| from keras.datasets import mnist | |
| (X_train, y_train), (X_test, y_test) = mnist.load_data() | |
| rows, cols = X_train[0].shape[0], X_train[0].shape[1] | |
| X_train = X_train.reshape(X_train.shape[0], rows, cols, 1) | |
| X_test = X_test.reshape(X_test.shape[0], rows, cols, 1) | |
| X_train = X_train.astype('float32')/255 | |
| X_test = X_test.astype('float32')/255 | |
| num_of_classes = len(set(y_train)) | |
| y_train = to_categorical(y_train, num_of_classes) | |
| y_test = to_categorical(y_test, num_of_classes) |
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