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| Original file line number | Diff line number | Diff line change |
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| from __future__ import print_function, division | ||
| from sklearn import datasets | ||
| import math | ||
| import matplotlib.pyplot as plt | ||
| import numpy as np | ||
| import progressbar | ||
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| from sklearn.datasets import fetch_mldata | ||
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| from mlfromscratch.deep_learning.optimizers import Adam | ||
| from mlfromscratch.deep_learning.loss_functions import CrossEntropy, SquareLoss | ||
| from mlfromscratch.deep_learning.layers import Dense, Dropout, Flatten, Activation, Reshape, BatchNormalization | ||
| from mlfromscratch.deep_learning import NeuralNetwork | ||
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| class Autoencoder(): | ||
| """A Autoencoder with deep fully-connected neural nets. | ||
| Training Data: MNIST Handwritten Digits (28x28 images) | ||
| """ | ||
| def __init__(self): | ||
| self.img_rows = 28 | ||
| self.img_cols = 28 | ||
| self.img_dim = self.img_rows * self.img_cols | ||
| self.latent_dim = 128 # The dimension of the data embedding | ||
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| optimizer = Adam(learning_rate=0.0002, b1=0.5) | ||
| loss_function = SquareLoss | ||
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| # Build the discriminator | ||
| self.encoder = self.build_encoder(optimizer, loss_function) | ||
| self.decoder = self.build_decoder(optimizer, loss_function) | ||
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| self.autoencoder = NeuralNetwork(optimizer=optimizer, loss=loss_function) | ||
| self.autoencoder.layers.extend(self.encoder.layers) | ||
| self.autoencoder.layers.extend(self.decoder.layers) | ||
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| print () | ||
| self.autoencoder.summary(name="Variational Autoencoder") | ||
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| def build_encoder(self, optimizer, loss_function): | ||
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| encoder = NeuralNetwork(optimizer=optimizer, loss=loss_function) | ||
| encoder.add(Dense(512, input_shape=(self.img_dim,))) | ||
| encoder.add(Activation('leaky_relu')) | ||
| encoder.add(BatchNormalization(momentum=0.8)) | ||
| encoder.add(Dense(256)) | ||
| encoder.add(Activation('leaky_relu')) | ||
| encoder.add(BatchNormalization(momentum=0.8)) | ||
| encoder.add(Dense(self.latent_dim)) | ||
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| return encoder | ||
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| def build_decoder(self, optimizer, loss_function): | ||
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| decoder = NeuralNetwork(optimizer=optimizer, loss=loss_function) | ||
| decoder.add(Dense(256, input_shape=(self.latent_dim,))) | ||
| decoder.add(Activation('leaky_relu')) | ||
| decoder.add(BatchNormalization(momentum=0.8)) | ||
| decoder.add(Dense(512)) | ||
| decoder.add(Activation('leaky_relu')) | ||
| decoder.add(BatchNormalization(momentum=0.8)) | ||
| decoder.add(Dense(self.img_dim)) | ||
| decoder.add(Activation('tanh')) | ||
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| return decoder | ||
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| def train(self, n_epochs, batch_size=128, save_interval=50): | ||
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| mnist = fetch_mldata('MNIST original') | ||
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| X = mnist.data | ||
| y = mnist.target | ||
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| # Rescale [-1, 1] | ||
| X = (X.astype(np.float32) - 127.5) / 127.5 | ||
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| for epoch in range(n_epochs): | ||
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| # Select a random half batch of images | ||
| idx = np.random.randint(0, X.shape[0], batch_size) | ||
| imgs = X[idx] | ||
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| # Train the Autoencoder | ||
| loss, _ = self.autoencoder.train_on_batch(imgs, imgs) | ||
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| # Display the progress | ||
| print ("%d [D loss: %f]" % (epoch, loss)) | ||
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| # If at save interval => save generated image samples | ||
| if epoch % save_interval == 0: | ||
| self.save_imgs(epoch, X) | ||
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| def save_imgs(self, epoch, X): | ||
| r, c = 5, 5 # Grid size | ||
| # Select a random half batch of images | ||
| idx = np.random.randint(0, X.shape[0], r*c) | ||
| imgs = X[idx] | ||
| # Generate images and reshape to image shape | ||
| gen_imgs = self.autoencoder.predict(imgs).reshape((-1, self.img_rows, self.img_cols)) | ||
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| # Rescale images 0 - 1 | ||
| gen_imgs = 0.5 * gen_imgs + 0.5 | ||
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| fig, axs = plt.subplots(r, c) | ||
| plt.suptitle("Autoencoder") | ||
| cnt = 0 | ||
| for i in range(r): | ||
| for j in range(c): | ||
| axs[i,j].imshow(gen_imgs[cnt,:,:], cmap='gray') | ||
| axs[i,j].axis('off') | ||
| cnt += 1 | ||
| fig.savefig("ae_%d.png" % epoch) | ||
| plt.close() | ||
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| if __name__ == '__main__': | ||
| ae = Autoencoder() | ||
| ae.train(n_epochs=200000, batch_size=64, save_interval=400) |