Python Deep Learning Cookbook

How to do it...

1. Import all necessary libraries:

import numpy as npfrom matplotlib import pyplot as pltfrom keras.utils.np_utils import to_categoricalfrom keras.models import Sequentialfrom keras.layers.core import Dense, Dropout, Flattenfrom keras.layers import Conv2Dfrom keras.optimizers import Adamfrom keras.datasets import mnist

2. Load the mnist dataset:

from keras.datasets import mnist(X_train, y_train), (X_test, y_test) = mnist.load_data()

3. Reshape the training data to represent one-channel image input:

img_rows, img_cols = X_train[0].shape[0], X_train[0].shape[1]X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)input_shape = (img_rows, img_cols, 1)