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This is an image classification Fashion MNIST dataset. Please use Softmax Regression for classification.

This is an image classification Fashion MNIST dataset. Please use Softmax Regression for classification.

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This is an image classification Fashion MNIST dataset. Please use Softmax Regression for classification.

 

 

import numpy as np import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split [] from keras.datasets import fashion_mnist (train_X, train_y), (test_X, test_y) = fashion_mnist.load_data() fig = plt.figure(figsize=(10,7)) for i in range (15): ax fig.add_subplot (3, 5, i+1) ax.imshow (train_X[i], cmap= 'viridis') ax.set_title('Label (y): {y}'.format(y=train_y[i])) plt.axis ('off') Label (y): 9 Label (y): 2 Label (y): 0 Label (y): 7 Label (y): 0 Label (y): 2 Label (y): 3 Label (y): 5 Label (y): 0 Label (y): 5
Transcribed Image Text:import numpy as np import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split [] from keras.datasets import fashion_mnist (train_X, train_y), (test_X, test_y) = fashion_mnist.load_data() fig = plt.figure(figsize=(10,7)) for i in range (15): ax fig.add_subplot (3, 5, i+1) ax.imshow (train_X[i], cmap= 'viridis') ax.set_title('Label (y): {y}'.format(y=train_y[i])) plt.axis ('off') Label (y): 9 Label (y): 2 Label (y): 0 Label (y): 7 Label (y): 0 Label (y): 2 Label (y): 3 Label (y): 5 Label (y): 0 Label (y): 5
E R x} > Label (y): 2 Label (y): 0 [ ] train_X.shape (60000, 28, 28) Label (y): 7 Label (y): 9 A Label (y): 2 Label (y): 5 Label (y): 5 Label (y): 5 A A Label (y): 7 Label (y): 5
Transcribed Image Text:E R x} > Label (y): 2 Label (y): 0 [ ] train_X.shape (60000, 28, 28) Label (y): 7 Label (y): 9 A Label (y): 2 Label (y): 5 Label (y): 5 Label (y): 5 A A Label (y): 7 Label (y): 5
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