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gan.py
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from numpy import expand_dims
from numpy import zeros
from numpy import ones
from numpy import vstack
from numpy.random import randn
from numpy.random import randint
from tensorflow.keras.datasets import mnist
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Reshape
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import Conv2DTranspose
from tensorflow.keras.layers import LeakyReLU
from tensorflow.keras.layers import ReLU
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import BatchNormalization
from matplotlib import pyplot
def load_mnist():
(trainX, _), (_, _) = mnist.load_data()
X = expand_dims(trainX, axis=-1)
X = X.astype('float32')
X = X / 255.0
return X
def generator():
model = Sequential()
#nodes = 128 * 7 * 7
model.add(Dense(128*7*7, input_dim=100))
model.add(BatchNormalization())
model.add(ReLU())
model.add(Reshape((7, 7, 128)))
model.add(Conv2DTranspose(128, (4,4), strides=(2,2), padding='same'))
model.add(BatchNormalization())
model.add(ReLU())
model.add(Conv2DTranspose(128, (4,4), strides=(2,2), padding='same'))
model.add(BatchNormalization())
model.add(ReLU())
model.add(Conv2D(1, (7,7), activation='sigmoid', padding='same'))
return model
def discriminator(in_shape=(28,28,1)):
model = Sequential()
model.add(Conv2D(64, (3,3), strides=(2, 2), padding='same', input_shape=in_shape))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(.4))
model.add(Conv2D(64, (3,3), strides=(2, 2), padding='same'))
model.add(LeakyReLU(alpha=.2))
model.add(Dropout(.4))
# model.add(Conv2D(64, (3,3), strides=(2, 2), padding='same'))
# model.add(LeakyReLU(alpha=.2))
# model.add(Dropout(.4))
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
opt = Adam(lr=0.0002, beta_1=0.5)
model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])
return model
def gan(gen, disc):
disc.trainable = False
model = Sequential()
model.add(gen)
model.add(disc)
opt = Adam(lr=0.0002, beta_1=0.5)
model.compile(loss='binary_crossentropy', optimizer=opt)
return model
def gen_fake(gen, n_samples):
x = randn(100 * n_samples)
x = x.reshape(n_samples, 100)
x =gen.predict(x)
return x
def save_plot(examples, epoch, n=10):
for i in range(n * n):
pyplot.subplot(n, n, 1 + i)
pyplot.axis('off')
pyplot.imshow(examples[i, :, :, 0], cmap='gray_r')
filename = 'generated_plot_e%03d.png' % (epoch+1)
pyplot.savefig(filename)
pyplot.close()
def eval(epoch, gen, disc, dataset, n_samples=100):
x_real = dataset[randint(0, dataset.shape[0], n_samples)]
y_real = ones((n_samples, 1))
x_fake = gen_fake(gen, n_samples)
y_fake = zeros((n_samples, 1))
_, acc_real = disc.evaluate(x_real, y_real, verbose=0)
_, acc_fake = disc.evaluate(x_fake, y_fake, verbose=0)
print('>Accuracy real: %.0f%%, fake: %.0f%%' % (acc_real*100, acc_fake*100))
save_plot(x_fake, epoch)
filename = 'generator_model_%03d.h5' % (epoch + 1)
gen.save(filename)
def train():
disc = discriminator()
gen = generator()
ganm = gan(gen, disc)
dataset = load_mnist()
n_epochs=100
batch=256
run = int(dataset.shape[0] / batch)
half_batch = int(batch / 2)
for i in range(n_epochs):
for j in range(run):
x_real = dataset[randint(0, dataset.shape[0], half_batch)]
y_real = ones((half_batch, 1))
x_fake = gen_fake(gen, half_batch)
y_fake = zeros((half_batch, 1))
x, y = vstack((x_real, x_fake)), vstack((y_real, y_fake))
d_loss, _ = disc.train_on_batch(x, y)
x_gen = randn(100 *batch)
x_gen = x_gen.reshape(batch, 100)
y_gen = ones((batch, 1))
gan_loss = ganm.train_on_batch(x_gen, y_gen)
print('>%d, %d/%d, d=%.3f, g=%.3f' % (i+1, j+1, run, d_loss, gan_loss))
eval(i, gen, disc, dataset)
if __name__ == '__main__':
train()