July 2019
Intermediate to advanced
512 pages
19h 39m
English
Content preview from Hands-On Deep Learning Algorithms with Python
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Preparing the dataset
Load the MNIST dataset:
(x_train, _), (x_test, _) = mnist.load_data()
Normalize the dataset:
x_train = x_train.astype('float32') / 255.x_test = x_test.astype('float32') / 255.
Reshape the dataset:
x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
Now let's define some important parameters:
batch_size = 100original_dim = 784latent_dim = 2intermediate_dim = 256epochs = 50epsilon_std = 1.0
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