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September 23, 2019 10:25
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A Simple GAN implementation with Keras and Tensorflow
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from __future__ import print_function, division | |
from keras.datasets import mnist | |
from keras.layers import Input, Dense, Reshape, Flatten, Dropout | |
from keras.layers import BatchNormalization, Activation | |
from keras.layers.advanced_activations import LeakyReLU | |
from keras.models import Sequential, Model | |
from keras.optimizers import Adam | |
import matplotlib.pyplot as plt | |
import sys | |
import numpy as np | |
class GAN(): | |
def __init__(self): | |
self.img_rows = 28 | |
self.img_cols = 28 | |
self.channels = 1 | |
self.img_shape = (self.img_rows, self.img_cols, self.channels) | |
self.latent_dim = 100 | |
optimizer = Adam(0.0002, 0.5) | |
self.discriminator = self.build_discriminator() | |
self.discriminator.compile(loss='binary_crossentropy', | |
optimizer=optimizer, | |
metrics=['accuracy']) | |
self.generator = self.build_generator() | |
z = Input(shape=(self.latent_dim,)) | |
img = self.generator(z) | |
self.discriminator.trainable = False | |
validity = self.discriminator(img) | |
self.combined = Model(z, validity) | |
self.combined.compile(loss='binary_crossentropy', optimizer=optimizer) | |
def build_generator(self): | |
model = Sequential() | |
model.add(Dense(256, input_dim=self.latent_dim)) | |
model.add(LeakyReLU(alpha=0.2)) | |
model.add(BatchNormalization(momentum=0.8)) | |
model.add(Dense(512)) | |
model.add(LeakyReLU(alpha=0.2)) | |
model.add(BatchNormalization(momentum=0.8)) | |
model.add(Dense(1024)) | |
model.add(LeakyReLU(alpha=0.2)) | |
model.add(BatchNormalization(momentum=0.8)) | |
model.add(Dense(np.prod(self.img_shape), activation='tanh')) | |
model.add(Reshape(self.img_shape)) | |
model.summary() | |
noise = Input(shape=(self.latent_dim,)) | |
img = model(noise) | |
return Model(noise, img) | |
def build_discriminator(self): | |
model = Sequential() | |
model.add(Flatten(input_shape=self.img_shape)) | |
model.add(Dense(512)) | |
model.add(LeakyReLU(alpha=0.2)) | |
model.add(Dense(256)) | |
model.add(LeakyReLU(alpha=0.2)) | |
model.add(Dense(1, activation='sigmoid')) | |
model.summary() | |
img = Input(shape=self.img_shape) | |
validity = model(img) | |
return Model(img, validity) | |
def train(self, epochs, batch_size=128, sample_interval=50): | |
(X_train, _), (_, _) = mnist.load_data() | |
X_train = X_train / 127.5 - 1. | |
X_train = np.expand_dims(X_train, axis=3) | |
valid = np.ones((batch_size, 1)) | |
fake = np.zeros((batch_size, 1)) | |
for epoch in range(epochs): | |
idx = np.random.randint(0, X_train.shape[0], batch_size) | |
imgs = X_train[idx] | |
noise = np.random.normal(0, 1, (batch_size, self.latent_dim)) | |
gen_imgs = self.generator.predict(noise) | |
d_loss_real = self.discriminator.train_on_batch(imgs, valid) | |
d_loss_fake = self.discriminator.train_on_batch(gen_imgs, fake) | |
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake) | |
noise = np.random.normal(0, 1, (batch_size, self.latent_dim)) | |
g_loss = self.combined.train_on_batch(noise, valid) | |
print ("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[1], g_loss)) | |
if epoch % sample_interval == 0: | |
self.sample_images(epoch) | |
def sample_images(self, epoch): | |
r, c = 5, 5 | |
noise = np.random.normal(0, 1, (r * c, self.latent_dim)) | |
gen_imgs = self.generator.predict(noise) | |
gen_imgs = 0.5 * gen_imgs + 0.5 | |
fig, axs = plt.subplots(r, c) | |
cnt = 0 | |
for i in range(r): | |
for j in range(c): | |
axs[i,j].imshow(gen_imgs[cnt, :,:,0], cmap='gray') | |
axs[i,j].axis('off') | |
cnt += 1 | |
fig.savefig("images/%d.png" % epoch) | |
plt.close() | |
if __name__ == '__main__': | |
gan = GAN() | |
gan.train(epochs=100000, batch_size=132, sample_interval=10000) |
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