Keras

CS计算机代考程序代写 database python algorithm data science Keras Machine Learning I

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Machine Learning I Machine Learning II Lecture 13 – Unsupervised learning and DNN 1 1 Unsupervised learning What is unsupervised learning? Unsupervised learning is a branch of machine learning that learns from data that has not been labeled, classified or categorized. Example 1: A company has hired a data scientist. Here is what they asked […]

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CS计算机代考程序代写 Keras In [1]:

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In [1]: import numpy as np %matplotlib inline In [2]: from keras.datasets import mnist import matplotlib.pyplot as plt # load (downloaded if needed) the MNIST dataset (X_train, y_train), (X_test, y_test) = mnist.load_data() # plot 4 images as gray scale plt.subplot(221) plt.imshow(X_train[0], cmap=plt.get_cmap(‘gray’)) plt.subplot(222) plt.imshow(X_train[1], cmap=plt.get_cmap(‘gray’)) plt.subplot(223) plt.imshow(X_train[2], cmap=plt.get_cmap(‘gray’)) plt.subplot(224) plt.imshow(X_train[3], cmap=plt.get_cmap(‘gray’)) # show the plot plt.show() Using

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CS计算机代考程序代写 python Keras In [2]:

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In [2]: import tensorflow as tf In [4]: hello = tf.constant(‘Hello! Tensorflow’) print(hello) tf.Tensor(b’Hello! Tensorflow’, shape=(), dtype=string) In [15]: import numpy as np In [5]: mnist = tf.keras.datasets.mnist In [90]: (X_train, y_train), (X_test, y_test) = mnist.load_data() Q1- Use same method that explain in lecture to show first 6 elements of the dataset In [11]: # plot 6 images as gray scale

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CS计算机代考程序代写 Keras In [1]:

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In [1]: # read the MNIST dataset from __future__ import print_function import keras from keras.datasets import mnist from keras.models import Sequential from keras.layers import Dense, Dropout, Flatten, Activation from keras.layers import Conv2D, MaxPooling2D from keras.layers.normalization import BatchNormalization import matplotlib.pyplot as plt from keras.utils import np_utils from keras.layers import Dense from keras import optimizers from keras import

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CS计算机代考程序代写 Keras In [1]:

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In [1]: import numpy as np In [7]: from keras.datasets import mnist import matplotlib.pyplot as plt # load (downloaded if needed) the MNIST dataset (X_train, y_train), (X_test, y_test) = mnist.load_data() # plot 4 images as gray scale plt.subplot(221) plt.imshow(X_train[0], cmap=plt.get_cmap(‘gray’)) plt.subplot(222) plt.imshow(X_train[1], cmap=plt.get_cmap(‘gray’)) plt.subplot(223) plt.imshow(X_train[2], cmap=plt.get_cmap(‘gray’)) plt.subplot(224) plt.imshow(X_train[3], cmap=plt.get_cmap(‘gray’)) # show the plot plt.show()  In [10]: #

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CS计算机代考程序代写 python Keras chain In [8]:

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In [8]: import cv2 import numpy as np I. PART 1: OpenCV and object detection¶ I.1 Video Capture¶ In [14]: cap =cv2.VideoCapture(0) while True: #means forever ret,frame=cap.read() # Forever it returns the frame and ret which is false or true gray =cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)#if you want to convert the color cv2.imshow(‘frame’,frame) cv2.imshow(‘gray’,gray)# to show the gray video if cvq2.waitKey(1)

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CS计算机代考程序代写 Keras chain In [ ]:

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In [ ]: import cv2 import numpy as np cap = cv2.VideoCapture(0) while True: ret, frame= cap.read() # Forever it returns the frame and ret which is false or true gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) #if you want to convert the color cv2.imshow(‘frame’, frame) cv2.imshow(‘gray’, gray) # to show the gray video if cv2.waitKey(1) & 0xFF == ord(‘q’):

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CS计算机代考程序代写 python Keras In [1]:

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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

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CS计算机代考程序代写 Keras In [1]:

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In [1]: from keras.datasets import mnist import matplotlib.pyplot as plt # load (downloaded if needed) the MNIST dataset (X_train, y_train), (X_test, y_test) = mnist.load_data() # plot 4 images as gray scale plt.subplot(221) plt.imshow(X_train[0], cmap=plt.get_cmap(‘gray’)) plt.subplot(222) plt.imshow(X_train[1], cmap=plt.get_cmap(‘gray’)) plt.subplot(223) plt.imshow(X_train[2], cmap=plt.get_cmap(‘gray’)) plt.subplot(224) plt.imshow(X_train[3], cmap=plt.get_cmap(‘gray’)) # show the plot plt.show() C:\Users\rsadeghian\AppData\Local\Continuum\anaconda3\lib\site-packages\h5py\__init__.py:36: FutureWarning: Conversion of the second argument of

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CS计算机代考程序代写 python Keras ML2-Lecture13

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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

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