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from __future__ import print_function, division
from keras.datasets import mnist
from keras.layers import Input, Dense, Reshape, Flatten, Dropout, multiply
from keras.layers import BatchNormalization, Activation, Embedding, ZeroPadding2D
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam
import matplotlib.pyplot as plt
import numpy as np
class CGAN():
def __init__(self):
# 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.num_classes = 10
self.latent_dim = 100
optimizer = Adam(0.0002, 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 and the target label as input
# and generates the corresponding digit of that label
noise = Input(shape=(self.latent_dim,))
label = Input(shape=(1,))
img = self.generator([noise, label])
# For the combined model we will only train the generator
self.discriminator.trainable = False
# The discriminator takes generated image as input and determines validity
# and the label of that image
valid = self.discriminator([img, label])
# The combined model (stacked generator and discriminator)
# Trains generator to fool discriminator
self.combined = Model([noise, label], valid)
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,))
label = Input(shape=(1,), dtype='int32')
label_embedding = Flatten()(Embedding(self.num_classes, self.latent_dim)(label))
model_input = multiply([noise, label_embedding])
img = model(model_input)
return Model([noise, label], img)
def build_discriminator(self):
model = Sequential()
model.add(Dense(512, input_dim=np.prod(self.img_shape)))
model.add(LeakyReLU(alpha=0.2))
model.add(Dense(512))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.4))
model.add(Dense(512))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.4))
model.add(Dense(1, activation='sigmoid'))
#model.summary()
img = Input(shape=self.img_shape)
label = Input(shape=(1,), dtype='int32')
label_embedding = Flatten()(Embedding(self.num_classes, np.prod(self.img_shape))(label))
flat_img = Flatten()(img)
model_input = multiply([flat_img, label_embedding])
validity = model(model_input)
return Model([img, label], validity)
def train(self, epochs, batch_size=128, sample_interval=50, graph=False):
# Load the dataset
(X_train, y_train), (_, _) = mnist.load_data()
# Configure input
X_train = (X_train.astype(np.float32) - 127.5) / 127.5
X_train = np.expand_dims(X_train, axis=3)
y_train = y_train.reshape(-1, 1)
# 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 range(epochs):
# ---------------------
# Train Discriminator
# ---------------------
# Select a random half batch of images
idx = np.random.randint(0, X_train.shape[0], batch_size)
imgs, labels = X_train[idx], y_train[idx]
# Sample noise as generator input
noise = np.random.normal(0, 1, (batch_size, 100))
# Generate a half batch of new images
gen_imgs = self.generator.predict([noise, labels])
# Train the discriminator
d_loss_real = self.discriminator.train_on_batch([imgs, labels], valid)
d_loss_fake = self.discriminator.train_on_batch([gen_imgs, labels], fake)
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
# ---------------------
# Train Generator
# ---------------------
# Condition on labels
sampled_labels = np.random.randint(0, 10, batch_size).reshape(-1, 1)
# Train the generator
g_loss = self.combined.train_on_batch([noise, sampled_labels], valid)
# Plot the progress
#print ("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[1], g_loss))
if epoch % 500 == 0:
print(epoch)
loss[0][epoch] = d_loss[0]
loss[1][epoch] = g_loss
# If at save interval => save generated image samples
if epoch % sample_interval == 0:
self.sample_images(epoch)
if graph:
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 sample_images(self, epoch):
r, c = 2, 5
noise = np.random.normal(0, 1, (r * c, 100))
sampled_labels = np.arange(0, 10).reshape(-1, 1)
dummy_labels = np.zeros(32).reshape(-1, 1)
gen_imgs = self.generator.predict([noise, dummy_labels])
# Rescale images 0 - 1
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].set_title("Digit: %d" % sampled_labels[cnt])
axs[i,j].axis('off')
cnt += 1
fig.savefig("images/%d.png" % epoch)
plt.close()
def generate_data(self):
noise_train = np.random.normal(0, 1, (60000, 100))
noise_test = np.random.normal(0, 1, (10000, 100))
labels_train = np.zeros(60000).reshape(-1, 1)
labels_test = np.zeros(10000).reshape(-1, 1)
for i in range(10):
labels_train[i*600:] = i
labels_test[i*100:] = i
train_data = self.generator.predict([noise_train, labels_train])
test_data = self.generator.predict([noise_test, labels_test])
return train_data, test_data, labels_train, labels_test
'''
if __name__ == '__main__':
cgan = CGAN()
cgan.train(epochs=7000, batch_size=32, sample_interval=200)
train, test, tr_labels, te_labels = cgan.generate_data()
print(train.shape, test.shape)
'''
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