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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
from sklearn.datasets import fetch_mldata
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
class Autoencoder():
"""An 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
optimizer = Adam(learning_rate=0.0002, b1=0.5)
loss_function = SquareLoss
self.encoder = self.build_encoder(optimizer, loss_function)
self.decoder = self.build_decoder(optimizer, loss_function)
self.autoencoder = NeuralNetwork(optimizer=optimizer, loss=loss_function)
self.autoencoder.layers.extend(self.encoder.layers)
self.autoencoder.layers.extend(self.decoder.layers)
print ()
self.autoencoder.summary(name="Variational Autoencoder")
def build_encoder(self, optimizer, loss_function):
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))
return encoder
def build_decoder(self, optimizer, loss_function):
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'))
return decoder
def train(self, n_epochs, batch_size=128, save_interval=50):
mnist = fetch_mldata('MNIST original')
X = mnist.data
y = mnist.target
# Rescale [-1, 1]
X = (X.astype(np.float32) - 127.5) / 127.5
for epoch in range(n_epochs):
# Select a random half batch of images
idx = np.random.randint(0, X.shape[0], batch_size)
imgs = X[idx]
# Train the Autoencoder
loss, _ = self.autoencoder.train_on_batch(imgs, imgs)
# Display the progress
print ("%d [D loss: %f]" % (epoch, loss))
# If at save interval => save generated image samples
if epoch % save_interval == 0:
self.save_imgs(epoch, X)
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))
# Rescale images 0 - 1
gen_imgs = 0.5 * gen_imgs + 0.5
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()
if __name__ == '__main__':
ae = Autoencoder()
ae.train(n_epochs=200000, batch_size=64, save_interval=400)
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