Python代写代考

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

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)

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

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

ANLY535 HW4 In [49]: import tensorflow as tf from tensorflow import keras In [50]: import numpy as np import matplotlib.pyplot as plt In [51]: fashion_mnist = keras.datasets.fashion_mnist In [52]: (train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data() In [53]: class_names = [‘T-shirt/top’, ‘Trouser’, ‘Pullover’, ‘Dress’, ‘Coat’, ‘Sandal’, ‘Shirt’, ‘Sneaker’, ‘Bag’, ‘Ankle boot’] In [54]: plt.figure() plt.imshow(train_images[0]) plt.colorbar() plt.gca().grid(False) plt.show() In [55]: train_images=train_images/255.0 test_images=test_images/255.0 In [56]: