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from __future__ import print_function, division
import tensorflow as keras
import tensorflow as tf
from tensorflow.keras.datasets import mnist
from tensorflow.keras.layers import Input, Dense, Reshape, Flatten, Dropout
from tensorflow.keras.layers import BatchNormalization, Activation, ZeroPadding2D
from tensorflow.keras.layers import LeakyReLU
from tensorflow.keras.layers import UpSampling2D, Conv2D
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.optimizers import Adam
from lib.virtual_batch import VirtualBatchNormalization
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from tqdm import tqdm
import sys
import numpy as np
class DCGAN():
def __init__(self, conv_layers = 1, virtual_batch_normalization=False):
# Input shape
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
self.conv_layers = conv_layers
self.virtual_batch_normalization = virtual_batch_normalization
optimizer = Adam(0.002, 0.5)
# Build and compile the discriminator
self.discriminator = self.build_discriminator()
self.discriminator.compile(loss='binary_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
# Build the generator
self.generator = self.build_generator()
# The generator takes noise as input and generates imgs
z = Input(shape=(self.latent_dim,))
img = self.generator(z)
# For the combined model we will only train the generator
self.discriminator.trainable = False
# The discriminator takes generated images as input and determines validity
valid = self.discriminator(img)
# The combined model (stacked generator and discriminator)
# Trains the generator to fool the discriminator
self.combined = Model(z, valid)
self.combined.compile(loss='binary_crossentropy', optimizer=optimizer)
def build_generator(self):
model = Sequential()
model.add(Dense(128 * 7 * 7, activation="relu", input_dim=self.latent_dim))
model.add(Reshape((7, 7, 128)))
model.add(UpSampling2D())
for i in range(self.conv_layers):
model.add(Conv2D(128, kernel_size=3, padding="same"))
if self.virtual_batch_normalization:
model.add(VirtualBatchNormalization())
else:
model.add(BatchNormalization())
model.add(Activation("relu"))
model.add(UpSampling2D())
for i in range(self.conv_layers):
model.add(Conv2D(64, kernel_size=3, padding="same"))
if self.virtual_batch_normalization:
model.add(VirtualBatchNormalization())
else:
model.add(BatchNormalization())
model.add(Activation("relu"))
model.add(Conv2D(self.channels, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
#model.summary()
noise = Input(shape=(self.latent_dim,))
img = model(noise)
return Model(noise, img)
def build_discriminator(self):
model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=self.img_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(BatchNormalization())
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(BatchNormalization())
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
model.add(BatchNormalization())
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Flatten())
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, save_interval=50, VBN=False):
# Load the dataset
(X_train, _), (_, _) = mnist.load_data()
# Rescale -1 to 1
X_train = X_train / 127.5 - 1.
X_train = np.expand_dims(X_train, axis=3)
# Adversarial ground truths
valid = np.ones((batch_size, 1))
fake = np.zeros((batch_size, 1))
xaxis = np.arange(epochs)
loss = np.zeros((2,epochs))
for epoch in tqdm(range(epochs)):
# ---------------------
# Train Discriminator
# ---------------------
# Select a random half of images
idx = np.random.randint(0, X_train.shape[0], batch_size)
imgs = X_train[idx]
tf.keras.backend.get_session().run(tf.global_variables_initializer())
# Sample noise and generate a batch of new images
noise = np.random.normal(0, 1, (batch_size, self.latent_dim))
gen_imgs = self.generator.predict(noise)
# Train the discriminator (real classified as ones and generated as zeros)
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)
# ---------------------
# Train Generator
# ---------------------
# Train the generator (wants discriminator to mistake images as real)
g_loss = self.combined.train_on_batch(noise, valid)
# Plot the progress
#print ("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[1], g_loss))
loss[0][epoch] = d_loss[0]
loss[1][epoch] = g_loss
# If at save interval => save generated image samples
if epoch % save_interval == 0:
self.save_imgs(epoch)
plt.plot(xaxis,loss[0])
plt.plot(xaxis,loss[1])
plt.legend(('Discriminator', 'Generator'), loc='best')
plt.xlabel('Epoch')
plt.ylabel('Binary Crossentropy Loss')
def save_imgs(self, epoch):
r, c = 10, 10
noise = np.random.normal(0, 1, (r * c, self.latent_dim))
gen_imgs = self.generator.predict(noise)
# Rescale images 0 - 1
gen_imgs = 0.5 * gen_imgs + 0.5
fig, axs = plt.subplots(r, c)
gs = gridspec.GridSpec(r, c)
gs.update(wspace=0.05, hspace=0.05)
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/mnist_%d.png" % epoch)
plt.close()
'''
if __name__ == '__main__':
dcgan = DCGAN(virtual_batch_normalization=True)
dcgan.train(epochs=4000, batch_size=32, save_interval=50)
'''
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