910.4188 – mean_absolute_error: 19910.4141 – val_loss: 25941.9602 – val_mean_absolute_error: 25941.9609
Epoch 157/500
928/1168 [======================>…….] – ETA: 0s – loss: 21851.5649 – mean_absolute_error: 21851.5684
Epoch 00157: val_loss did not improve from 21666.45960
1168/1168 [==============================] – 0s 140us/sample – loss: 20954.1882 – mean_absolute_error: 20954.1875 – val_loss: 22751.3978 – val_mean_absolute_error: 22751.3965
Epoch 158/500
928/1168 [======================>…….] – ETA: 0s – loss: 18354.8168 – mean_absolute_error: 18354.8145
Epoch 00158: val_loss did not improve from 21666.45960
1168/1168 [==============================] – 0s 128us/sample – loss: 18940.2233 – mean_absolute_error: 18940.2227 – val_loss: 21956.9349 – val_mean_absolute_error: 21956.9336
Epoch 159/500
928/1168 [======================>…….] – ETA: 0s – loss: 18146.0272 – mean_absolute_error: 18146.0254
Epoch 00159: val_loss did not improve from 21666.45960
1168/1168 [==============================] – 0s 141us/sample – loss: 18536.4695 – mean_absolute_error: 18536.4668 – val_loss: 22300.8713 – val_mean_absolute_error: 22300.8711
Epoch 160/500
960/1168 [=======================>……] – ETA: 0s – loss: 18357.5418 – mean_absolute_error: 18357.5410
Epoch 00160: val_loss did not improve from 21666.45960
1168/1168 [==============================] – 0s 130us/sample – loss: 18824.5435 – mean_absolute_error: 18824.5430 – val_loss: 22335.7567 – val_mean_absolute_error: 22335.7578
Epoch 161/500
1088/1168 [==========================>…] – ETA: 0s – loss: 17935.2874 – mean_absolute_error: 17935.2871
Epoch 00161: val_loss did not improve from 21666.45960
1168/1168 [==============================] – 0s 117us/sample – loss: 18230.0467 – mean_absolute_error: 18230.0488 – val_loss: 22687.2400 – val_mean_absolute_error: 22687.2402
Epoch 162/500
1024/1168 [=========================>….] – ETA: 0s – loss: 19314.6831 – mean_absolute_error: 19314.6816
Epoch 00162: val_loss did not improve from 21666.45960
1168/1168 [==============================] – 0s 124us/sample – loss: 19658.9870 – mean_absolute_error: 19658.9844 – val_loss: 23582.7912 – val_mean_absolute_error: 23582.7910
Epoch 163/500
1056/1168 [==========================>…] – ETA: 0s – loss: 19000.3126 – mean_absolute_error: 19000.3125
Epoch 00163: val_loss did not improve from 21666.45960
1168/1168 [==============================] – 0s 120us/sample – loss: 19194.7212 – mean_absolute_error: 19194.7207 – val_loss: 22008.8200 – val_mean_absolute_error: 22008.8184
Epoch 164/500
1024/1168 [=========================>….] – ETA: 0s – loss: 18659.6918 – mean_absolute_error: 18659.6895
Epoch 00164: val_loss did not improve from 21666.45960
1168/1168 [==============================] – 0s 123us/sample – loss: 18577.4239 – mean_absolute_error: 18577.4219 – val_loss: 22718.7215 – val_mean_absolute_error: 22718.7227
Epoch 165/500
960/1168 [=======================>……] – ETA: 0s – loss: 17977.9676 – mean_absolute_error: 17977.9688
Epoch 00165: val_loss did not improve from 21666.45960
1168/1168 [==============================] – 0s 135us/sample – loss: 18463.3787 – mean_absolute_error: 18463.3828 – val_loss: 37545.8113 – val_mean_absolute_error: 37545.8125
Epoch 166/500
960/1168 [=======================>……] – ETA: 0s – loss: 22661.5484 – mean_absolute_error: 22661.5449
Epoch 00166: val_loss did not improve from 21666.45960
1168/1168 [==============================] – 0s 138us/sample – loss: 22098.2380 – mean_absolute_error: 22098.2363 – val_loss: 21720.7644 – val_mean_absolute_error: 21720.7617
Epoch 167/500
800/1168 [===================>……….] – ETA: 0s – loss: 19648.3425 – mean_absolute_error: 19648.3418
Epoch 00167: val_loss did not improve from 21666.45960
1168/1168 [==============================] – 0s 153us/sample – loss: 19088.7970 – mean_absolute_error: 19088.7949 – val_loss: 22364.1132 – val_mean_absolute_error: 22364.1133
Epoch 168/500
1024/1168 [=========================>….] – ETA: 0s – loss: 19813.3383 – mean_absolute_error: 19813.3418
Epoch 00168: val_loss did not improve from 21666.45960
1168/1168 [==============================] – 0s 128us/sample – loss: 19368.5707 – mean_absolute_error: 19368.5742 – val_loss: 21680.4195 – val_mean_absolute_error: 21680.4219
Epoch 169/500
1056/1168 [==========================>…] – ETA: 0s – loss: 17879.1916 – mean_absolute_error: 17879.1895
Epoch 00169: val_loss did not improve from 21666.45960
1168/1168 [==============================] – 0s 120us/sample – loss: 17747.8121 – mean_absolute_error: 17747.8105 – val_loss: 21717.7318 – val_mean_absolute_error: 21717.7324
Epoch 170/500
992/1168 [========================>…..] – ETA: 0s – loss: 18064.1510 – mean_absolute_error: 18064.1523
Epoch 00170: val_loss did not improve from 21666.45960
1168/1168 [==============================] – 0s 126us/sample – loss: 18557.7776 – mean_absolute_error: 18557.7793 – val_loss: 22181.6759 – val_mean_absolute_error: 22181.6758
Epoch 171/500
1024/1168 [=========================>….] – ETA: 0s – loss: 19720.1451 – mean_absolute_error: 19720.1484
Epoch 00171: val_loss did not improve from 21666.45960
1168/1168 [==============================] – 0s 120us/sample – loss: 19503.2294 – mean_absolute_error: 19503.2344 – val_loss: 21962.8538 – val_mean_absolute_error: 21962.8535
Epoch 172/500
1024/1168 [=========================>….] – ETA: 0s – loss: 17606.8132 – mean_absolute_error: 17606.8145
Epoch 00172: val_loss did not improve from 21666.45960
1168/1168 [==============================] – 0s 121us/sample – loss: 17797.3198 – mean_absolute_error: 17797.3184 – val_loss: 22593.2917 – val_mean_absolute_error: 22593.2910
Epoch 173/500
1024/1168 [=========================>….] – ETA: 0s – loss: 17931.2068 – mean_absolute_error: 17931.2051
Epoch 00173: val_loss did not improve from 21666.45960
1168/1168 [==============================] – 0s 122us/sample – loss: 17831.9037 – mean_absolute_error: 17831.9043 – val_loss: 21900.8054 – val_mean_absolute_error: 21900.8066
Epoch 174/500
928/1168 [======================>…….] – ETA: 0s – loss: 17359.3067 – mean_absolute_error: 17359.3086
Epoch 00174: val_loss did not improve from 21666.45960
1168/1168 [==============================] – 0s 130us/sample – loss: 17763.5687 – mean_absolute_error: 17763.5703 – val_loss: 22182.1499 – val_mean_absolute_error: 22182.1484
Epoch 175/500
992/1168 [========================>…..] – ETA: 0s – loss: 18278.6717 – mean_absolute_error: 18278.6738
Epoch 00175: val_loss improved from 21666.45960 to 21445.55988, saving model to Models/Weights-175–21445.55988.hdf5
1168/1168 [==============================] – 0s 144us/sample – loss: 18457.5285 – mean_absolute_error: 18457.5312 – val_loss: 21445.5599 – val_mean_absolute_error: 21445.5625
Epoch 176/500
1056/1168 [==========================>…] – ETA: 0s – loss: 17808.7448 – mean_absolute_error: 17808.7441
Epoch 00176: val_loss did not improve from 21445.55988
1168/1168 [==============================] – 0s 122us/sample – loss: 18149.1385 – mean_absolute_error: 18149.1367 – val_loss: 21936.6283 – val_mean_absolute_error: 21936.6289
Epoch 177/500
768/1168 [==================>………..] – ETA: 0s – loss: 18784.7463 – mean_absolute_error: 18784.7480
Epoch 00177: val_loss did not improve from 21445.55988
1168/1168 [==============================] – 0s 147us/sample – loss: 17722.4225 – mean_absolute_error: 17722.4238 – val_loss: 22755.9389 – val_mean_absolute_error: 22755.9395
Epoch 178/500
768/1168 [==================>………..] – ETA: 0s – loss: 17963.9006 – mean_absolute_error: 17963.9023
Epoch 00178: val_loss improved from 21445.55988 to 20631.49443, saving model to Models/Weights-178–20631.49443.hdf5
1168/1168 [==============================] – 0s 190us/sample – loss: 17987.7644 – mean_absolute_error: 17987.7656 – val_loss: 20631.4944 – val_mean_absolute_error: 20631.4922
Epoch 179/500
1152/1168 [============================>.] – ETA: 0s – loss: 17388.9605 – mean_absolute_error: 17388.9609
Epoch 00179: val_loss did not improve from 20631.49443
1168/1168 [==============================] – 0s 164us/sample – loss: 17421.3617 – mean_absolute_error: 17421.3613 – val_loss: 22063.3021 – val_mean_absolute_error: 22063.3008
Epoch 180/500
800/1168 [===================>……….] – ETA: 0s – loss: 19839.0639 – mean_absolute_error: 19839.0664
Epoch 00180: val_loss did not improve from 20631.49443
1168/1168 [==============================] – 0s 145us/sample – loss: 19409.3890 – mean_absolute_error: 19409.3906 – val_loss: 22611.2677 – val_mean_absolute_error: 22611.2695
Epoch 181/500
800/1168 [===================>……….] – ETA: 0s – loss: 17706.6698 – mean_absolute_error: 17706.6699
Epoch 00181: val_loss did not improve from 20631.49443
1168/1168 [==============================] – 0s 156us/sample – loss: 17567.1855 – mean_absolute_error: 17567.1875 – val_loss: 21238.6232 – val_mean_absolute_error: 21238.6211
Epoch 182/500
960/1168 [=======================>……] – ETA: 0s – loss: 19183.4176 – mean_absolute_error: 19183.4160
Epoch 00182: val_loss did not improve from 20631.49443
1168/1168 [==============================] – 0s 136us/sample – loss: 19098.2191 – mean_absolute_error: 19098.2188 – val_loss: 21494.7002 – val_mean_absolute_error: 21494.7031
Epoch 183/500
928/1168 [======================>…….] – ETA: 0s – loss: 18146.1964 – mean_absolute_error: 18146.1953
Epoch 00183: val_loss did not improve from 20631.49443
1168/1168 [==============================] – 0s 127us/sample – loss: 17874.3248 – mean_absolute_error: 17874.3242 – val_loss: 20787.3895 – val_mean_absolute_error: 20787.3887
Epoch 184/500
960/1168 [=======================>……] – ETA: 0s – loss: 17375.9347 – mean_absolute_error: 17375.9336
Epoch 00184: val_loss did not improve from 20631.49443
1168/1168 [==============================] – 0s 141us/sample – loss: 17474.0987 – mean_absolute_error: 17474.0996 – val_loss: 22985.5092 – val_mean_absolute_error: 22985.5078
Epoch 185/500
960/1168 [=======================>……] – ETA: 0s – loss: 18187.7201 – mean_absolute_error: 18187.7188
Epoch 00185: val_loss did not improve from 20631.49443
1168/1168 [==============================] – 0s 132us/sample – loss: 18273.0552 – mean_absolute_error: 18273.0547 – val_loss: 21852.5378 – val_mean_absolute_error: 21852.5371
Epoch 186/500
1024/1168 [=========================>….] – ETA: 0s – loss: 18095.3817 – mean_absolute_error: 18095.3809
Epoch 00186: val_loss did not improve from 20631.49443
1168/1168 [==============================] – 0s 181us/sample – loss: 18186.4450 – mean_absolute_error: 18186.4434 – val_loss: 26895.1211 – val_mean_absolute_error: 26895.1191
Epoch 187/500
1152/1168 [============================>.] – ETA: 0s – loss: 20272.6115 – mean_absolute_error: 20272.6133
Epoch 00187: val_loss did not improve from 20631.49443
1168/1168 [==============================] – 0s 175us/sample – loss: 20425.7142 – mean_absolute_error: 20425.7148 – val_loss: 25391.6566 – val_mean_absolute_error: 25391.6582
Epoch 188/500
1088/1168 [==========================>…] – ETA: 0s – loss: 20374.7450 – mean_absolute_error: 20374.7441
Epoch 00188: val_loss did not improve from 20631.49443
1168/1168 [==============================] – 0s 173us/sample – loss: 20170.6711 – mean_absolute_error: 20170.6699 – val_loss: 27169.5036 – val_mean_absolute_error: 27169.5059
Epoch 189/500
1088/1168 [==========================>…] – ETA: 0s – loss: 18142.4016 – mean_absolute_error: 18142.4023
Epoch 00189: val_loss did not improve from 20631.49443
1168/1168 [==============================] – 0s 171us/sample – loss: 18290.7011 – mean_absolute_error: 18290.7012 – val_loss: 22035.4797 – val_mean_absolute_error: 22035.4785
Epoch 190/500
992/1168 [========================>…..] – ETA: 0s – loss: 16832.1843 – mean_absolute_error: 16832.1836
Epoch 00190: val_loss did not improve from 20631.49443
1168/1168 [==============================] – 0s 126us/sample – loss: 16827.6151 – mean_absolute_error: 16827.6152 – val_loss: 23532.1576 – val_mean_absolute_error: 23532.1582
Epoch 191/500
992/1168 [========================>…..] – ETA: 0s – loss: 17601.1799 – mean_absolute_error: 17601.1797
Epoch 00191: val_loss improved from 20631.49443 to 20155.00433, saving model to Models/Weights-191–20155.00433.hdf5
1168/1168 [==============================] – 0s 149us/sample – loss: 17674.6929 – mean_absolute_error: 17674.6934 – val_loss: 20155.0043 – val_mean_absolute_error: 20155.0059
Epoch 192/500
1024/1168 [=========================>….] – ETA: 0s – loss: 17979.1756 – mean_absolute_error: 17979.1758
Epoch 00192: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 122us/sample – loss: 17978.0637 – mean_absolute_error: 17978.0625 – val_loss: 22116.0785 – val_mean_absolute_error: 22116.0781
Epoch 193/500
1024/1168 [=========================>….] – ETA: 0s – loss: 17594.3775 – mean_absolute_error: 17594.3750
Epoch 00193: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 123us/sample – loss: 17470.7536 – mean_absolute_error: 17470.7520 – val_loss: 21925.2167 – val_mean_absolute_error: 21925.2168
Epoch 194/500
1056/1168 [==========================>…] – ETA: 0s – loss: 17813.3890 – mean_absolute_error: 17813.3906
Epoch 00194: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 122us/sample – loss: 17855.5724 – mean_absolute_error: 17855.5762 – val_loss: 21620.3046 – val_mean_absolute_error: 21620.3047
Epoch 195/500
1056/1168 [==========================>…] – ETA: 0s – loss: 17941.3406 – mean_absolute_error: 17941.3398
Epoch 00195: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 121us/sample – loss: 17745.1993 – mean_absolute_error: 17745.1992 – val_loss: 21745.9998 – val_mean_absolute_error: 21746.0000
Epoch 196/500
992/1168 [========================>…..] – ETA: 0s – loss: 17841.7636 – mean_absolute_error: 17841.7617
Epoch 00196: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 126us/sample – loss: 17530.8337 – mean_absolute_error: 17530.8320 – val_loss: 22355.5655 – val_mean_absolute_error: 22355.5645
Epoch 197/500
928/1168 [======================>…….] – ETA: 0s – loss: 18113.1269 – mean_absolute_error: 18113.1250
Epoch 00197: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 132us/sample – loss: 17497.6222 – mean_absolute_error: 17497.6191 – val_loss: 24520.3872 – val_mean_absolute_error: 24520.3867
Epoch 198/500
992/1168 [========================>…..] – ETA: 0s – loss: 18652.6703 – mean_absolute_error: 18652.6738
Epoch 00198: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 130us/sample – loss: 18408.8482 – mean_absolute_error: 18408.8496 – val_loss: 21456.2143 – val_mean_absolute_error: 21456.2148
Epoch 199/500
960/1168 [=======================>……] – ETA: 0s – loss: 19418.0370 – mean_absolute_error: 19418.0332
Epoch 00199: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 127us/sample – loss: 19406.3802 – mean_absolute_error: 19406.3789 – val_loss: 22157.3756 – val_mean_absolute_error: 22157.3750
Epoch 200/500
992/1168 [========================>…..] – ETA: 0s – loss: 18192.3553 – mean_absolute_error: 18192.3594
Epoch 00200: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 132us/sample – loss: 17936.0510 – mean_absolute_error: 17936.0527 – val_loss: 21945.7626 – val_mean_absolute_error: 21945.7617
Epoch 201/500
1056/1168 [==========================>…] – ETA: 0s – loss: 17595.7971 – mean_absolute_error: 17595.7969
Epoch 00201: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 117us/sample – loss: 17748.6112 – mean_absolute_error: 17748.6113 – val_loss: 21444.1679 – val_mean_absolute_error: 21444.1680
Epoch 202/500
992/1168 [========================>…..] – ETA: 0s – loss: 17524.2800 – mean_absolute_error: 17524.2832
Epoch 00202: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 126us/sample – loss: 17817.0301 – mean_absolute_error: 17817.0332 – val_loss: 21999.3509 – val_mean_absolute_error: 21999.3516
Epoch 203/500
1088/1168 [==========================>…] – ETA: 0s – loss: 16343.4837 – mean_absolute_error: 16343.4834
Epoch 00203: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 119us/sample – loss: 16422.5375 – mean_absolute_error: 16422.5371 – val_loss: 20920.3667 – val_mean_absolute_error: 20920.3652
Epoch 204/500
928/1168 [======================>…….] – ETA: 0s – loss: 17114.0579 – mean_absolute_error: 17114.0586
Epoch 00204: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 128us/sample – loss: 17157.7442 – mean_absolute_error: 17157.7422 – val_loss: 21255.8736 – val_mean_absolute_error: 21255.8750
Epoch 205/500
960/1168 [=======================>……] – ETA: 0s – loss: 16970.0336 – mean_absolute_error: 16970.0332
Epoch 00205: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 133us/sample – loss: 17226.9866 – mean_absolute_error: 17226.9844 – val_loss: 22757.7323 – val_mean_absolute_error: 22757.7344
Epoch 206/500
736/1168 [=================>…………] – ETA: 0s – loss: 18083.9791 – mean_absolute_error: 18083.9805
Epoch 00206: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 156us/sample – loss: 17666.3450 – mean_absolute_error: 17666.3457 – val_loss: 21564.2475 – val_mean_absolute_error: 21564.2461
Epoch 207/500
960/1168 [=======================>……] – ETA: 0s – loss: 18146.5751 – mean_absolute_error: 18146.5742
Epoch 00207: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 134us/sample – loss: 17794.0219 – mean_absolute_error: 17794.0234 – val_loss: 21190.4570 – val_mean_absolute_error: 21190.4551
Epoch 208/500
992/1168 [========================>…..] – ETA: 0s – loss: 17383.7374 – mean_absolute_error: 17383.7363
Epoch 00208: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 123us/sample – loss: 17113.9445 – mean_absolute_error: 17113.9434 – val_loss: 21233.5063 – val_mean_absolute_error: 21233.5078
Epoch 209/500
1088/1168 [==========================>…] – ETA: 0s – loss: 16716.7325 – mean_absolute_error: 16716.7344
Epoch 00209: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 119us/sample – loss: 16855.9071 – mean_absolute_error: 16855.9082 – val_loss: 20916.5543 – val_mean_absolute_error: 20916.5547
Epoch 210/500
992/1168 [========================>…..] – ETA: 0s – loss: 17376.3285 – mean_absolute_error: 17376.3281
Epoch 00210: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 123us/sample – loss: 16735.8622 – mean_absolute_error: 16735.8613 – val_loss: 21219.6518 – val_mean_absolute_error: 21219.6504
Epoch 211/500
960/1168 [=======================>……] – ETA: 0s – loss: 16825.6265 – mean_absolute_error: 16825.6270
Epoch 00211: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 131us/sample – loss: 16772.7442 – mean_absolute_error: 16772.7441 – val_loss: 21099.3310 – val_mean_absolute_error: 21099.3301
Epoch 212/500
1088/1168 [==========================>…] – ETA: 0s – loss: 17852.9990 – mean_absolute_error: 17852.9980
Epoch 00212: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 120us/sample – loss: 17684.2882 – mean_absolute_error: 17684.2871 – val_loss: 25009.5557 – val_mean_absolute_error: 25009.5566
Epoch 213/500
992/1168 [========================>…..] – ETA: 0s – loss: 17393.6846 – mean_absolute_error: 17393.6836
Epoch 00213: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 127us/sample – loss: 17436.1117 – mean_absolute_error: 17436.1094 – val_loss: 23266.4582 – val_mean_absolute_error: 23266.4590
Epoch 214/500
1024/1168 [=========================>….] – ETA: 0s – loss: 18240.7403 – mean_absolute_error: 18240.7402
Epoch 00214: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 122us/sample – loss: 17678.5707 – mean_absolute_error: 17678.5703 – val_loss: 23713.1950 – val_mean_absolute_error: 23713.1953
Epoch 215/500
992/1168 [========================>…..] – ETA: 0s – loss: 17090.8072 – mean_absolute_error: 17090.8086
Epoch 00215: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 123us/sample – loss: 17532.9506 – mean_absolute_error: 17532.9512 – val_loss: 22621.2292 – val_mean_absolute_error: 22621.2285
Epoch 216/500
1056/1168 [==========================>…] – ETA: 0s – loss: 16952.2753 – mean_absolute_error: 16952.2773
Epoch 00216: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 120us/sample – loss: 17466.6257 – mean_absolute_error: 17466.6270 – val_loss: 28233.5022 – val_mean_absolute_error: 28233.5039
Epoch 217/500
928/1168 [======================>…….] – ETA: 0s – loss: 18928.8814 – mean_absolute_error: 18928.8809
Epoch 00217: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 137us/sample – loss: 18694.0999 – mean_absolute_error: 18694.0996 – val_loss: 22236.3388 – val_mean_absolute_error: 22236.3398
Epoch 218/500
864/1168 [=====================>……..] – ETA: 0s – loss: 18424.4779 – mean_absolute_error: 18424.4785
Epoch 00218: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 149us/sample – loss: 19451.6528 – mean_absolute_error: 19451.6562 – val_loss: 30793.4209 – val_mean_absolute_error: 30793.4219
Epoch 219/500
1024/1168 [=========================>….] – ETA: 0s – loss: 18564.1952 – mean_absolute_error: 18564.1953
Epoch 00219: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 180us/sample – loss: 18161.6501 – mean_absolute_error: 18161.6504 – val_loss: 22360.8116 – val_mean_absolute_error: 22360.8125
Epoch 220/500
960/1168 [=======================>……] – ETA: 0s – loss: 18712.4631 – mean_absolute_error: 18712.4648
Epoch 00220: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 127us/sample – loss: 18147.5643 – mean_absolute_error: 18147.5664 – val_loss: 21235.4537 – val_mean_absolute_error: 21235.4531
Epoch 221/500
1024/1168 [=========================>….] – ETA: 0s – loss: 17126.2538 – mean_absolute_error: 17126.2539
Epoch 00221: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 122us/sample – loss: 16953.1734 – mean_absolute_error: 16953.1738 – val_loss: 21309.6798 – val_mean_absolute_error: 21309.6797
Epoch 222/500
1024/1168 [=========================>….] – ETA: 0s – loss: 17256.7879 – mean_absolute_error: 17256.7891
Epoch 00222: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 126us/sample – loss: 17414.1159 – mean_absolute_error: 17414.1172 – val_loss: 20780.6492 – val_mean_absolute_error: 20780.6484
Epoch 223/500
1024/1168 [=========================>….] – ETA: 0s – loss: 16502.3582 – mean_absolute_error: 16502.3574
Epoch 00223: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 122us/sample – loss: 16785.6678 – mean_absolute_error: 16785.6680 – val_loss: 22345.9591 – val_mean_absolute_error: 22345.9590
Epoch 224/500
960/1168 [=======================>……] – ETA: 0s – loss: 18068.6375 – mean_absolute_error: 18068.6367
Epoch 00224: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 127us/sample – loss: 17612.7142 – mean_absolute_error: 17612.7148 – val_loss: 21163.5887 – val_mean_absolute_error: 21163.5879
Epoch 225/500
832/1168 [====================>………] – ETA: 0s – loss: 18940.0248 – mean_absolute_error: 18940.0273
Epoch 00225: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 143us/sample – loss: 17987.7733 – mean_absolute_error: 17987.7734 – val_loss: 21402.4371 – val_mean_absolute_error: 21402.4375
Epoch 226/500
1024/1168 [=========================>….] – ETA: 0s – loss: 16990.0551 – mean_absolute_error: 16990.0566
Epoch 00226: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 126us/sample – loss: 16519.9988 – mean_absolute_error: 16519.9980 – val_loss: 21422.4060 – val_mean_absolute_error: 21422.4062
Epoch 227/500
928/1168 [======================>…….] – ETA: 0s – loss: 16201.6850 – mean_absolute_error: 16201.6855
Epoch 00227: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 130us/sample – loss: 16702.7078 – mean_absolute_error: 16702.7090 – val_loss: 24205.7937 – val_mean_absolute_error: 24205.7949
Epoch 228/500
1088/1168 [==========================>…] – ETA: 0s – loss: 18069.9448 – mean_absolute_error: 18069.9434
Epoch 00228: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 118us/sample – loss: 18095.8682 – mean_absolute_error: 18095.8672 – val_loss: 28085.5630 – val_mean_absolute_error: 28085.5625
Epoch 229/500
1024/1168 [=========================>….] – ETA: 0s – loss: 20224.8027 – mean_absolute_error: 20224.8008
Epoch 00229: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 127us/sample – loss: 20397.9877 – mean_absolute_error: 20397.9863 – val_loss: 29275.8818 – val_mean_absolute_error: 29275.8809
Epoch 230/500
928/1168 [======================>…….] – ETA: 0s – loss: 18245.3180 – mean_absolute_error: 18245.3164
Epoch 00230: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 133us/sample – loss: 17676.7267 – mean_absolute_error: 17676.7246 – val_loss: 25759.2458 – val_mean_absolute_error: 25759.2480
Epoch 231/500
1088/1168 [==========================>…] – ETA: 0s – loss: 16790.9760 – mean_absolute_error: 16790.9766
Epoch 00231: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 116us/sample – loss: 16776.8694 – mean_absolute_error: 16776.8711 – val_loss: 22923.1904 – val_mean_absolute_error: 22923.1914
Epoch 232/500
992/1168 [========================>…..] – ETA: 0s – loss: 18069.3876 – mean_absolute_error: 18069.3867
Epoch 00232: val_loss did not improve from 20155.00433
1168/1168 [==============================] – 0s 130us/sample – loss: 17855.9702 – mean_absolute_error: 17855.9688 – val_loss: 20742.2024 – val_mean_absolute_error: 20742.2012
Epoch 233/500
1056/1168 [==========================>…] – ETA: 0s – loss: 16159.3843 – mean_absolute_error: 16159.3848
Epoch 00233: val_loss improved from 20155.00433 to 20069.28385, saving model to Models/Weights-233–20069.28385.hdf5
1168/1168 [==============================] – 0s 137us/sample – loss: 16115.8319 – mean_absolute_error: 16115.8301 – val_loss: 20069.2838 – val_mean_absolute_error: 20069.2832
Epoch 234/500
1024/1168 [=========================>….] – ETA: 0s – loss: 15169.1620 – mean_absolute_error: 15169.1621
Epoch 00234: val_loss did not improve from 20069.28385
1168/1168 [==============================] – 0s 128us/sample – loss: 15566.3287 – mean_absolute_error: 15566.3301 – val_loss: 20652.9901 – val_mean_absolute_error: 20652.9902
Epoch 235/500
1024/1168 [=========================>….] – ETA: 0s – loss: 16428.6206 – mean_absolute_error: 16428.6211
Epoch 00235: val_loss did not improve from 20069.28385
1168/1168 [==============================] – 0s 191us/sample – loss: 16407.7068 – mean_absolute_error: 16407.7070 – val_loss: 24597.7613 – val_mean_absolute_error: 24597.7598
Epoch 236/500
1088/1168 [==========================>…] – ETA: 0s – loss: 18405.6732 – mean_absolute_error: 18405.6719
Epoch 00236: val_loss did not improve from 20069.28385
1168/1168 [==============================] – 0s 239us/sample – loss: 18258.9174 – mean_absolute_error: 18258.9180 – val_loss: 22148.2448 – val_mean_absolute_error: 22148.2441
Epoch 237/500
800/1168 [===================>……….] – ETA: 0s – loss: 17720.6498 – mean_absolute_error: 17720.6504
Epoch 00237: val_loss did not improve from 20069.28385
1168/1168 [==============================] – 0s 231us/sample – loss: 17658.5994 – mean_absolute_error: 17658.5996 – val_loss: 24697.9895 – val_mean_absolute_error: 24697.9902
Epoch 238/500
832/1168 [====================>………] – ETA: 0s – loss: 16765.4304 – mean_absolute_error: 16765.4297
Epoch 00238: val_loss did not improve from 20069.28385
1168/1168 [==============================] – 0s 150us/sample – loss: 16537.9248 – mean_absolute_error: 16537.9238 – val_loss: 21444.8503 – val_mean_absolute_error: 21444.8516
Epoch 239/500
800/1168 [===================>……….] – ETA: 0s – loss: 16637.2343 – mean_absolute_error: 16637.2344
Epoch 00239: val_loss did not improve from 20069.28385
1168/1168 [==============================] – 0s 207us/sample – loss: 18154.8612 – mean_absolute_error: 18154.8613 – val_loss: 23119.1866 – val_mean_absolute_error: 23119.1875
Epoch 240/500
800/1168 [===================>……….] – ETA: 0s – loss: 18242.5641 – mean_absolute_error: 18242.5645
Epoch 00240: val_loss did not improve from 20069.28385
1168/1168 [==============================] – 0s 148us/sample – loss: 18331.9750 – mean_absolute_error: 18331.9727 – val_loss: 20356.7376 – val_mean_absolute_error: 20356.7383
Epoch 241/500
992/1168 [========================>…..] – ETA: 0s – loss: 18108.0380 – mean_absolute_error: 18108.0391- ETA: 0s – loss: 17735.0366 – mean_absolute_error: 17735.037
Epoch 00241: val_loss did not improve from 20069.28385
1168/1168 [==============================] – 0s 131us/sample – loss: 17796.6541 – mean_absolute_error: 17796.6562 – val_loss: 21359.1164 – val_mean_absolute_error: 21359.1152
Epoch 242/500
896/1168 [======================>…….] – ETA: 0s – loss: 17424.8707 – mean_absolute_error: 17424.8711
Epoch 00242: val_loss did not improve from 20069.28385
1168/1168 [==============================] – 0s 138us/sample – loss: 17077.0787 – mean_absolute_error: 17077.0762 – val_loss: 21891.9460 – val_mean_absolute_error: 21891.9453
Epoch 243/500
1152/1168 [============================>.] – ETA: 0s – loss: 17554.0576 – mean_absolute_error: 17554.0586
Epoch 00243: val_loss did not improve from 20069.28385
1168/1168 [==============================] – 0s 161us/sample – loss: 17544.3392 – mean_absolute_error: 17544.3398 – val_loss: 23006.5459 – val_mean_absolute_error: 23006.5469
Epoch 244/500
864/1168 [=====================>……..] – ETA: 0s – loss: 16692.5310 – mean_absolute_error: 16692.5332
Epoch 00244: val_loss did not improve from 20069.28385
1168/1168 [==============================] – 0s 143us/sample – loss: 16929.2249 – mean_absolute_error: 16929.2285 – val_loss: 22671.9738 – val_mean_absolute_error: 22671.9746
Epoch 245/500
1152/1168 [============================>.] – ETA: 0s – loss: 17243.0515 – mean_absolute_error: 17243.0488
Epoch 00245: val_loss did not improve from 20069.28385
1168/1168 [==============================] – 0s 168us/sample – loss: 17170.6751 – mean_absolute_error: 17170.6738 – val_loss: 20381.8023 – val_mean_absolute_error: 20381.8008
Epoch 246/500
704/1168 [=================>…………] – ETA: 0s – loss: 16236.5425 – mean_absolute_error: 16236.5439
Epoch 00246: val_loss did not improve from 20069.28385
1168/1168 [==============================] – 0s 152us/sample – loss: 16982.0901 – mean_absolute_error: 16982.0918 – val_loss: 24780.7623 – val_mean_absolute_error: 24780.7617
Epoch 247/500
928/1168 [======================>…….] – ETA: 0s – loss: 20228.8479 – mean_absolute_error: 20228.8496
Epoch 00247: val_loss did not improve from 20069.28385
1168/1168 [==============================] – 0s 141us/sample – loss: 19864.9892 – mean_absolute_error: 19864.9883 – val_loss: 21974.0476 – val_mean_absolute_error: 21974.0469
Epoch 248/500
864/1168 [=====================>……..] – ETA: 0s – loss: 16036.1611 – mean_absolute_error: 16036.1602
Epoch 00248: val_loss did not improve from 20069.28385
1168/1168 [==============================] – 0s 138us/sample – loss: 16379.2478 – mean_absolute_error: 16379.2461 – val_loss: 21433.2928 – val_mean_absolute_error: 21433.2930
Epoch 249/500
800/1168 [===================>……….] – ETA: 0s – loss: 16518.3551 – mean_absolute_error: 16518.3555
Epoch 00249: val_loss improved from 20069.28385 to 19552.26584, saving model to Models/Weights-249–19552.26584.hdf5
1168/1168 [==============================] – 0s 173us/sample – loss: 16466.6442 – mean_absolute_error: 16466.6445 – val_loss: 19552.2658 – val_mean_absolute_error: 19552.2676
Epoch 250/500
960/1168 [=======================>……] – ETA: 0s – loss: 15940.9112 – mean_absolute_error: 15940.9102
Epoch 00250: val_loss did not improve from 19552.26584
1168/1168 [==============================] – 0s 137us/sample – loss: 15824.7580 – mean_absolute_error: 15824.7588 – val_loss: 22135.3169 – val_mean_absolute_error: 22135.3164
Epoch 251/500
768/1168 [==================>………..] – ETA: 0s – loss: 17763.6964 – mean_absolute_error: 17763.6953
Epoch 00251: val_loss did not improve from 19552.26584
1168/1168 [==============================] – 0s 151us/sample – loss: 17791.5399 – mean_absolute_error: 17791.5391 – val_loss: 21760.9916 – val_mean_absolute_error: 21760.9922
Epoch 252/500
928/1168 [======================>…….] – ETA: 0s – loss: 17704.3297 – mean_absolute_error: 17704.3281
Epoch 00252: val_loss did not improve from 19552.26584
1168/1168 [==============================] – 0s 137us/sample – loss: 17444.1040 – mean_absolute_error: 17444.1035 – val_loss: 21660.3052 – val_mean_absolute_error: 21660.3047
Epoch 253/500
928/1168 [======================>…….] – ETA: 0s – loss: 16472.4878 – mean_absolute_error: 16472.4863
Epoch 00253: val_loss did not improve from 19552.26584
1168/1168 [==============================] – 0s 136us/sample – loss: 16498.5621 – mean_absolute_error: 16498.5625 – val_loss: 20606.9024 – val_mean_absolute_error: 20606.9023
Epoch 254/500
928/1168 [======================>…….] – ETA: 0s – loss: 15476.8300 – mean_absolute_error: 15476.8311
Epoch 00254: val_loss did not improve from 19552.26584
1168/1168 [==============================] – 0s 129us/sample – loss: 15906.7527 – mean_absolute_error: 15906.7539 – val_loss: 22189.1129 – val_mean_absolute_error: 22189.1133
Epoch 255/500
1024/1168 [=========================>….] – ETA: 0s – loss: 16763.3632 – mean_absolute_error: 16763.3613
Epoch 00255: val_loss did not improve from 19552.26584
1168/1168 [==============================] – 0s 128us/sample – loss: 16903.6495 – mean_absolute_error: 16903.6484 – val_loss: 20568.3010 – val_mean_absolute_error: 20568.3008
Epoch 256/500
1152/1168 [============================>.] – ETA: 0s – loss: 16499.8887 – mean_absolute_error: 16499.8887
Epoch 00256: val_loss did not improve from 19552.26584
1168/1168 [==============================] – 0s 182us/sample – loss: 16631.3987 – mean_absolute_error: 16631.3984 – val_loss: 22989.8949 – val_mean_absolute_error: 22989.8945
Epoch 257/500
864/1168 [=====================>……..] – ETA: 0s – loss: 15998.1697 – mean_absolute_error: 15998.1689
Epoch 00257: val_loss did not improve from 19552.26584
1168/1168 [==============================] – 0s 157us/sample – loss: 16307.4438 – mean_absolute_error: 16307.4414 – val_loss: 20928.1759 – val_mean_absolute_error: 20928.1758
Epoch 258/500
960/1168 [=======================>……] – ETA: 0s – loss: 16818.2880 – mean_absolute_error: 16818.2891
Epoch 00258: val_loss did not improve from 19552.26584
1168/1168 [==============================] – 0s 199us/sample – loss: 16845.8988 – mean_absolute_error: 16845.8984 – val_loss: 22740.4519 – val_mean_absolute_error: 22740.4531
Epoch 259/500
1088/1168 [==========================>…] – ETA: 0s – loss: 17640.1318 – mean_absolute_error: 17640.1328
Epoch 00259: val_loss did not improve from 19552.26584
1168/1168 [==============================] – 0s 185us/sample – loss: 17930.7688 – mean_absolute_error: 17930.7715 – val_loss: 28538.2329 – val_mean_absolute_error: 28538.2344
Epoch 260/500
992/1168 [========================>…..] – ETA: 0s – loss: 19765.2004 – mean_absolute_error: 19765.2012
Epoch 00260: val_loss did not improve from 19552.26584
1168/1168 [==============================] – 0s 148us/sample – loss: 19585.5472 – mean_absolute_error: 19585.5469 – val_loss: 21866.5651 – val_mean_absolute_error: 21866.5645
Epoch 261/500
1152/1168 [============================>.] – ETA: 0s – loss: 17472.3678 – mean_absolute_error: 17472.3672
Epoch 00261: val_loss did not improve from 19552.26584
1168/1168 [==============================] – 0s 229us/sample – loss: 17459.4199 – mean_absolute_error: 17459.4199 – val_loss: 20352.7615 – val_mean_absolute_error: 20352.7617
Epoch 262/500
928/1168 [======================>…….] – ETA: 0s – loss: 16128.1924 – mean_absolute_error: 16128.1904
Epoch 00262: val_loss did not improve from 19552.26584
1168/1168 [==============================] – 0s 186us/sample – loss: 16427.3798 – mean_absolute_error: 16427.3789 – val_loss: 20527.0056 – val_mean_absolute_error: 20527.0078
Epoch 263/500
1152/1168 [============================>.] – ETA: 0s – loss: 17079.2316 – mean_absolute_error: 17079.2305
Epoch 00263: val_loss did not improve from 19552.26584
1168/1168 [==============================] – 0s 167us/sample – loss: 17050.4989 – mean_absolute_error: 17050.4980 – val_loss: 21765.3273 – val_mean_absolute_error: 21765.3262
Epoch 264/500
864/1168 [=====================>……..] – ETA: 0s – loss: 16896.7051 – mean_absolute_error: 16896.7051
Epoch 00264: val_loss did not improve from 19552.26584
1168/1168 [==============================] – 0s 196us/sample – loss: 16548.0403 – mean_absolute_error: 16548.0391 – val_loss: 19612.0594 – val_mean_absolute_error: 19612.0605
Epoch 265/500
800/1168 [===================>……….] – ETA: 0s – loss: 16664.9939 – mean_absolute_error: 16664.9922
Epoch 00265: val_loss did not improve from 19552.26584
1168/1168 [==============================] – 0s 153us/sample – loss: 17149.8533 – mean_absolute_error: 17149.8516 – val_loss: 21726.2985 – val_mean_absolute_error: 21726.2988
Epoch 266/500
1152/1168 [============================>.] – ETA: 0s – loss: 16223.9035 – mean_absolute_error: 16223.9023
Epoch 00266: val_loss did not improve from 19552.26584
1168/1168 [==============================] – 0s 165us/sample – loss: 16172.4513 – mean_absolute_error: 16172.4502 – val_loss: 21069.7747 – val_mean_absolute_error: 21069.7734
Epoch 267/500
928/1168 [======================>…….] – ETA: 0s – loss: 15232.5469 – mean_absolute_error: 15232.5488
Epoch 00267: val_loss did not improve from 19552.26584
1168/1168 [==============================] – 0s 136us/sample – loss: 15366.1795 – mean_absolute_error: 15366.1816 – val_loss: 21146.0879 – val_mean_absolute_error: 21146.0879
Epoch 268/500
1152/1168 [============================>.] – ETA: 0s – loss: 16732.4875 – mean_absolute_error: 16732.4883
Epoch 00268: val_loss improved from 19552.26584 to 19021.54955, saving model to Models/Weights-268–19021.54955.hdf5
1168/1168 [==============================] – 0s 190us/sample – loss: 16639.0665 – mean_absolute_error: 16639.0664 – val_loss: 19021.5496 – val_mean_absolute_error: 19021.5488
Epoch 269/500
992/1168 [========================>…..] – ETA: 0s – loss: 16030.3541 – mean_absolute_error: 16030.3506
Epoch 00269: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 125us/sample – loss: 15836.1735 – mean_absolute_error: 15836.1699 – val_loss: 22087.7949 – val_mean_absolute_error: 22087.7949
Epoch 270/500
992/1168 [========================>…..] – ETA: 0s – loss: 16020.0936 – mean_absolute_error: 16020.0938
Epoch 00270: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 137us/sample – loss: 16012.6197 – mean_absolute_error: 16012.6201 – val_loss: 20154.3281 – val_mean_absolute_error: 20154.3262
Epoch 271/500
928/1168 [======================>…….] – ETA: 0s – loss: 17051.5984 – mean_absolute_error: 17051.5996
Epoch 00271: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 128us/sample – loss: 17127.2782 – mean_absolute_error: 17127.2773 – val_loss: 21760.3748 – val_mean_absolute_error: 21760.3750
Epoch 272/500
1024/1168 [=========================>….] – ETA: 0s – loss: 15667.7315 – mean_absolute_error: 15667.7324
Epoch 00272: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 125us/sample – loss: 15673.0947 – mean_absolute_error: 15673.0938 – val_loss: 20548.7428 – val_mean_absolute_error: 20548.7422
Epoch 273/500
960/1168 [=======================>……] – ETA: 0s – loss: 15879.2524 – mean_absolute_error: 15879.2520
Epoch 00273: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 130us/sample – loss: 15630.3344 – mean_absolute_error: 15630.3340 – val_loss: 22869.2197 – val_mean_absolute_error: 22869.2207
Epoch 274/500
768/1168 [==================>………..] – ETA: 0s – loss: 15827.6042 – mean_absolute_error: 15827.6045
Epoch 00274: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 153us/sample – loss: 15755.3905 – mean_absolute_error: 15755.3926 – val_loss: 20715.8464 – val_mean_absolute_error: 20715.8438
Epoch 275/500
800/1168 [===================>……….] – ETA: 0s – loss: 16387.5168 – mean_absolute_error: 16387.5176
Epoch 00275: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 145us/sample – loss: 16109.3658 – mean_absolute_error: 16109.3643 – val_loss: 19735.8547 – val_mean_absolute_error: 19735.8555
Epoch 276/500
1056/1168 [==========================>…] – ETA: 0s – loss: 16119.5435 – mean_absolute_error: 16119.5439
Epoch 00276: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 120us/sample – loss: 15931.0402 – mean_absolute_error: 15931.0410 – val_loss: 25450.9626 – val_mean_absolute_error: 25450.9629
Epoch 277/500
896/1168 [======================>…….] – ETA: 0s – loss: 19557.3858 – mean_absolute_error: 19557.3867
Epoch 00277: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 134us/sample – loss: 19044.3402 – mean_absolute_error: 19044.3398 – val_loss: 26165.6626 – val_mean_absolute_error: 26165.6602
Epoch 278/500
1056/1168 [==========================>…] – ETA: 0s – loss: 16900.1645 – mean_absolute_error: 16900.1641
Epoch 00278: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 125us/sample – loss: 16811.5604 – mean_absolute_error: 16811.5625 – val_loss: 24107.0167 – val_mean_absolute_error: 24107.0156
Epoch 279/500
1024/1168 [=========================>….] – ETA: 0s – loss: 16891.9062 – mean_absolute_error: 16891.9062
Epoch 00279: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 122us/sample – loss: 16698.9984 – mean_absolute_error: 16698.9980 – val_loss: 20065.1357 – val_mean_absolute_error: 20065.1367
Epoch 280/500
992/1168 [========================>…..] – ETA: 0s – loss: 15571.6316 – mean_absolute_error: 15571.6299
Epoch 00280: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 128us/sample – loss: 15701.6951 – mean_absolute_error: 15701.6934 – val_loss: 19395.5357 – val_mean_absolute_error: 19395.5352
Epoch 281/500
1024/1168 [=========================>….] – ETA: 0s – loss: 16189.1171 – mean_absolute_error: 16189.1201
Epoch 00281: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 125us/sample – loss: 16343.4096 – mean_absolute_error: 16343.4111 – val_loss: 22247.4430 – val_mean_absolute_error: 22247.4434
Epoch 282/500
992/1168 [========================>…..] – ETA: 0s – loss: 15492.6667 – mean_absolute_error: 15492.6670
Epoch 00282: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 125us/sample – loss: 15420.3920 – mean_absolute_error: 15420.3936 – val_loss: 22276.0804 – val_mean_absolute_error: 22276.0801
Epoch 283/500
1056/1168 [==========================>…] – ETA: 0s – loss: 16917.6558 – mean_absolute_error: 16917.6562
Epoch 00283: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 175us/sample – loss: 17146.0998 – mean_absolute_error: 17146.1016 – val_loss: 19553.4938 – val_mean_absolute_error: 19553.4922
Epoch 284/500
928/1168 [======================>…….] – ETA: 0s – loss: 15814.2379 – mean_absolute_error: 15814.2383
Epoch 00284: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 132us/sample – loss: 16255.6788 – mean_absolute_error: 16255.6777 – val_loss: 20169.8747 – val_mean_absolute_error: 20169.8750
Epoch 285/500
800/1168 [===================>……….] – ETA: 0s – loss: 15561.3565 – mean_absolute_error: 15561.3564
Epoch 00285: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 149us/sample – loss: 15750.6733 – mean_absolute_error: 15750.6729 – val_loss: 21262.0163 – val_mean_absolute_error: 21262.0156
Epoch 286/500
928/1168 [======================>…….] – ETA: 0s – loss: 20331.1423 – mean_absolute_error: 20331.1426
Epoch 00286: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 131us/sample – loss: 19537.0691 – mean_absolute_error: 19537.0684 – val_loss: 20746.7298 – val_mean_absolute_error: 20746.7305
Epoch 287/500
896/1168 [======================>…….] – ETA: 0s – loss: 15707.3424 – mean_absolute_error: 15707.3438
Epoch 00287: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 131us/sample – loss: 15712.3728 – mean_absolute_error: 15712.3730 – val_loss: 20555.5917 – val_mean_absolute_error: 20555.5918
Epoch 288/500
1024/1168 [=========================>….] – ETA: 0s – loss: 16422.4984 – mean_absolute_error: 16422.4980
Epoch 00288: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 123us/sample – loss: 16525.8595 – mean_absolute_error: 16525.8594 – val_loss: 23512.1669 – val_mean_absolute_error: 23512.1660
Epoch 289/500
960/1168 [=======================>……] – ETA: 0s – loss: 18270.2671 – mean_absolute_error: 18270.2695
Epoch 00289: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 132us/sample – loss: 18054.7661 – mean_absolute_error: 18054.7676 – val_loss: 20327.7378 – val_mean_absolute_error: 20327.7402
Epoch 290/500
960/1168 [=======================>……] – ETA: 0s – loss: 15998.9400 – mean_absolute_error: 15998.9404
Epoch 00290: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 129us/sample – loss: 15949.9951 – mean_absolute_error: 15949.9951 – val_loss: 24954.3564 – val_mean_absolute_error: 24954.3555
Epoch 291/500
992/1168 [========================>…..] – ETA: 0s – loss: 17184.4082 – mean_absolute_error: 17184.4082
Epoch 00291: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 126us/sample – loss: 17057.5022 – mean_absolute_error: 17057.5039 – val_loss: 20287.4848 – val_mean_absolute_error: 20287.4844
Epoch 292/500
1056/1168 [==========================>…] – ETA: 0s – loss: 15214.2940 – mean_absolute_error: 15214.2930
Epoch 00292: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 121us/sample – loss: 15625.2864 – mean_absolute_error: 15625.2861 – val_loss: 24601.4041 – val_mean_absolute_error: 24601.4043
Epoch 293/500
992/1168 [========================>…..] – ETA: 0s – loss: 16877.8315 – mean_absolute_error: 16877.8320
Epoch 00293: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 124us/sample – loss: 17068.6204 – mean_absolute_error: 17068.6211 – val_loss: 20566.5935 – val_mean_absolute_error: 20566.5938
Epoch 294/500
928/1168 [======================>…….] – ETA: 0s – loss: 16125.0835 – mean_absolute_error: 16125.0840
Epoch 00294: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 130us/sample – loss: 15676.8761 – mean_absolute_error: 15676.8770 – val_loss: 19518.0571 – val_mean_absolute_error: 19518.0566
Epoch 295/500
1088/1168 [==========================>…] – ETA: 0s – loss: 15786.6820 – mean_absolute_error: 15786.6797
Epoch 00295: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 171us/sample – loss: 15789.9397 – mean_absolute_error: 15789.9365 – val_loss: 20805.8538 – val_mean_absolute_error: 20805.8535
Epoch 296/500
1120/1168 [===========================>..] – ETA: 0s – loss: 17059.8597 – mean_absolute_error: 17059.8594
Epoch 00296: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 226us/sample – loss: 16968.3377 – mean_absolute_error: 16968.3359 – val_loss: 19980.0143 – val_mean_absolute_error: 19980.0156
Epoch 297/500
928/1168 [======================>…….] – ETA: 0s – loss: 16853.2219 – mean_absolute_error: 16853.2188
Epoch 00297: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 185us/sample – loss: 16299.5798 – mean_absolute_error: 16299.5771 – val_loss: 20998.8319 – val_mean_absolute_error: 20998.8320
Epoch 298/500
832/1168 [====================>………] – ETA: 0s – loss: 17189.7474 – mean_absolute_error: 17189.7461
Epoch 00298: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 148us/sample – loss: 16547.5464 – mean_absolute_error: 16547.5469 – val_loss: 20500.2505 – val_mean_absolute_error: 20500.2520
Epoch 299/500
800/1168 [===================>……….] – ETA: 0s – loss: 16240.4274 – mean_absolute_error: 16240.4277
Epoch 00299: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 148us/sample – loss: 16497.7649 – mean_absolute_error: 16497.7676 – val_loss: 19426.1331 – val_mean_absolute_error: 19426.1348
Epoch 300/500
1120/1168 [===========================>..] – ETA: 0s – loss: 18564.7002 – mean_absolute_error: 18564.6973
Epoch 00300: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 168us/sample – loss: 18353.6405 – mean_absolute_error: 18353.6387 – val_loss: 23184.6914 – val_mean_absolute_error: 23184.6895
Epoch 301/500
1056/1168 [==========================>…] – ETA: 0s – loss: 16415.8812 – mean_absolute_error: 16415.8828
Epoch 00301: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 188us/sample – loss: 16182.4559 – mean_absolute_error: 16182.4570 – val_loss: 19928.5236 – val_mean_absolute_error: 19928.5215
Epoch 302/500
864/1168 [=====================>……..] – ETA: 0s – loss: 14938.4753 – mean_absolute_error: 14938.4756
Epoch 00302: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 217us/sample – loss: 14988.5488 – mean_absolute_error: 14988.5498 – val_loss: 22334.1454 – val_mean_absolute_error: 22334.1445
Epoch 303/500
832/1168 [====================>………] – ETA: 0s – loss: 17604.1705 – mean_absolute_error: 17604.1680
Epoch 00303: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 148us/sample – loss: 16694.4279 – mean_absolute_error: 16694.4238 – val_loss: 19945.4006 – val_mean_absolute_error: 19945.3984
Epoch 304/500
1120/1168 [===========================>..] – ETA: 0s – loss: 14578.6728 – mean_absolute_error: 14578.6729
Epoch 00304: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 170us/sample – loss: 14781.0041 – mean_absolute_error: 14781.0049 – val_loss: 21344.4728 – val_mean_absolute_error: 21344.4746
Epoch 305/500
800/1168 [===================>……….] – ETA: 0s – loss: 17946.0412 – mean_absolute_error: 17946.0391
Epoch 00305: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 155us/sample – loss: 17176.5767 – mean_absolute_error: 17176.5762 – val_loss: 19942.2400 – val_mean_absolute_error: 19942.2402
Epoch 306/500
960/1168 [=======================>……] – ETA: 0s – loss: 18263.3584 – mean_absolute_error: 18263.3633
Epoch 00306: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 195us/sample – loss: 17947.7809 – mean_absolute_error: 17947.7832 – val_loss: 22813.3359 – val_mean_absolute_error: 22813.3359
Epoch 307/500
1120/1168 [===========================>..] – ETA: 0s – loss: 18194.0981 – mean_absolute_error: 18194.0977
Epoch 00307: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 171us/sample – loss: 18188.1225 – mean_absolute_error: 18188.1230 – val_loss: 20307.9393 – val_mean_absolute_error: 20307.9375
Epoch 308/500
768/1168 [==================>………..] – ETA: 0s – loss: 16342.9684 – mean_absolute_error: 16342.9678
Epoch 00308: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 161us/sample – loss: 16511.4445 – mean_absolute_error: 16511.4414 – val_loss: 21438.8253 – val_mean_absolute_error: 21438.8262
Epoch 309/500
1088/1168 [==========================>…] – ETA: 0s – loss: 15429.0893 – mean_absolute_error: 15429.0879
Epoch 00309: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 175us/sample – loss: 15511.1876 – mean_absolute_error: 15511.1865 – val_loss: 19409.9737 – val_mean_absolute_error: 19409.9746
Epoch 310/500
800/1168 [===================>……….] – ETA: 0s – loss: 15644.7456 – mean_absolute_error: 15644.7471
Epoch 00310: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 160us/sample – loss: 16401.0288 – mean_absolute_error: 16401.0312 – val_loss: 22178.1154 – val_mean_absolute_error: 22178.1172
Epoch 311/500
800/1168 [===================>……….] – ETA: 0s – loss: 15468.2246 – mean_absolute_error: 15468.2236
Epoch 00311: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 155us/sample – loss: 15445.2091 – mean_absolute_error: 15445.2070 – val_loss: 19642.8854 – val_mean_absolute_error: 19642.8867
Epoch 312/500
896/1168 [======================>…….] – ETA: 0s – loss: 15158.9816 – mean_absolute_error: 15158.9824
Epoch 00312: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 156us/sample – loss: 15648.2909 – mean_absolute_error: 15648.2910 – val_loss: 21732.4877 – val_mean_absolute_error: 21732.4883
Epoch 313/500
960/1168 [=======================>……] – ETA: 0s – loss: 15815.3923 – mean_absolute_error: 15815.3916
Epoch 00313: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 243us/sample – loss: 15752.9024 – mean_absolute_error: 15752.9004 – val_loss: 20260.1375 – val_mean_absolute_error: 20260.1367
Epoch 314/500
1024/1168 [=========================>….] – ETA: 0s – loss: 14915.5204 – mean_absolute_error: 14915.5195
Epoch 00314: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 185us/sample – loss: 14860.5902 – mean_absolute_error: 14860.5889 – val_loss: 20059.6154 – val_mean_absolute_error: 20059.6172
Epoch 315/500
896/1168 [======================>…….] – ETA: 0s – loss: 15606.0799 – mean_absolute_error: 15606.0801
Epoch 00315: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 136us/sample – loss: 15546.2348 – mean_absolute_error: 15546.2363 – val_loss: 22224.4467 – val_mean_absolute_error: 22224.4473
Epoch 316/500
1088/1168 [==========================>…] – ETA: 0s – loss: 15926.3848 – mean_absolute_error: 15926.3838
Epoch 00316: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 120us/sample – loss: 15807.5814 – mean_absolute_error: 15807.5801 – val_loss: 22424.5847 – val_mean_absolute_error: 22424.5840
Epoch 317/500
960/1168 [=======================>……] – ETA: 0s – loss: 17969.3337 – mean_absolute_error: 17969.3340
Epoch 00317: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 132us/sample – loss: 17446.8622 – mean_absolute_error: 17446.8613 – val_loss: 19893.0884 – val_mean_absolute_error: 19893.0879
Epoch 318/500
992/1168 [========================>…..] – ETA: 0s – loss: 16374.7144 – mean_absolute_error: 16374.7129
Epoch 00318: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 131us/sample – loss: 16515.0883 – mean_absolute_error: 16515.0879 – val_loss: 20956.5375 – val_mean_absolute_error: 20956.5371
Epoch 319/500
1024/1168 [=========================>….] – ETA: 0s – loss: 16782.5903 – mean_absolute_error: 16782.5938
Epoch 00319: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 122us/sample – loss: 16235.5993 – mean_absolute_error: 16235.6025 – val_loss: 19953.0642 – val_mean_absolute_error: 19953.0625
Epoch 320/500
896/1168 [======================>…….] – ETA: 0s – loss: 15111.1277 – mean_absolute_error: 15111.1260
Epoch 00320: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 136us/sample – loss: 15035.4058 – mean_absolute_error: 15035.4072 – val_loss: 19988.2789 – val_mean_absolute_error: 19988.2773
Epoch 321/500
992/1168 [========================>…..] – ETA: 0s – loss: 15403.9074 – mean_absolute_error: 15403.9072
Epoch 00321: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 121us/sample – loss: 15615.6131 – mean_absolute_error: 15615.6133 – val_loss: 21855.8030 – val_mean_absolute_error: 21855.8027
Epoch 322/500
1056/1168 [==========================>…] – ETA: 0s – loss: 15255.2090 – mean_absolute_error: 15255.2090
Epoch 00322: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 120us/sample – loss: 15369.9876 – mean_absolute_error: 15369.9883 – val_loss: 21530.8035 – val_mean_absolute_error: 21530.8047
Epoch 323/500
1056/1168 [==========================>…] – ETA: 0s – loss: 14993.1332 – mean_absolute_error: 14993.1338
Epoch 00323: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 120us/sample – loss: 15098.2304 – mean_absolute_error: 15098.2314 – val_loss: 22872.3384 – val_mean_absolute_error: 22872.3379
Epoch 324/500
1024/1168 [=========================>….] – ETA: 0s – loss: 15530.0456 – mean_absolute_error: 15530.0469
Epoch 00324: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 123us/sample – loss: 15529.8799 – mean_absolute_error: 15529.8818 – val_loss: 19881.1419 – val_mean_absolute_error: 19881.1426
Epoch 325/500
1056/1168 [==========================>…] – ETA: 0s – loss: 17534.8034 – mean_absolute_error: 17534.8047
Epoch 00325: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 124us/sample – loss: 17775.5622 – mean_absolute_error: 17775.5625 – val_loss: 24894.0337 – val_mean_absolute_error: 24894.0332
Epoch 326/500
896/1168 [======================>…….] – ETA: 0s – loss: 18753.5224 – mean_absolute_error: 18753.5195
Epoch 00326: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 143us/sample – loss: 17768.1002 – mean_absolute_error: 17768.0977 – val_loss: 25733.6027 – val_mean_absolute_error: 25733.6035
Epoch 327/500
1152/1168 [============================>.] – ETA: 0s – loss: 15787.4358 – mean_absolute_error: 15787.4355
Epoch 00327: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 272us/sample – loss: 15856.7580 – mean_absolute_error: 15856.7588 – val_loss: 19551.8092 – val_mean_absolute_error: 19551.8105
Epoch 328/500
832/1168 [====================>………] – ETA: 0s – loss: 14932.0005 – mean_absolute_error: 14932.0000- ETA: 0s – loss: 14282.0821 – mean_absolute_error: 14282.082
Epoch 00328: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 315us/sample – loss: 14568.4967 – mean_absolute_error: 14568.4961 – val_loss: 21351.7682 – val_mean_absolute_error: 21351.7676
Epoch 329/500
1120/1168 [===========================>..] – ETA: 0s – loss: 14677.3046 – mean_absolute_error: 14677.3037
Epoch 00329: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 180us/sample – loss: 14716.9363 – mean_absolute_error: 14716.9346 – val_loss: 22458.2519 – val_mean_absolute_error: 22458.2520
Epoch 330/500
1120/1168 [===========================>..] – ETA: 0s – loss: 16471.9417 – mean_absolute_error: 16471.9434
Epoch 00330: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 181us/sample – loss: 16400.5851 – mean_absolute_error: 16400.5859 – val_loss: 23558.7308 – val_mean_absolute_error: 23558.7285
Epoch 331/500
1088/1168 [==========================>…] – ETA: 0s – loss: 16488.3830 – mean_absolute_error: 16488.3828
Epoch 00331: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 1s 576us/sample – loss: 16418.9693 – mean_absolute_error: 16418.9668 – val_loss: 23887.5446 – val_mean_absolute_error: 23887.5469
Epoch 332/500
1120/1168 [===========================>..] – ETA: 0s – loss: 17315.4756 – mean_absolute_error: 17315.4746
Epoch 00332: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 1s 634us/sample – loss: 17214.4504 – mean_absolute_error: 17214.4512 – val_loss: 22558.6069 – val_mean_absolute_error: 22558.6055
Epoch 333/500
992/1168 [========================>…..] – ETA: 0s – loss: 15654.2718 – mean_absolute_error: 15654.2734
Epoch 00333: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 309us/sample – loss: 16088.4289 – mean_absolute_error: 16088.4316 – val_loss: 23768.9476 – val_mean_absolute_error: 23768.9453
Epoch 334/500
1088/1168 [==========================>…] – ETA: 0s – loss: 16878.6407 – mean_absolute_error: 16878.6406
Epoch 00334: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 241us/sample – loss: 17006.0741 – mean_absolute_error: 17006.0762 – val_loss: 21168.0973 – val_mean_absolute_error: 21168.0996
Epoch 335/500
928/1168 [======================>…….] – ETA: 0s – loss: 16303.8836 – mean_absolute_error: 16303.883 – ETA: 0s – loss: 15356.5684 – mean_absolute_error: 15356.5684
Epoch 00335: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 205us/sample – loss: 15413.3890 – mean_absolute_error: 15413.3867 – val_loss: 23183.8555 – val_mean_absolute_error: 23183.8555
Epoch 336/500
992/1168 [========================>…..] – ETA: 0s – loss: 15133.5436 – mean_absolute_error: 15133.5430
Epoch 00336: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 197us/sample – loss: 14925.7137 – mean_absolute_error: 14925.7119 – val_loss: 21094.3756 – val_mean_absolute_error: 21094.3770
Epoch 337/500
832/1168 [====================>………] – ETA: 0s – loss: 16569.6904 – mean_absolute_error: 16569.6875
Epoch 00337: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 152us/sample – loss: 16936.7284 – mean_absolute_error: 16936.7266 – val_loss: 20448.4828 – val_mean_absolute_error: 20448.4824
Epoch 338/500
832/1168 [====================>………] – ETA: 0s – loss: 15928.8205 – mean_absolute_error: 15928.8193
Epoch 00338: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 155us/sample – loss: 15865.5481 – mean_absolute_error: 15865.5459 – val_loss: 23073.1254 – val_mean_absolute_error: 23073.1250
Epoch 339/500
832/1168 [====================>………] – ETA: 0s – loss: 16937.4749 – mean_absolute_error: 16937.4746
Epoch 00339: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 145us/sample – loss: 16739.6268 – mean_absolute_error: 16739.6270 – val_loss: 20120.0441 – val_mean_absolute_error: 20120.0469
Epoch 340/500
864/1168 [=====================>……..] – ETA: 0s – loss: 15475.4761 – mean_absolute_error: 15475.4746
Epoch 00340: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 154us/sample – loss: 15949.6367 – mean_absolute_error: 15949.6338 – val_loss: 26906.8640 – val_mean_absolute_error: 26906.8633
Epoch 341/500
864/1168 [=====================>……..] – ETA: 0s – loss: 19513.4959 – mean_absolute_error: 19513.4980
Epoch 00341: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 149us/sample – loss: 18729.9658 – mean_absolute_error: 18729.9688 – val_loss: 22793.9219 – val_mean_absolute_error: 22793.9219
Epoch 342/500
928/1168 [======================>…….] – ETA: 0s – loss: 17928.8845 – mean_absolute_error: 17928.8867
Epoch 00342: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 188us/sample – loss: 17457.6031 – mean_absolute_error: 17457.6055 – val_loss: 21034.4485 – val_mean_absolute_error: 21034.4512
Epoch 343/500
960/1168 [=======================>……] – ETA: 0s – loss: 17394.1881 – mean_absolute_error: 17394.1895
Epoch 00343: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 137us/sample – loss: 17011.6332 – mean_absolute_error: 17011.6328 – val_loss: 20211.6056 – val_mean_absolute_error: 20211.6035
Epoch 344/500
928/1168 [======================>…….] – ETA: 0s – loss: 14964.8713 – mean_absolute_error: 14964.8691
Epoch 00344: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 137us/sample – loss: 15106.4549 – mean_absolute_error: 15106.4541 – val_loss: 21448.1360 – val_mean_absolute_error: 21448.1348
Epoch 345/500
960/1168 [=======================>……] – ETA: 0s – loss: 15253.5210 – mean_absolute_error: 15253.5195
Epoch 00345: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 129us/sample – loss: 15161.0075 – mean_absolute_error: 15161.0068 – val_loss: 20558.7120 – val_mean_absolute_error: 20558.7129
Epoch 346/500
896/1168 [======================>…….] – ETA: 0s – loss: 14714.8508 – mean_absolute_error: 14714.8486
Epoch 00346: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 131us/sample – loss: 14758.7189 – mean_absolute_error: 14758.7178 – val_loss: 22725.4251 – val_mean_absolute_error: 22725.4258
Epoch 347/500
1024/1168 [=========================>….] – ETA: 0s – loss: 15348.0690 – mean_absolute_error: 15348.0674
Epoch 00347: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 121us/sample – loss: 15661.5295 – mean_absolute_error: 15661.5273 – val_loss: 24563.7273 – val_mean_absolute_error: 24563.7285
Epoch 348/500
992/1168 [========================>…..] – ETA: 0s – loss: 16913.2380 – mean_absolute_error: 16913.2383
Epoch 00348: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 125us/sample – loss: 16903.9252 – mean_absolute_error: 16903.9238 – val_loss: 20011.8382 – val_mean_absolute_error: 20011.8379
Epoch 349/500
864/1168 [=====================>……..] – ETA: 0s – loss: 14166.5973 – mean_absolute_error: 14166.5996
Epoch 00349: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 137us/sample – loss: 15007.3905 – mean_absolute_error: 15007.3926 – val_loss: 29576.9707 – val_mean_absolute_error: 29576.9688
Epoch 350/500
992/1168 [========================>…..] – ETA: 0s – loss: 16801.2579 – mean_absolute_error: 16801.2578
Epoch 00350: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 124us/sample – loss: 16801.5092 – mean_absolute_error: 16801.5078 – val_loss: 22118.7928 – val_mean_absolute_error: 22118.7930
Epoch 351/500
1024/1168 [=========================>….] – ETA: 0s – loss: 15724.9623 – mean_absolute_error: 15724.9629
Epoch 00351: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 132us/sample – loss: 15914.2795 – mean_absolute_error: 15914.2793 – val_loss: 23899.2353 – val_mean_absolute_error: 23899.2344
Epoch 352/500
896/1168 [======================>…….] – ETA: 0s – loss: 16033.9812 – mean_absolute_error: 16033.9785
Epoch 00352: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 132us/sample – loss: 15926.6021 – mean_absolute_error: 15926.5996 – val_loss: 19911.0517 – val_mean_absolute_error: 19911.0527
Epoch 353/500
832/1168 [====================>………] – ETA: 0s – loss: 15163.5993 – mean_absolute_error: 15163.5996
Epoch 00353: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 140us/sample – loss: 15146.3862 – mean_absolute_error: 15146.3887 – val_loss: 21056.5532 – val_mean_absolute_error: 21056.5527
Epoch 354/500
1152/1168 [============================>.] – ETA: 0s – loss: 14828.3260 – mean_absolute_error: 14828.3262
Epoch 00354: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 156us/sample – loss: 14749.9822 – mean_absolute_error: 14749.9824 – val_loss: 19807.2181 – val_mean_absolute_error: 19807.2188
Epoch 355/500
992/1168 [========================>…..] – ETA: 0s – loss: 14456.4809 – mean_absolute_error: 14456.4805
Epoch 00355: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 153us/sample – loss: 14302.0524 – mean_absolute_error: 14302.0518 – val_loss: 20872.8785 – val_mean_absolute_error: 20872.8809
Epoch 356/500
1024/1168 [=========================>….] – ETA: 0s – loss: 15089.3184 – mean_absolute_error: 15089.3193
Epoch 00356: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 126us/sample – loss: 15210.0742 – mean_absolute_error: 15210.0771 – val_loss: 21819.4739 – val_mean_absolute_error: 21819.4746
Epoch 357/500
960/1168 [=======================>……] – ETA: 0s – loss: 15713.8789 – mean_absolute_error: 15713.8799
Epoch 00357: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 131us/sample – loss: 16825.7533 – mean_absolute_error: 16825.7578 – val_loss: 20172.3342 – val_mean_absolute_error: 20172.3359
Epoch 358/500
1056/1168 [==========================>…] – ETA: 0s – loss: 17026.9816 – mean_absolute_error: 17026.9805
Epoch 00358: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 120us/sample – loss: 16556.3900 – mean_absolute_error: 16556.3906 – val_loss: 19810.5738 – val_mean_absolute_error: 19810.5762
Epoch 359/500
960/1168 [=======================>……] – ETA: 0s – loss: 14319.6381 – mean_absolute_error: 14319.6396
Epoch 00359: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 134us/sample – loss: 14379.2859 – mean_absolute_error: 14379.2881 – val_loss: 22097.9127 – val_mean_absolute_error: 22097.9121
Epoch 360/500
1024/1168 [=========================>….] – ETA: 0s – loss: 15120.0550 – mean_absolute_error: 15120.0557
Epoch 00360: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 124us/sample – loss: 14925.5874 – mean_absolute_error: 14925.5869 – val_loss: 20549.7819 – val_mean_absolute_error: 20549.7852
Epoch 361/500
1088/1168 [==========================>…] – ETA: 0s – loss: 15417.0240 – mean_absolute_error: 15417.0254
Epoch 00361: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 119us/sample – loss: 15751.6813 – mean_absolute_error: 15751.6816 – val_loss: 21726.1744 – val_mean_absolute_error: 21726.1738
Epoch 362/500
960/1168 [=======================>……] – ETA: 0s – loss: 15348.6249 – mean_absolute_error: 15348.6240
Epoch 00362: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 128us/sample – loss: 15134.3548 – mean_absolute_error: 15134.3545 – val_loss: 20437.9143 – val_mean_absolute_error: 20437.9160
Epoch 363/500
992/1168 [========================>…..] – ETA: 0s – loss: 14299.0334 – mean_absolute_error: 14299.0332
Epoch 00363: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 124us/sample – loss: 14921.6041 – mean_absolute_error: 14921.6045 – val_loss: 20761.9821 – val_mean_absolute_error: 20761.9824
Epoch 364/500
992/1168 [========================>…..] – ETA: 0s – loss: 16006.2771 – mean_absolute_error: 16006.2783
Epoch 00364: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 126us/sample – loss: 15768.9410 – mean_absolute_error: 15768.9404 – val_loss: 20392.5676 – val_mean_absolute_error: 20392.5664
Epoch 365/500
960/1168 [=======================>……] – ETA: 0s – loss: 14123.5484 – mean_absolute_error: 14123.5508
Epoch 00365: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 132us/sample – loss: 14802.2682 – mean_absolute_error: 14802.2705 – val_loss: 20529.9502 – val_mean_absolute_error: 20529.9512
Epoch 366/500
992/1168 [========================>…..] – ETA: 0s – loss: 15388.4183 – mean_absolute_error: 15388.4199
Epoch 00366: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 128us/sample – loss: 15316.6379 – mean_absolute_error: 15316.6367 – val_loss: 20741.1024 – val_mean_absolute_error: 20741.1035
Epoch 367/500
1024/1168 [=========================>….] – ETA: 0s – loss: 14909.6963 – mean_absolute_error: 14909.6963
Epoch 00367: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 123us/sample – loss: 14780.0674 – mean_absolute_error: 14780.0654 – val_loss: 20313.8469 – val_mean_absolute_error: 20313.8477
Epoch 368/500
960/1168 [=======================>……] – ETA: 0s – loss: 14881.7781 – mean_absolute_error: 14881.7793
Epoch 00368: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 128us/sample – loss: 14830.6807 – mean_absolute_error: 14830.6797 – val_loss: 21033.6005 – val_mean_absolute_error: 21033.6016
Epoch 369/500
960/1168 [=======================>……] – ETA: 0s – loss: 17600.7660 – mean_absolute_error: 17600.7676
Epoch 00369: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 129us/sample – loss: 17795.1072 – mean_absolute_error: 17795.1074 – val_loss: 21423.5371 – val_mean_absolute_error: 21423.5371
Epoch 370/500
992/1168 [========================>…..] – ETA: 0s – loss: 14250.9241 – mean_absolute_error: 14250.9238
Epoch 00370: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 126us/sample – loss: 14599.1867 – mean_absolute_error: 14599.1846 – val_loss: 19927.1830 – val_mean_absolute_error: 19927.1836
Epoch 371/500
896/1168 [======================>…….] – ETA: 0s – loss: 15492.9236 – mean_absolute_error: 15492.9219
Epoch 00371: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 131us/sample – loss: 16391.2059 – mean_absolute_error: 16391.2051 – val_loss: 22520.9067 – val_mean_absolute_error: 22520.9043
Epoch 372/500
864/1168 [=====================>……..] – ETA: 0s – loss: 17264.2965 – mean_absolute_error: 17264.2949
Epoch 00372: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 154us/sample – loss: 17104.2285 – mean_absolute_error: 17104.2285 – val_loss: 21141.4331 – val_mean_absolute_error: 21141.4336
Epoch 373/500
992/1168 [========================>…..] – ETA: 0s – loss: 18492.4579 – mean_absolute_error: 18492.4609
Epoch 00373: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 124us/sample – loss: 18108.3498 – mean_absolute_error: 18108.3535 – val_loss: 21293.6901 – val_mean_absolute_error: 21293.6875
Epoch 374/500
1056/1168 [==========================>…] – ETA: 0s – loss: 17528.1077 – mean_absolute_error: 17528.1035
Epoch 00374: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 120us/sample – loss: 17517.1538 – mean_absolute_error: 17517.1523 – val_loss: 21316.8897 – val_mean_absolute_error: 21316.8887
Epoch 375/500
1056/1168 [==========================>…] – ETA: 0s – loss: 16033.0815 – mean_absolute_error: 16033.0811
Epoch 00375: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 120us/sample – loss: 16054.4688 – mean_absolute_error: 16054.4678 – val_loss: 21928.1924 – val_mean_absolute_error: 21928.1914
Epoch 376/500
1024/1168 [=========================>….] – ETA: 0s – loss: 15087.9535 – mean_absolute_error: 15087.9531
Epoch 00376: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 126us/sample – loss: 14854.2329 – mean_absolute_error: 14854.2324 – val_loss: 20041.3304 – val_mean_absolute_error: 20041.3301
Epoch 377/500
960/1168 [=======================>……] – ETA: 0s – loss: 14841.4031 – mean_absolute_error: 14841.4043
Epoch 00377: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 135us/sample – loss: 15246.6847 – mean_absolute_error: 15246.6846 – val_loss: 21578.8753 – val_mean_absolute_error: 21578.8750
Epoch 378/500
1024/1168 [=========================>….] – ETA: 0s – loss: 17025.7240 – mean_absolute_error: 17025.7246
Epoch 00378: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 120us/sample – loss: 16824.6958 – mean_absolute_error: 16824.6953 – val_loss: 21896.2646 – val_mean_absolute_error: 21896.2637
Epoch 379/500
928/1168 [======================>…….] – ETA: 0s – loss: 15030.1840 – mean_absolute_error: 15030.1836
Epoch 00379: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 131us/sample – loss: 14902.8983 – mean_absolute_error: 14902.8975 – val_loss: 21383.7381 – val_mean_absolute_error: 21383.7363
Epoch 380/500
1056/1168 [==========================>…] – ETA: 0s – loss: 14267.6779 – mean_absolute_error: 14267.6787
Epoch 00380: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 121us/sample – loss: 14164.1220 – mean_absolute_error: 14164.1221 – val_loss: 20567.6058 – val_mean_absolute_error: 20567.6035
Epoch 381/500
1024/1168 [=========================>….] – ETA: 0s – loss: 14422.5734 – mean_absolute_error: 14422.5732
Epoch 00381: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 121us/sample – loss: 14835.5396 – mean_absolute_error: 14835.5391 – val_loss: 22170.4826 – val_mean_absolute_error: 22170.4824
Epoch 382/500
992/1168 [========================>…..] – ETA: 0s – loss: 14527.4846 – mean_absolute_error: 14527.4873
Epoch 00382: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 125us/sample – loss: 14478.3695 – mean_absolute_error: 14478.3701 – val_loss: 22242.7331 – val_mean_absolute_error: 22242.7324
Epoch 383/500
1024/1168 [=========================>….] – ETA: 0s – loss: 14763.3050 – mean_absolute_error: 14763.3066
Epoch 00383: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 125us/sample – loss: 14903.3884 – mean_absolute_error: 14903.3906 – val_loss: 22794.2194 – val_mean_absolute_error: 22794.2188
Epoch 384/500
960/1168 [=======================>……] – ETA: 0s – loss: 15809.1714 – mean_absolute_error: 15809.1719
Epoch 00384: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 128us/sample – loss: 15463.8322 – mean_absolute_error: 15463.8320 – val_loss: 21620.9754 – val_mean_absolute_error: 21620.9766
Epoch 385/500
1056/1168 [==========================>…] – ETA: 0s – loss: 14766.5511 – mean_absolute_error: 14766.5498
Epoch 00385: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 123us/sample – loss: 14759.4205 – mean_absolute_error: 14759.4209 – val_loss: 20690.8033 – val_mean_absolute_error: 20690.8047
Epoch 386/500
1024/1168 [=========================>….] – ETA: 0s – loss: 15445.5867 – mean_absolute_error: 15445.5879
Epoch 00386: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 125us/sample – loss: 15251.1144 – mean_absolute_error: 15251.1143 – val_loss: 20205.1397 – val_mean_absolute_error: 20205.1406
Epoch 387/500
960/1168 [=======================>……] – ETA: 0s – loss: 14196.3321 – mean_absolute_error: 14196.3301
Epoch 00387: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 128us/sample – loss: 14442.8795 – mean_absolute_error: 14442.8789 – val_loss: 20679.8848 – val_mean_absolute_error: 20679.8828
Epoch 388/500
1088/1168 [==========================>…] – ETA: 0s – loss: 17457.7319 – mean_absolute_error: 17457.7305
Epoch 00388: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 119us/sample – loss: 17358.3350 – mean_absolute_error: 17358.3340 – val_loss: 20816.9597 – val_mean_absolute_error: 20816.9590
Epoch 389/500
864/1168 [=====================>……..] – ETA: 0s – loss: 13980.9396 – mean_absolute_error: 13980.9375
Epoch 00389: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 137us/sample – loss: 14576.8045 – mean_absolute_error: 14576.8027 – val_loss: 20469.2269 – val_mean_absolute_error: 20469.2285
Epoch 390/500
1056/1168 [==========================>…] – ETA: 0s – loss: 14387.4011 – mean_absolute_error: 14387.4014
Epoch 00390: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 123us/sample – loss: 14379.8189 – mean_absolute_error: 14379.8203 – val_loss: 19796.8717 – val_mean_absolute_error: 19796.8691
Epoch 391/500
896/1168 [======================>…….] – ETA: 0s – loss: 14762.0925 – mean_absolute_error: 14762.0918
Epoch 00391: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 134us/sample – loss: 15133.1830 – mean_absolute_error: 15133.1816 – val_loss: 21783.0958 – val_mean_absolute_error: 21783.0957
Epoch 392/500
928/1168 [======================>…….] – ETA: 0s – loss: 15247.6208 – mean_absolute_error: 15247.6211
Epoch 00392: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 149us/sample – loss: 16160.4541 – mean_absolute_error: 16160.4541 – val_loss: 20947.2779 – val_mean_absolute_error: 20947.2773
Epoch 393/500
896/1168 [======================>…….] – ETA: 0s – loss: 15614.9150 – mean_absolute_error: 15614.9141
Epoch 00393: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 149us/sample – loss: 15430.8046 – mean_absolute_error: 15430.8066 – val_loss: 21319.6911 – val_mean_absolute_error: 21319.6914
Epoch 394/500
960/1168 [=======================>……] – ETA: 0s – loss: 14438.1852 – mean_absolute_error: 14438.1855
Epoch 00394: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 131us/sample – loss: 14228.9914 – mean_absolute_error: 14228.9902 – val_loss: 20146.5828 – val_mean_absolute_error: 20146.5820
Epoch 395/500
1088/1168 [==========================>…] – ETA: 0s – loss: 14472.3176 – mean_absolute_error: 14472.3164
Epoch 00395: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 120us/sample – loss: 14398.4574 – mean_absolute_error: 14398.4551 – val_loss: 20585.9247 – val_mean_absolute_error: 20585.9238
Epoch 396/500
1152/1168 [============================>.] – ETA: 0s – loss: 13930.0449 – mean_absolute_error: 13930.0449
Epoch 00396: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 161us/sample – loss: 13891.1053 – mean_absolute_error: 13891.1055 – val_loss: 20545.6099 – val_mean_absolute_error: 20545.6074
Epoch 397/500
960/1168 [=======================>……] – ETA: 0s – loss: 14230.7075 – mean_absolute_error: 14230.7080
Epoch 00397: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 134us/sample – loss: 14241.0597 – mean_absolute_error: 14241.0596 – val_loss: 20206.5463 – val_mean_absolute_error: 20206.5469
Epoch 398/500
960/1168 [=======================>……] – ETA: 0s – loss: 14043.8622 – mean_absolute_error: 14043.8604
Epoch 00398: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 140us/sample – loss: 14481.5040 – mean_absolute_error: 14481.5020 – val_loss: 19746.7072 – val_mean_absolute_error: 19746.7070
Epoch 399/500
864/1168 [=====================>……..] – ETA: 0s – loss: 13967.7686 – mean_absolute_error: 13967.7686
Epoch 00399: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 144us/sample – loss: 14467.2675 – mean_absolute_error: 14467.2686 – val_loss: 21081.3887 – val_mean_absolute_error: 21081.3867
Epoch 400/500
768/1168 [==================>………..] – ETA: 0s – loss: 14033.5311 – mean_absolute_error: 14033.5312
Epoch 00400: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 152us/sample – loss: 14589.3799 – mean_absolute_error: 14589.3789 – val_loss: 21607.6176 – val_mean_absolute_error: 21607.6172
Epoch 401/500
896/1168 [======================>…….] – ETA: 0s – loss: 15005.8307 – mean_absolute_error: 15005.8291
Epoch 00401: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 144us/sample – loss: 14890.8719 – mean_absolute_error: 14890.8711 – val_loss: 22093.8774 – val_mean_absolute_error: 22093.8789
Epoch 402/500
1152/1168 [============================>.] – ETA: 0s – loss: 14712.9746 – mean_absolute_error: 14712.9756
Epoch 00402: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 160us/sample – loss: 14677.0392 – mean_absolute_error: 14677.0410 – val_loss: 21520.1642 – val_mean_absolute_error: 21520.1621
Epoch 403/500
992/1168 [========================>…..] – ETA: 0s – loss: 14746.1308 – mean_absolute_error: 14746.1289
Epoch 00403: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 127us/sample – loss: 14782.9402 – mean_absolute_error: 14782.9404 – val_loss: 20303.6143 – val_mean_absolute_error: 20303.6133
Epoch 404/500
992/1168 [========================>…..] – ETA: 0s – loss: 15045.8342 – mean_absolute_error: 15045.8350
Epoch 00404: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 124us/sample – loss: 15207.0172 – mean_absolute_error: 15207.0176 – val_loss: 19923.2407 – val_mean_absolute_error: 19923.2402
Epoch 405/500
1056/1168 [==========================>…] – ETA: 0s – loss: 15570.7501 – mean_absolute_error: 15570.7510
Epoch 00405: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 121us/sample – loss: 15720.4504 – mean_absolute_error: 15720.4502 – val_loss: 20735.1406 – val_mean_absolute_error: 20735.1426
Epoch 406/500
1056/1168 [==========================>…] – ETA: 0s – loss: 14766.1141 – mean_absolute_error: 14766.1143
Epoch 00406: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 120us/sample – loss: 14852.5453 – mean_absolute_error: 14852.5459 – val_loss: 20173.2770 – val_mean_absolute_error: 20173.2754
Epoch 407/500
992/1168 [========================>…..] – ETA: 0s – loss: 13418.0606 – mean_absolute_error: 13418.0625
Epoch 00407: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 123us/sample – loss: 13988.1077 – mean_absolute_error: 13988.1084 – val_loss: 20921.7791 – val_mean_absolute_error: 20921.7793
Epoch 408/500
960/1168 [=======================>……] – ETA: 0s – loss: 15384.5604 – mean_absolute_error: 15384.5605
Epoch 00408: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 137us/sample – loss: 16129.3982 – mean_absolute_error: 16129.3994 – val_loss: 23485.0858 – val_mean_absolute_error: 23485.0898
Epoch 409/500
1056/1168 [==========================>…] – ETA: 0s – loss: 16019.7239 – mean_absolute_error: 16019.7256
Epoch 00409: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 237us/sample – loss: 15999.7013 – mean_absolute_error: 15999.7021 – val_loss: 20732.6632 – val_mean_absolute_error: 20732.6621
Epoch 410/500
896/1168 [======================>…….] – ETA: 0s – loss: 14407.4587 – mean_absolute_error: 14407.4609
Epoch 00410: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 179us/sample – loss: 14417.6908 – mean_absolute_error: 14417.6934 – val_loss: 20812.2567 – val_mean_absolute_error: 20812.2578
Epoch 411/500
1088/1168 [==========================>…] – ETA: 0s – loss: 15146.6086 – mean_absolute_error: 15146.6104
Epoch 00411: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 281us/sample – loss: 15106.9815 – mean_absolute_error: 15106.9814 – val_loss: 21764.6454 – val_mean_absolute_error: 21764.6445
Epoch 412/500
1056/1168 [==========================>…] – ETA: 0s – loss: 15199.4033 – mean_absolute_error: 15199.4043
Epoch 00412: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 376us/sample – loss: 15499.2408 – mean_absolute_error: 15499.2432 – val_loss: 21973.0601 – val_mean_absolute_error: 21973.0605
Epoch 413/500
1088/1168 [==========================>…] – ETA: 0s – loss: 17532.6324 – mean_absolute_error: 17532.6328
Epoch 00413: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 280us/sample – loss: 17418.4748 – mean_absolute_error: 17418.4766 – val_loss: 21422.2697 – val_mean_absolute_error: 21422.2715
Epoch 414/500
1120/1168 [===========================>..] – ETA: 0s – loss: 16875.1547 – mean_absolute_error: 16875.1543
Epoch 00414: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 297us/sample – loss: 16736.2794 – mean_absolute_error: 16736.2773 – val_loss: 20063.5805 – val_mean_absolute_error: 20063.5781
Epoch 415/500
864/1168 [=====================>……..] – ETA: 0s – loss: 15063.7038 – mean_absolute_error: 15063.7041
Epoch 00415: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 214us/sample – loss: 14958.4468 – mean_absolute_error: 14958.4473 – val_loss: 25498.4567 – val_mean_absolute_error: 25498.4570
Epoch 416/500
960/1168 [=======================>……] – ETA: 0s – loss: 14805.6788 – mean_absolute_error: 14805.6787
Epoch 00416: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 202us/sample – loss: 14506.7739 – mean_absolute_error: 14506.7744 – val_loss: 20069.9039 – val_mean_absolute_error: 20069.9043
Epoch 417/500
832/1168 [====================>………] – ETA: 0s – loss: 14078.0315 – mean_absolute_error: 14078.0322
Epoch 00417: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 155us/sample – loss: 13721.8001 – mean_absolute_error: 13721.8018 – val_loss: 20507.2341 – val_mean_absolute_error: 20507.2324
Epoch 418/500
864/1168 [=====================>……..] – ETA: 0s – loss: 13157.5209 – mean_absolute_error: 13157.5205
Epoch 00418: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 144us/sample – loss: 13520.3549 – mean_absolute_error: 13520.3545 – val_loss: 20622.4551 – val_mean_absolute_error: 20622.4551
Epoch 419/500
896/1168 [======================>…….] – ETA: 0s – loss: 13872.3142 – mean_absolute_error: 13872.3154
Epoch 00419: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 143us/sample – loss: 13942.9585 – mean_absolute_error: 13942.9600 – val_loss: 21041.8620 – val_mean_absolute_error: 21041.8613
Epoch 420/500
864/1168 [=====================>……..] – ETA: 0s – loss: 14808.0598 – mean_absolute_error: 14808.0586
Epoch 00420: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 142us/sample – loss: 14944.6996 – mean_absolute_error: 14944.6982 – val_loss: 22529.9663 – val_mean_absolute_error: 22529.9648
Epoch 421/500
960/1168 [=======================>……] – ETA: 0s – loss: 14463.3302 – mean_absolute_error: 14463.3291
Epoch 00421: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 133us/sample – loss: 14580.7967 – mean_absolute_error: 14580.7979 – val_loss: 19707.7302 – val_mean_absolute_error: 19707.7305
Epoch 422/500
896/1168 [======================>…….] – ETA: 0s – loss: 13837.4266 – mean_absolute_error: 13837.4277
Epoch 00422: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 139us/sample – loss: 13998.0369 – mean_absolute_error: 13998.0381 – val_loss: 20511.1238 – val_mean_absolute_error: 20511.1230
Epoch 423/500
960/1168 [=======================>……] – ETA: 0s – loss: 16042.3515 – mean_absolute_error: 16042.3525
Epoch 00423: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 133us/sample – loss: 16363.1911 – mean_absolute_error: 16363.1904 – val_loss: 21591.3618 – val_mean_absolute_error: 21591.3613
Epoch 424/500
864/1168 [=====================>……..] – ETA: 0s – loss: 15588.4439 – mean_absolute_error: 15588.4443
Epoch 00424: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 143us/sample – loss: 16633.2031 – mean_absolute_error: 16633.2012 – val_loss: 21464.8307 – val_mean_absolute_error: 21464.8301
Epoch 425/500
960/1168 [=======================>……] – ETA: 0s – loss: 15581.8015 – mean_absolute_error: 15581.8027
Epoch 00425: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 137us/sample – loss: 15654.8730 – mean_absolute_error: 15654.8750 – val_loss: 23370.3757 – val_mean_absolute_error: 23370.3770
Epoch 426/500
864/1168 [=====================>……..] – ETA: 0s – loss: 14927.7908 – mean_absolute_error: 14927.7891
Epoch 00426: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 143us/sample – loss: 14724.5034 – mean_absolute_error: 14724.5039 – val_loss: 20691.3507 – val_mean_absolute_error: 20691.3496
Epoch 427/500
928/1168 [======================>…….] – ETA: 0s – loss: 14352.9057 – mean_absolute_error: 14352.9043
Epoch 00427: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 199us/sample – loss: 14857.3964 – mean_absolute_error: 14857.3936 – val_loss: 23522.8354 – val_mean_absolute_error: 23522.8359
Epoch 428/500
1152/1168 [============================>.] – ETA: 0s – loss: 14119.7272 – mean_absolute_error: 14119.7275
Epoch 00428: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 168us/sample – loss: 14159.2495 – mean_absolute_error: 14159.2500 – val_loss: 20504.9887 – val_mean_absolute_error: 20504.9902
Epoch 429/500
1088/1168 [==========================>…] – ETA: 0s – loss: 14266.9906 – mean_absolute_error: 14266.9912
Epoch 00429: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 179us/sample – loss: 14270.8645 – mean_absolute_error: 14270.8652 – val_loss: 19776.2923 – val_mean_absolute_error: 19776.2930
Epoch 430/500
1056/1168 [==========================>…] – ETA: 0s – loss: 16165.6495 – mean_absolute_error: 16165.6494
Epoch 00430: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 176us/sample – loss: 15757.8773 – mean_absolute_error: 15757.8789 – val_loss: 20522.5475 – val_mean_absolute_error: 20522.5469
Epoch 431/500
896/1168 [======================>…….] – ETA: 0s – loss: 15042.5746 – mean_absolute_error: 15042.5762
Epoch 00431: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 137us/sample – loss: 15081.6061 – mean_absolute_error: 15081.6074 – val_loss: 21918.0388 – val_mean_absolute_error: 21918.0391
Epoch 432/500
960/1168 [=======================>……] – ETA: 0s – loss: 14446.1263 – mean_absolute_error: 14446.1260
Epoch 00432: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 130us/sample – loss: 14118.4315 – mean_absolute_error: 14118.4316 – val_loss: 20178.1891 – val_mean_absolute_error: 20178.1875
Epoch 433/500
1056/1168 [==========================>…] – ETA: 0s – loss: 14015.7029 – mean_absolute_error: 14015.7031
Epoch 00433: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 120us/sample – loss: 14008.7264 – mean_absolute_error: 14008.7256 – val_loss: 20090.7153 – val_mean_absolute_error: 20090.7148
Epoch 434/500
992/1168 [========================>…..] – ETA: 0s – loss: 14850.6303 – mean_absolute_error: 14850.6299
Epoch 00434: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 127us/sample – loss: 14921.0210 – mean_absolute_error: 14921.0205 – val_loss: 22175.5360 – val_mean_absolute_error: 22175.5352
Epoch 435/500
960/1168 [=======================>……] – ETA: 0s – loss: 15104.0317 – mean_absolute_error: 15104.0312
Epoch 00435: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 131us/sample – loss: 14713.3482 – mean_absolute_error: 14713.3477 – val_loss: 21132.9668 – val_mean_absolute_error: 21132.9668
Epoch 436/500
1056/1168 [==========================>…] – ETA: 0s – loss: 14055.2515 – mean_absolute_error: 14055.2549
Epoch 00436: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 124us/sample – loss: 14100.6908 – mean_absolute_error: 14100.6934 – val_loss: 20280.5771 – val_mean_absolute_error: 20280.5762
Epoch 437/500
928/1168 [======================>…….] – ETA: 0s – loss: 13483.3265 – mean_absolute_error: 13483.3252
Epoch 00437: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 129us/sample – loss: 13595.7874 – mean_absolute_error: 13595.7871 – val_loss: 21580.8058 – val_mean_absolute_error: 21580.8066
Epoch 438/500
1056/1168 [==========================>…] – ETA: 0s – loss: 14078.7517 – mean_absolute_error: 14078.7520
Epoch 00438: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 120us/sample – loss: 14273.1702 – mean_absolute_error: 14273.1709 – val_loss: 21115.1065 – val_mean_absolute_error: 21115.1055
Epoch 439/500
928/1168 [======================>…….] – ETA: 0s – loss: 15660.8069 – mean_absolute_error: 15660.8066
Epoch 00439: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 140us/sample – loss: 15726.7151 – mean_absolute_error: 15726.7139 – val_loss: 21526.1163 – val_mean_absolute_error: 21526.1152
Epoch 440/500
1024/1168 [=========================>….] – ETA: 0s – loss: 16152.1456 – mean_absolute_error: 16152.1465
Epoch 00440: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 123us/sample – loss: 15848.3714 – mean_absolute_error: 15848.3730 – val_loss: 19915.0706 – val_mean_absolute_error: 19915.0723
Epoch 441/500
1056/1168 [==========================>…] – ETA: 0s – loss: 14167.8328 – mean_absolute_error: 14167.8320
Epoch 00441: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 120us/sample – loss: 14281.8887 – mean_absolute_error: 14281.8877 – val_loss: 21858.2217 – val_mean_absolute_error: 21858.2207
Epoch 442/500
1024/1168 [=========================>….] – ETA: 0s – loss: 14646.2816 – mean_absolute_error: 14646.2812
Epoch 00442: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 123us/sample – loss: 14598.8508 – mean_absolute_error: 14598.8496 – val_loss: 20645.6546 – val_mean_absolute_error: 20645.6562
Epoch 443/500
1024/1168 [=========================>….] – ETA: 0s – loss: 16066.5299 – mean_absolute_error: 16066.5283
Epoch 00443: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 125us/sample – loss: 16124.5736 – mean_absolute_error: 16124.5732 – val_loss: 22049.5109 – val_mean_absolute_error: 22049.5098
Epoch 444/500
1024/1168 [=========================>….] – ETA: 0s – loss: 15399.3902 – mean_absolute_error: 15399.3896
Epoch 00444: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 126us/sample – loss: 15682.8362 – mean_absolute_error: 15682.8369 – val_loss: 21554.1650 – val_mean_absolute_error: 21554.1660
Epoch 445/500
992/1168 [========================>…..] – ETA: 0s – loss: 18136.1030 – mean_absolute_error: 18136.1035
Epoch 00445: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 131us/sample – loss: 17665.9577 – mean_absolute_error: 17665.9570 – val_loss: 21579.7411 – val_mean_absolute_error: 21579.7402
Epoch 446/500
1024/1168 [=========================>….] – ETA: 0s – loss: 15122.9383 – mean_absolute_error: 15122.9365
Epoch 00446: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 121us/sample – loss: 15104.3569 – mean_absolute_error: 15104.3564 – val_loss: 20169.8637 – val_mean_absolute_error: 20169.8652
Epoch 447/500
736/1168 [=================>…………] – ETA: 0s – loss: 13387.5981 – mean_absolute_error: 13387.5967
Epoch 00447: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 150us/sample – loss: 13864.2558 – mean_absolute_error: 13864.2549 – val_loss: 19634.0642 – val_mean_absolute_error: 19634.0625
Epoch 448/500
960/1168 [=======================>……] – ETA: 0s – loss: 14022.0524 – mean_absolute_error: 14022.0527
Epoch 00448: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 127us/sample – loss: 14035.7583 – mean_absolute_error: 14035.7607 – val_loss: 24074.1371 – val_mean_absolute_error: 24074.1367
Epoch 449/500
832/1168 [====================>………] – ETA: 0s – loss: 18390.5351 – mean_absolute_error: 18390.5371
Epoch 00449: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 147us/sample – loss: 17621.8406 – mean_absolute_error: 17621.8457 – val_loss: 20905.0840 – val_mean_absolute_error: 20905.0840
Epoch 450/500
992/1168 [========================>…..] – ETA: 0s – loss: 14157.9878 – mean_absolute_error: 14157.9873
Epoch 00450: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 122us/sample – loss: 14023.4549 – mean_absolute_error: 14023.4541 – val_loss: 19991.6394 – val_mean_absolute_error: 19991.6406
Epoch 451/500
1024/1168 [=========================>….] – ETA: 0s – loss: 14349.6573 – mean_absolute_error: 14349.6572
Epoch 00451: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 127us/sample – loss: 14294.7909 – mean_absolute_error: 14294.7910 – val_loss: 20355.6864 – val_mean_absolute_error: 20355.6875
Epoch 452/500
896/1168 [======================>…….] – ETA: 0s – loss: 13507.4978 – mean_absolute_error: 13507.4980
Epoch 00452: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 135us/sample – loss: 13552.1243 – mean_absolute_error: 13552.1250 – val_loss: 21270.4807 – val_mean_absolute_error: 21270.4785
Epoch 453/500
1024/1168 [=========================>….] – ETA: 0s – loss: 14186.5132 – mean_absolute_error: 14186.5117
Epoch 00453: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 123us/sample – loss: 14584.4355 – mean_absolute_error: 14584.4336 – val_loss: 23918.4784 – val_mean_absolute_error: 23918.4785
Epoch 454/500
1088/1168 [==========================>…] – ETA: 0s – loss: 14737.4963 – mean_absolute_error: 14737.4951
Epoch 00454: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 120us/sample – loss: 14701.5626 – mean_absolute_error: 14701.5635 – val_loss: 20175.8788 – val_mean_absolute_error: 20175.8809
Epoch 455/500
928/1168 [======================>…….] – ETA: 0s – loss: 14790.6585 – mean_absolute_error: 14790.6582
Epoch 00455: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 130us/sample – loss: 14589.5621 – mean_absolute_error: 14589.5635 – val_loss: 20659.9534 – val_mean_absolute_error: 20659.9551
Epoch 456/500
992/1168 [========================>…..] – ETA: 0s – loss: 13435.3501 – mean_absolute_error: 13435.3506
Epoch 00456: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 126us/sample – loss: 13530.5448 – mean_absolute_error: 13530.5449 – val_loss: 22627.3492 – val_mean_absolute_error: 22627.3477
Epoch 457/500
992/1168 [========================>…..] – ETA: 0s – loss: 15800.9155 – mean_absolute_error: 15800.9150
Epoch 00457: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 125us/sample – loss: 15528.1973 – mean_absolute_error: 15528.1973 – val_loss: 19348.9627 – val_mean_absolute_error: 19348.9629
Epoch 458/500
960/1168 [=======================>……] – ETA: 0s – loss: 13228.9563 – mean_absolute_error: 13228.9541
Epoch 00458: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 139us/sample – loss: 13419.5860 – mean_absolute_error: 13419.5850 – val_loss: 19819.7969 – val_mean_absolute_error: 19819.7988
Epoch 459/500
1024/1168 [=========================>….] – ETA: 0s – loss: 14316.6400 – mean_absolute_error: 14316.6387
Epoch 00459: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 123us/sample – loss: 14475.0008 – mean_absolute_error: 14475.0000 – val_loss: 22079.2545 – val_mean_absolute_error: 22079.2559
Epoch 460/500
992/1168 [========================>…..] – ETA: 0s – loss: 14063.2345 – mean_absolute_error: 14063.2334
Epoch 00460: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 126us/sample – loss: 14151.5165 – mean_absolute_error: 14151.5176 – val_loss: 20580.4763 – val_mean_absolute_error: 20580.4785
Epoch 461/500
1056/1168 [==========================>…] – ETA: 0s – loss: 13874.3369 – mean_absolute_error: 13874.3340
Epoch 00461: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 124us/sample – loss: 13802.2805 – mean_absolute_error: 13802.2783 – val_loss: 21258.6699 – val_mean_absolute_error: 21258.6699
Epoch 462/500
928/1168 [======================>…….] – ETA: 0s – loss: 13678.9797 – mean_absolute_error: 13678.9795
Epoch 00462: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 135us/sample – loss: 14152.8272 – mean_absolute_error: 14152.8271 – val_loss: 23484.5117 – val_mean_absolute_error: 23484.5117
Epoch 463/500
992/1168 [========================>…..] – ETA: 0s – loss: 14652.3095 – mean_absolute_error: 14652.3105
Epoch 00463: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 123us/sample – loss: 14538.5104 – mean_absolute_error: 14538.5107 – val_loss: 21905.9122 – val_mean_absolute_error: 21905.9102
Epoch 464/500
992/1168 [========================>…..] – ETA: 0s – loss: 16645.6280 – mean_absolute_error: 16645.6309
Epoch 00464: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 126us/sample – loss: 16084.3097 – mean_absolute_error: 16084.3115 – val_loss: 20323.4585 – val_mean_absolute_error: 20323.4609
Epoch 465/500
864/1168 [=====================>……..] – ETA: 0s – loss: 15229.7613 – mean_absolute_error: 15229.7588
Epoch 00465: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 141us/sample – loss: 14991.7334 – mean_absolute_error: 14991.7295 – val_loss: 20973.0342 – val_mean_absolute_error: 20973.0332
Epoch 466/500
992/1168 [========================>…..] – ETA: 0s – loss: 14959.0172 – mean_absolute_error: 14959.0176
Epoch 00466: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 125us/sample – loss: 15542.0663 – mean_absolute_error: 15542.0664 – val_loss: 22160.7071 – val_mean_absolute_error: 22160.7070
Epoch 467/500
1056/1168 [==========================>…] – ETA: 0s – loss: 16200.0662 – mean_absolute_error: 16200.0664
Epoch 00467: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 121us/sample – loss: 16161.2082 – mean_absolute_error: 16161.2090 – val_loss: 20880.2627 – val_mean_absolute_error: 20880.2617
Epoch 468/500
992/1168 [========================>…..] – ETA: 0s – loss: 16434.9585 – mean_absolute_error: 16434.9570
Epoch 00468: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 131us/sample – loss: 15921.1917 – mean_absolute_error: 15921.1904 – val_loss: 20467.7013 – val_mean_absolute_error: 20467.7012
Epoch 469/500
1024/1168 [=========================>….] – ETA: 0s – loss: 14109.3159 – mean_absolute_error: 14109.3164
Epoch 00469: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 128us/sample – loss: 14007.2533 – mean_absolute_error: 14007.2539 – val_loss: 21241.0563 – val_mean_absolute_error: 21241.0566
Epoch 470/500
864/1168 [=====================>……..] – ETA: 0s – loss: 13963.7756 – mean_absolute_error: 13963.7754
Epoch 00470: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 140us/sample – loss: 14163.2466 – mean_absolute_error: 14163.2461 – val_loss: 20729.8093 – val_mean_absolute_error: 20729.8105
Epoch 471/500
1056/1168 [==========================>…] – ETA: 0s – loss: 13781.9896 – mean_absolute_error: 13781.9873
Epoch 00471: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 125us/sample – loss: 14061.9108 – mean_absolute_error: 14061.9082 – val_loss: 20257.6809 – val_mean_absolute_error: 20257.6797
Epoch 472/500
1024/1168 [=========================>….] – ETA: 0s – loss: 17278.4297 – mean_absolute_error: 17278.4297
Epoch 00472: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 120us/sample – loss: 17355.3693 – mean_absolute_error: 17355.3691 – val_loss: 25877.3856 – val_mean_absolute_error: 25877.3848
Epoch 473/500
960/1168 [=======================>……] – ETA: 0s – loss: 14794.6887 – mean_absolute_error: 14794.6895
Epoch 00473: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 140us/sample – loss: 14568.8348 – mean_absolute_error: 14568.8359 – val_loss: 20397.6283 – val_mean_absolute_error: 20397.6289
Epoch 474/500
928/1168 [======================>…….] – ETA: 0s – loss: 14228.1290 – mean_absolute_error: 14228.1289
Epoch 00474: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 140us/sample – loss: 13829.6723 – mean_absolute_error: 13829.6729 – val_loss: 20309.1032 – val_mean_absolute_error: 20309.1035
Epoch 475/500
928/1168 [======================>…….] – ETA: 0s – loss: 14263.3244 – mean_absolute_error: 14263.3232
Epoch 00475: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 137us/sample – loss: 14164.5187 – mean_absolute_error: 14164.5176 – val_loss: 20046.4128 – val_mean_absolute_error: 20046.4102
Epoch 476/500
928/1168 [======================>…….] – ETA: 0s – loss: 13850.0321 – mean_absolute_error: 13850.0322
Epoch 00476: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 137us/sample – loss: 13682.3427 – mean_absolute_error: 13682.3418 – val_loss: 19864.1399 – val_mean_absolute_error: 19864.1387
Epoch 477/500
960/1168 [=======================>……] – ETA: 0s – loss: 14264.4938 – mean_absolute_error: 14264.4932
Epoch 00477: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 131us/sample – loss: 14567.2910 – mean_absolute_error: 14567.2891 – val_loss: 20929.7508 – val_mean_absolute_error: 20929.7480
Epoch 478/500
928/1168 [======================>…….] – ETA: 0s – loss: 14492.2464 – mean_absolute_error: 14492.2471
Epoch 00478: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 136us/sample – loss: 15234.8086 – mean_absolute_error: 15234.8086 – val_loss: 24013.7803 – val_mean_absolute_error: 24013.7793
Epoch 479/500
928/1168 [======================>…….] – ETA: 0s – loss: 15423.4942 – mean_absolute_error: 15423.4961
Epoch 00479: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 131us/sample – loss: 15306.5961 – mean_absolute_error: 15306.5977 – val_loss: 22284.3782 – val_mean_absolute_error: 22284.3789
Epoch 480/500
992/1168 [========================>…..] – ETA: 0s – loss: 13692.1782 – mean_absolute_error: 13692.1777
Epoch 00480: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 131us/sample – loss: 14152.7634 – mean_absolute_error: 14152.7627 – val_loss: 20748.4468 – val_mean_absolute_error: 20748.4453
Epoch 481/500
960/1168 [=======================>……] – ETA: 0s – loss: 16288.2014 – mean_absolute_error: 16288.2021
Epoch 00481: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 135us/sample – loss: 16315.2749 – mean_absolute_error: 16315.2754 – val_loss: 20135.2680 – val_mean_absolute_error: 20135.2695
Epoch 482/500
864/1168 [=====================>……..] – ETA: 0s – loss: 14158.9685 – mean_absolute_error: 14158.9688
Epoch 00482: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 147us/sample – loss: 14034.1647 – mean_absolute_error: 14034.1641 – val_loss: 20579.4992 – val_mean_absolute_error: 20579.4980
Epoch 483/500
896/1168 [======================>…….] – ETA: 0s – loss: 14612.5501 – mean_absolute_error: 14612.5488
Epoch 00483: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 144us/sample – loss: 14698.0003 – mean_absolute_error: 14698.0000 – val_loss: 21716.3569 – val_mean_absolute_error: 21716.3574
Epoch 484/500
864/1168 [=====================>……..] – ETA: 0s – loss: 13866.2125 – mean_absolute_error: 13866.2119
Epoch 00484: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 145us/sample – loss: 14265.1206 – mean_absolute_error: 14265.1211 – val_loss: 19965.4036 – val_mean_absolute_error: 19965.4023
Epoch 485/500
896/1168 [======================>…….] – ETA: 0s – loss: 13969.0258 – mean_absolute_error: 13969.0244
Epoch 00485: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 138us/sample – loss: 13965.8748 – mean_absolute_error: 13965.8740 – val_loss: 19908.1933 – val_mean_absolute_error: 19908.1934
Epoch 486/500
928/1168 [======================>…….] – ETA: 0s – loss: 14490.5191 – mean_absolute_error: 14490.5186
Epoch 00486: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 138us/sample – loss: 14479.1797 – mean_absolute_error: 14479.1797 – val_loss: 20195.7300 – val_mean_absolute_error: 20195.7324
Epoch 487/500
864/1168 [=====================>……..] – ETA: 0s – loss: 14005.1832 – mean_absolute_error: 14005.1826
Epoch 00487: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 143us/sample – loss: 15465.2619 – mean_absolute_error: 15465.2607 – val_loss: 29933.1949 – val_mean_absolute_error: 29933.1914
Epoch 488/500
928/1168 [======================>…….] – ETA: 0s – loss: 17983.1120 – mean_absolute_error: 17983.1113
Epoch 00488: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 137us/sample – loss: 17134.4067 – mean_absolute_error: 17134.4062 – val_loss: 20474.9724 – val_mean_absolute_error: 20474.9727
Epoch 489/500
896/1168 [======================>…….] – ETA: 0s – loss: 13382.3732 – mean_absolute_error: 13382.3721
Epoch 00489: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 143us/sample – loss: 13967.5620 – mean_absolute_error: 13967.5605 – val_loss: 23867.4266 – val_mean_absolute_error: 23867.4258
Epoch 490/500
832/1168 [====================>………] – ETA: 0s – loss: 17868.7001 – mean_absolute_error: 17868.6992
Epoch 00490: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 146us/sample – loss: 17102.0193 – mean_absolute_error: 17102.0176 – val_loss: 22157.6575 – val_mean_absolute_error: 22157.6582
Epoch 491/500
800/1168 [===================>……….] – ETA: 0s – loss: 14781.0398 – mean_absolute_error: 14781.0400
Epoch 00491: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 149us/sample – loss: 15119.1851 – mean_absolute_error: 15119.1846 – val_loss: 22109.9049 – val_mean_absolute_error: 22109.9043
Epoch 492/500
864/1168 [=====================>……..] – ETA: 0s – loss: 16073.1453 – mean_absolute_error: 16073.1455
Epoch 00492: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 139us/sample – loss: 16336.1436 – mean_absolute_error: 16336.1455 – val_loss: 21006.9140 – val_mean_absolute_error: 21006.9141
Epoch 493/500
928/1168 [======================>…….] – ETA: 0s – loss: 13231.8747 – mean_absolute_error: 13231.8750
Epoch 00493: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 138us/sample – loss: 13619.7374 – mean_absolute_error: 13619.7373 – val_loss: 26109.9220 – val_mean_absolute_error: 26109.9238
Epoch 494/500
896/1168 [======================>…….] – ETA: 0s – loss: 16279.9845 – mean_absolute_error: 16279.9844
Epoch 00494: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 143us/sample – loss: 16067.6761 – mean_absolute_error: 16067.6748 – val_loss: 23794.1211 – val_mean_absolute_error: 23794.1211
Epoch 495/500
928/1168 [======================>…….] – ETA: 0s – loss: 14325.2337 – mean_absolute_error: 14325.2324
Epoch 00495: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 135us/sample – loss: 14258.5056 – mean_absolute_error: 14258.5049 – val_loss: 20929.3663 – val_mean_absolute_error: 20929.3672
Epoch 496/500
800/1168 [===================>……….] – ETA: 0s – loss: 13857.6488 – mean_absolute_error: 13857.6475
Epoch 00496: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 146us/sample – loss: 14124.6974 – mean_absolute_error: 14124.6973 – val_loss: 20404.9172 – val_mean_absolute_error: 20404.9160
Epoch 497/500
864/1168 [=====================>……..] – ETA: 0s – loss: 14293.5812 – mean_absolute_error: 14293.5811
Epoch 00497: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 143us/sample – loss: 14158.6481 – mean_absolute_error: 14158.6494 – val_loss: 20707.6622 – val_mean_absolute_error: 20707.6602
Epoch 498/500
928/1168 [======================>…….] – ETA: 0s – loss: 13413.4861 – mean_absolute_error: 13413.485 – ETA: 0s – loss: 14169.1065 – mean_absolute_error: 14169.1064
Epoch 00498: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 138us/sample – loss: 14648.7456 – mean_absolute_error: 14648.7451 – val_loss: 21075.6366 – val_mean_absolute_error: 21075.6348
Epoch 499/500
928/1168 [======================>…….] – ETA: 0s – loss: 15687.6749 – mean_absolute_error: 15687.6768
Epoch 00499: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 127us/sample – loss: 15305.0852 – mean_absolute_error: 15305.0859 – val_loss: 22625.7373 – val_mean_absolute_error: 22625.7383
Epoch 500/500
1024/1168 [=========================>….] – ETA: 0s – loss: 14597.8644 – mean_absolute_error: 14597.8652
Epoch 00500: val_loss did not improve from 19021.54955
1168/1168 [==============================] – 0s 127us/sample – loss: 14994.0517 – mean_absolute_error: 14994.0527 – val_loss: 25678.1949 – val_mean_absolute_error: 25678.1953
In [27]:
# Load wights file of the best model :
wights_file = ‘Models/Weights-268–19021.54955.hdf5′ # choose the best checkpoint- YOURS IS DIFFERENT THAN THIS NUMBER
NN_model.load_weights(wights_file) # load it
NN_model.compile(loss=’mean_absolute_error’, optimizer=’adam’, metrics=[‘mean_absolute_error’])
In [28]:
# Make predictions
predictions = NN_model.predict(test)
WARNING:tensorflow:Falling back from v2 loop because of error: Failed to find data adapter that can handle input:
WARNING:tensorflow:Falling back from v2 loop because of error: Failed to find data adapter that can handle input:
In [29]:
predictions
Out[29]:
array([[123776.84],
[235035.56],
[180865.61],
…,
[172107.81],
[145937.22],
[214936.33]], dtype=float32)
In [30]:
#plt.style.use(‘ggplot’)
def plot_history(history):
loss = history.history[‘loss’]
val_loss = history.history[‘val_loss’]
x = range(1, len(loss) + 1)
plt.figure(figsize=(12, 5))
plt.plot(x, loss, ‘b’, label=’Training loss’)
plt.plot(x, val_loss, ‘r’, label=’Validation loss’)
plt.title(‘Training and validation loss’)
plt.legend()
plt.show()
plot_history(hist)
In [32]:
import tensorflow
early_stop = tensorflow.keras.callbacks.EarlyStopping(monitor=’val_loss’, patience= 30)
callbacks_list= ModelCheckpoint(‘Models/Weights-{epoch:03d}–{val_loss:.5f}.hdf5′, monitor=’val_loss’, save_best_only = True)
callbacks = [early_stop, callbacks_list]
In [33]:
hist = NN_model.fit(train, target, epochs=500, batch_size=32, validation_split = 0.2, callbacks=callbacks)
WARNING:tensorflow:Falling back from v2 loop because of error: Failed to find data adapter that can handle input:
WARNING:tensorflow:Falling back from v2 loop because of error: Failed to find data adapter that can handle input:
Train on 1168 samples, validate on 292 samples
Epoch 1/500
1168/1168 [==============================] – 0s 265us/sample – loss: 19515.0980 – mean_absolute_error: 19515.0977 – val_loss: 21412.6048 – val_mean_absolute_error: 21412.6035
Epoch 2/500
1168/1168 [==============================] – 0s 152us/sample – loss: 16076.9050 – mean_absolute_error: 16076.9062 – val_loss: 21335.5540 – val_mean_absolute_error: 21335.5547
Epoch 3/500
1168/1168 [==============================] – 0s 131us/sample – loss: 16652.1658 – mean_absolute_error: 16652.1641 – val_loss: 21366.2398 – val_mean_absolute_error: 21366.2422
Epoch 4/500
1168/1168 [==============================] – 0s 259us/sample – loss: 15734.0637 – mean_absolute_error: 15734.0635 – val_loss: 23244.6636 – val_mean_absolute_error: 23244.6621
Epoch 5/500
1168/1168 [==============================] – 0s 183us/sample – loss: 16418.7462 – mean_absolute_error: 16418.7461 – val_loss: 19819.9880 – val_mean_absolute_error: 19819.9883
Epoch 6/500
1168/1168 [==============================] – 0s 155us/sample – loss: 15823.5233 – mean_absolute_error: 15823.5225 – val_loss: 22173.8992 – val_mean_absolute_error: 22173.8965
Epoch 7/500
1168/1168 [==============================] – 0s 225us/sample – loss: 15473.8573 – mean_absolute_error: 15473.8594 – val_loss: 20303.1729 – val_mean_absolute_error: 20303.1738
Epoch 8/500
1168/1168 [==============================] – 0s 144us/sample – loss: 16859.8279 – mean_absolute_error: 16859.8262 – val_loss: 22120.0988 – val_mean_absolute_error: 22120.1016
Epoch 9/500
1168/1168 [==============================] – 0s 215us/sample – loss: 16039.1181 – mean_absolute_error: 16039.1182 – val_loss: 23774.6176 – val_mean_absolute_error: 23774.6172
Epoch 10/500
1168/1168 [==============================] – 0s 131us/sample – loss: 15562.4100 – mean_absolute_error: 15562.4111 – val_loss: 20220.8517 – val_mean_absolute_error: 20220.8535
Epoch 11/500
1168/1168 [==============================] – 0s 137us/sample – loss: 16386.9869 – mean_absolute_error: 16386.9902 – val_loss: 22865.5298 – val_mean_absolute_error: 22865.5293
Epoch 12/500
1168/1168 [==============================] – 0s 122us/sample – loss: 16283.3474 – mean_absolute_error: 16283.3477 – val_loss: 21156.2470 – val_mean_absolute_error: 21156.2461
Epoch 13/500
1168/1168 [==============================] – 0s 143us/sample – loss: 15270.3017 – mean_absolute_error: 15270.3027 – val_loss: 19521.2007 – val_mean_absolute_error: 19521.2012
Epoch 14/500
1168/1168 [==============================] – 0s 130us/sample – loss: 15152.6674 – mean_absolute_error: 15152.6680 – val_loss: 19756.4869 – val_mean_absolute_error: 19756.4883
Epoch 15/500
1168/1168 [==============================] – 0s 136us/sample – loss: 15487.3398 – mean_absolute_error: 15487.3408 – val_loss: 20807.4235 – val_mean_absolute_error: 20807.4238
Epoch 16/500
1168/1168 [==============================] – 0s 127us/sample – loss: 17103.4157 – mean_absolute_error: 17103.4160 – val_loss: 23899.6919 – val_mean_absolute_error: 23899.6914
Epoch 17/500
1168/1168 [==============================] – 0s 138us/sample – loss: 15601.9872 – mean_absolute_error: 15601.9883 – val_loss: 21620.4568 – val_mean_absolute_error: 21620.4551
Epoch 18/500
1168/1168 [==============================] – 0s 135us/sample – loss: 15304.3522 – mean_absolute_error: 15304.3506 – val_loss: 19947.5172 – val_mean_absolute_error: 19947.5176
Epoch 19/500
1168/1168 [==============================] – 0s 136us/sample – loss: 15853.4462 – mean_absolute_error: 15853.4473 – val_loss: 20316.2519 – val_mean_absolute_error: 20316.2539
Epoch 20/500
1168/1168 [==============================] – 0s 178us/sample – loss: 15406.1253 – mean_absolute_error: 15406.1250 – val_loss: 20765.2253 – val_mean_absolute_error: 20765.2285
Epoch 21/500
1168/1168 [==============================] – 0s 183us/sample – loss: 15879.8468 – mean_absolute_error: 15879.8477 – val_loss: 20478.1405 – val_mean_absolute_error: 20478.1406
Epoch 22/500
1168/1168 [==============================] – 0s 172us/sample – loss: 15532.7071 – mean_absolute_error: 15532.7070 – val_loss: 19213.1375 – val_mean_absolute_error: 19213.1367
Epoch 23/500
1168/1168 [==============================] – 0s 142us/sample – loss: 15275.2724 – mean_absolute_error: 15275.2725 – val_loss: 21355.4735 – val_mean_absolute_error: 21355.4746
Epoch 24/500
1168/1168 [==============================] – 0s 131us/sample – loss: 15877.9764 – mean_absolute_error: 15877.9746 – val_loss: 22735.8542 – val_mean_absolute_error: 22735.8535
Epoch 25/500
1168/1168 [==============================] – 0s 128us/sample – loss: 17154.3873 – mean_absolute_error: 17154.3867 – val_loss: 24303.8777 – val_mean_absolute_error: 24303.8789
Epoch 26/500
1168/1168 [==============================] – 0s 125us/sample – loss: 17391.4972 – mean_absolute_error: 17391.4980 – val_loss: 20208.8854 – val_mean_absolute_error: 20208.8848
Epoch 27/500
1168/1168 [==============================] – 0s 165us/sample – loss: 15274.3994 – mean_absolute_error: 15274.4004 – val_loss: 20086.7404 – val_mean_absolute_error: 20086.7402
Epoch 28/500
1168/1168 [==============================] – 0s 139us/sample – loss: 15786.8221 – mean_absolute_error: 15786.8223 – val_loss: 25395.0046 – val_mean_absolute_error: 25395.0039
Epoch 29/500
1168/1168 [==============================] – 0s 196us/sample – loss: 16303.5937 – mean_absolute_error: 16303.5928 – val_loss: 19973.5476 – val_mean_absolute_error: 19973.5488
Epoch 30/500
1168/1168 [==============================] – 0s 136us/sample – loss: 15538.2017 – mean_absolute_error: 15538.2041 – val_loss: 20229.8219 – val_mean_absolute_error: 20229.8242
Epoch 31/500
1168/1168 [==============================] – 0s 129us/sample – loss: 15714.7387 – mean_absolute_error: 15714.7412 – val_loss: 22653.3327 – val_mean_absolute_error: 22653.3301
Epoch 32/500
1168/1168 [==============================] – 0s 166us/sample – loss: 15955.7686 – mean_absolute_error: 15955.7686 – val_loss: 23535.3210 – val_mean_absolute_error: 23535.3223
Epoch 33/500
1168/1168 [==============================] – 0s 126us/sample – loss: 16668.9927 – mean_absolute_error: 16668.9922 – val_loss: 24576.1199 – val_mean_absolute_error: 24576.1191
Epoch 34/500
1168/1168 [==============================] – 0s 161us/sample – loss: 18063.1266 – mean_absolute_error: 18063.1250 – val_loss: 22423.2508 – val_mean_absolute_error: 22423.2500
Epoch 35/500
1168/1168 [==============================] – 0s 190us/sample – loss: 15730.8151 – mean_absolute_error: 15730.8154 – val_loss: 23198.6901 – val_mean_absolute_error: 23198.6895
Epoch 36/500
1168/1168 [==============================] – 0s 150us/sample – loss: 17847.9431 – mean_absolute_error: 17847.9434 – val_loss: 21266.7705 – val_mean_absolute_error: 21266.7715
Epoch 37/500
1168/1168 [==============================] – 0s 132us/sample – loss: 15184.0771 – mean_absolute_error: 15184.0752 – val_loss: 22168.0055 – val_mean_absolute_error: 22168.0059
Epoch 38/500
1168/1168 [==============================] – 0s 144us/sample – loss: 15774.0118 – mean_absolute_error: 15774.0107 – val_loss: 19585.7914 – val_mean_absolute_error: 19585.7930
Epoch 39/500
1168/1168 [==============================] – 0s 139us/sample – loss: 16415.5958 – mean_absolute_error: 16415.5938 – val_loss: 20628.5487 – val_mean_absolute_error: 20628.5488
Epoch 40/500
1168/1168 [==============================] – 0s 135us/sample – loss: 15532.1212 – mean_absolute_error: 15532.1211 – val_loss: 20208.4951 – val_mean_absolute_error: 20208.4922
Epoch 41/500
1168/1168 [==============================] – 0s 167us/sample – loss: 18307.2277 – mean_absolute_error: 18307.2285 – val_loss: 21913.8399 – val_mean_absolute_error: 21913.8398
Epoch 42/500
1168/1168 [==============================] – 0s 143us/sample – loss: 19448.5686 – mean_absolute_error: 19448.5703 – val_loss: 21961.7796 – val_mean_absolute_error: 21961.7812
Epoch 43/500
1168/1168 [==============================] – 0s 173us/sample – loss: 16667.0421 – mean_absolute_error: 16667.0449 – val_loss: 19736.1654 – val_mean_absolute_error: 19736.1641
Epoch 44/500
1168/1168 [==============================] – 0s 132us/sample – loss: 16455.1672 – mean_absolute_error: 16455.1680 – val_loss: 23348.7457 – val_mean_absolute_error: 23348.7461
Epoch 45/500
1168/1168 [==============================] – 0s 119us/sample – loss: 17937.1042 – mean_absolute_error: 17937.1055 – val_loss: 21423.4196 – val_mean_absolute_error: 21423.4199
Epoch 46/500
1168/1168 [==============================] – 0s 120us/sample – loss: 16816.7419 – mean_absolute_error: 16816.7422 – val_loss: 19725.0984 – val_mean_absolute_error: 19725.0996
Epoch 47/500
1168/1168 [==============================] – 0s 125us/sample – loss: 17620.9333 – mean_absolute_error: 17620.9336 – val_loss: 20608.2634 – val_mean_absolute_error: 20608.2637
Epoch 48/500
1168/1168 [==============================] – 0s 122us/sample – loss: 17637.1518 – mean_absolute_error: 17637.1543 – val_loss: 20147.1720 – val_mean_absolute_error: 20147.1719
Epoch 49/500
1168/1168 [==============================] – 0s 122us/sample – loss: 16260.3269 – mean_absolute_error: 16260.3271 – val_loss: 20381.3638 – val_mean_absolute_error: 20381.3633
Epoch 50/500
1168/1168 [==============================] – 0s 131us/sample – loss: 16667.4816 – mean_absolute_error: 16667.4805 – val_loss: 21200.7778 – val_mean_absolute_error: 21200.7793
Epoch 51/500
1168/1168 [==============================] – 0s 117us/sample – loss: 15138.0661 – mean_absolute_error: 15138.0664 – val_loss: 20690.2665 – val_mean_absolute_error: 20690.2676
Epoch 52/500
1168/1168 [==============================] – 0s 126us/sample – loss: 16944.9618 – mean_absolute_error: 16944.9609 – val_loss: 20995.3527 – val_mean_absolute_error: 20995.3535
In [34]:
plot_history(hist)
In [35]:
# Assignment 7 solution
# Make predictions
predictions2 = NN_model.predict(train)
WARNING:tensorflow:Falling back from v2 loop because of error: Failed to find data adapter that can handle input:
WARNING:tensorflow:Falling back from v2 loop because of error: Failed to find data adapter that can handle input:
In [ ]: