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import numpy as np
import keras
from keras.datasets import mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
Using TensorFlow backend.
train_images[0].shape, train_labels[0]
((28, 28), 5)
%matplotlib inline
import matplotlib.pyplot as plt

grid_size = 6
f, axarr = plt.subplots(grid_size, grid_size)
for i in range(grid_size):
    for j in range(grid_size):
        ax = axarr[i, j]
        ax.get_xaxis().set_visible(False)
        ax.get_yaxis().set_visible(False)
        ax.imshow(train_images[i * grid_size + j], cmap='gray')
Notebook Image
train_images = train_images.reshape((60000, 28, 28, 1))
train_images = train_images.astype('float32') / 255
test_images = test_images.reshape((10000, 28, 28, 1))
test_images = test_images.astype('float32') / 255

from keras.utils import to_categorical

partial_train_images = train_images[:45000]
partial_train_labels = train_labels[:45000]

validation_images = train_images[45000:]
validation_labels = train_labels[45000:]

partial_train_labels = to_categorical(partial_train_labels)
validation_labels = to_categorical(validation_labels)
test_labels = to_categorical(test_labels)
input_shape = (28,28,1)
num_classes = 10