def example_gan(adversarial_optimizer, path, opt_g, opt_d, nb_epoch, generator, discriminator, latent_dim, targets=gan_targets, loss="binary_crossentropy"):
csvpath = os.path.join(path, "history.csv")
if os.path.exists(csvpath):
print("Already exists: {}".format(csvpath))
return
print("Training: {}".format(csvpath))
# gan (x -> yfake, yreal), z is gaussian generated on GPU
# can also experiment with uniform_latent_sampling
d_g = discriminator(0)
d_d = discriminator(0.5)
generator.summary()
d_d.summary()
gan_g = simple_gan(generator, d_g, None)
gan_d = simple_gan(generator, d_d, None)
x = gan_g.inputs[1]
z = normal_latent_sampling((latent_dim,))(x)
# eliminate z from inputs
gan_g = Model([x], fix_names(gan_g([z, x]), gan_g.output_names))
gan_d = Model([x], fix_names(gan_d([z, x]), gan_d.output_names))
# build adversarial model
model = AdversarialModel(player_models=[gan_g, gan_d],
player_params=[generator.trainable_weights,
d_d.trainable_weights],
player_names=["generator", "discriminator"])
model.adversarial_compile(adversarial_optimizer=adversarial_optimizer,
player_optimizers=[opt_g, opt_d],
loss=loss)
# create callback to generate images
zsamples = np.random.normal(size=(10 * 10, latent_dim))
def generator_sampler():
xpred = generator.predict(zsamples)
xpred = dim_ordering_unfix(xpred.transpose((0, 2, 3, 1)))
return xpred.reshape((10, 10) + xpred.shape[1:])
generator_cb = ImageGridCallback(
os.path.join(path, "epoch-{:03d}.png"),
generator_sampler, cmap=None
)
callbacks = [generator_cb]
if K.backend() == "tensorflow":
callbacks.append(TensorBoard(log_dir=os.path.join(path, "logs"), histogram_freq=0, write_graph=True, write_images=True))
# train model
x_train, x_test = cifar10_data()
y = targets(x_train.shape[0])
y_test = targets(x_test.shape[0])
history = model.fit(x=x_train, y=y, validation_data=(x_test, y_test), callbacks=callbacks, epochs=nb_epoch, batch_size=32)
# save history to CSV
df = pd.DataFrame(history.history)
df.to_csv(csvpath)
# save models
generator.save(os.path.join(path, "generator.h5"))
d_d.save(os.path.join(path, "discriminator.h5"))
def example_gan(adversarial_optimizer, path, opt_g, opt_d, nb_epoch, generator, discriminator, latent_dim,
targets=gan_targets, loss='binary_crossentropy'):
csvpath = os.path.join(path, "history.csv")
if os.path.exists(csvpath):
print("Already exists: {}".format(csvpath))
return
print("Training: {}".format(csvpath))
# gan (x - > yfake, yreal), z is gaussian generated on GPU
# can also experiment with uniform_latent_sampling
generator.summary()
discriminator.summary()
gan = simple_gan(generator=generator,
discriminator=discriminator,
latent_sampling=normal_latent_sampling((latent_dim,)))
# 적대적 모델 정의
model = AdversarialModel(base_model=gan,
player_params=[generator.trainable_weights, discriminator.trainable_weights],
player_names=["generator", "discriminator"])
model.adversarial_compile(adversarial_optimizer=adversarial_optimizer,
player_optimizers=[opt_g, opt_d],
loss=loss)
# 이미지 생성을 위한 콜백 생성
zsamples = np.random.normal(size=(10 * 10, latent_dim))
def generator_sampler():
xpred = dim_ordering_unfix(generator.predict(zsamples)).transpose((0, 2, 3, 1))
return xpred.reshape((10, 10) + xpred.shape[1:])
generator_cb = ImageGridCallback(os.path.join(path, "epoch-{:03d}.png"), generator_sampler, cmap=None)
# 모델 학습
xtrain, xtest = cifar10_data()
y = targets(xtrain.shape[0])
ytest = targets(xtest.shape[0])
callbacks = [generator_cb]
K.set_image_dim_ordering('tf')
if K.backend() == "tensorflow":
os.makedirs(path + '/logs',exist_ok=True)
callbacks.append(
TensorBoard(log_dir=os.path.join(path, 'logs'), histogram_freq=0, write_graph=True, write_images=True))
history = fit(model, x=xtrain, y=y, validation_data=(xtest, ytest),
callbacks=callbacks, nb_epoch=nb_epoch,
batch_size=32)
# 히스토리를 CSV에 저장
df = pd.DataFrame(history.history)
df.to_csv(csvpath)
# 모델 저장
generator.save(os.path.join(path, "generator.h5"))
discriminator.save(os.path.join(path, "discriminator.h5"))
def example_gan(adversarial_optimizer,
path,
opt_g,
opt_d,
nb_epoch,
generator,
discriminator,
latent_dim,
targets=gan_targets,
loss='binary_crossentropy'):
# gan (x - > yfake, yreal)
# z generated on GPU
gan = simple_gan(generator, discriminator,
normal_latent_sampling((latent_dim, )))
# build adversarial model
model = AdversarialModel(base_model=gan,
player_params=[
generator.trainable_weights,
discriminator.trainable_weights
],
player_names=["generator", "discriminator"])
model.adversarial_compile(adversarial_optimizer=adversarial_optimizer,
player_optimizers=[opt_g, opt_d],
loss=loss)
# create callback to generate images
zsamples = np.random.normal(size=(10 * 10, latent_dim))
def generator_sampler():
return generator.predict(zsamples).reshape((10, 10, 28, 28))
generator_cb = ImageGridCallback(os.path.join(path, "epoch-{:03d}.png"),
generator_sampler)
# train model
xtrain, xtest = mnist_data()
# targets = gan_targets -> a 0/1 címkéket rendeli az adatokhoz
y = targets(xtrain.shape[0])
ytest = targets(xtest.shape[0])
callbacks = [generator_cb]
history = fit(model,
x=xtrain,
y=y,
validation_data=(xtest, ytest),
callbacks=callbacks,
nb_epoch=nb_epoch,
batch_size=32)
def example_gan(adversarial_optimizer, path, opt_g, opt_d, nb_epoch, generator, discriminator, latent_dim,
targets=gan_targets, loss='binary_crossentropy'):
csvpath = os.path.join(path, "history.csv")
if os.path.exists(csvpath):
print("Already exists: {}".format(csvpath))
return
print("Training: {}".format(csvpath))
# gan (x - > yfake, yreal), z generated on GPU
gan = simple_gan(generator, discriminator, normal_latent_sampling((latent_dim,)))
# print summary of models
generator.summary()
discriminator.summary()
gan.summary()
# build adversarial model
model = AdversarialModel(base_model=gan,
player_params=[generator.trainable_weights, discriminator.trainable_weights],
player_names=["generator", "discriminator"])
model.adversarial_compile(adversarial_optimizer=adversarial_optimizer,
player_optimizers=[opt_g, opt_d],
loss=loss)
# create callback to generate images
zsamples = np.random.normal(size=(10 * 10, latent_dim))
def generator_sampler():
return generator.predict(zsamples).reshape((10, 10, 28, 28))
generator_cb = ImageGridCallback(os.path.join(path, "epoch-{:03d}.png"), generator_sampler)
# train model
xtrain, xtest = mnist_data()
y = targets(xtrain.shape[0])
ytest = targets(xtest.shape[0])
callbacks = [generator_cb]
if K.backend() == "tensorflow":
callbacks.append(
TensorBoard(log_dir=os.path.join(path, 'logs'), histogram_freq=0, write_graph=True, write_images=True))
history = fit(model, x=xtrain, y=y, validation_data=(xtest, ytest), callbacks=callbacks, nb_epoch=nb_epoch,
batch_size=32)
# save history to CSV
df = pd.DataFrame(history.history)
df.to_csv(csvpath)
# save models
generator.save(os.path.join(path, "generator.h5"))
discriminator.save(os.path.join(path, "discriminator.h5"))
def main():
latent_dim = 100
input_shape = (1, 28, 28)
generator = model_generator()
discriminator = model_discriminator(input_shape=input_shape)
gan = simple_gan(generator, discriminator,
normal_latent_sampling((latent_dim, )))
generator.summary()
discriminator.summary()
gan.summary()
model = AdversarialModel(base_model=gan,
player_params=[
generator.trainable_weights,
discriminator.trainable_weights
],
player_names=["generator", "discriminator"])
model.adversarial_compile(
adversarial_optimizer=AdversarialOptimizerSimultaneous(),
player_optimizers=[Adam(1e-4, decay=1e-4),
Adam(1e-3, decay=1e-4)],
loss='binary_crossentropy')
generator_cb = ImageGridCallback(
"output/gan_convolutional/epoch-{:03d}.png",
generator_sampler(latent_dim, generator))
xtrain, xtest = mnist_data()
xtrain = dim_ordering_fix(xtrain.reshape((-1, 1, 28, 28)))
xtest = dim_ordering_fix(xtest.reshape((-1, 1, 28, 28)))
y = gan_targets(xtrain.shape[0])
ytest = gan_targets(xtest.shape[0])
history = model.fit(x=xtrain,
y=y,
validation_data=(xtest, ytest),
callbacks=[generator_cb],
nb_epoch=100,
batch_size=32)
df = pd.DataFrame(history.history)
df.to_csv("output/gan_convolutional/history.csv")
generator.save("output/gan_convolutional/generator.h5")
discriminator.save("output/gan_convolutional/discriminator.h5")
def run_gan(exp_dir, adversarial_optimizer, opt_g, opt_d, generator, discriminator, latent_dim,
targets=gan_targets, loss='binary_crossentropy'):
#print models
generator.summary()
discriminator.summary()
gan = simple_gan(generator=generator,
discriminator=discriminator,
latent_sampling=normal_latent_sampling((latent_dim,)))
# build adversarial model
model = AdversarialModel(base_model=gan, player_params=[generator.trainable_weights, discriminator.trainable_weights],
player_names=["generator", "discriminator"])
model.adversarial_compile(adversarial_optimizer=adversarial_optimizer, player_optimizers=[opt_g, opt_d], loss=loss)
# create callback to generate images
zsamples = np.random.normal(size=(10 * 10, latent_dim))
def generator_sampler():
xpred = dim_ordering_unfix(generator.predict(zsamples)).transpose((0, 2, 3, 1))
xpred = scale_value(xpred, [0.0, 1.0])
return xpred.reshape((10, 10) + xpred.shape[1:])
save_image_cb = ImageGridCallback('./dcgan-v2-images/' + exp_dir + '/epoch-{:03d}.png', generator_sampler, cmap=None)
save_model_cb = SaveModelWeights(generator, './dcgan-v2-model-weights/' + exp_dir)
# train model
xtrain, xtest = svhn_data()
y = targets(xtrain.shape[0])
ytest = targets(xtest.shape[0])
callbacks = [save_image_cb, save_model_cb]
#train model
epoch_start = 0
epoch_count = 100
history = fit(model, x=xtrain, y=y, validation_data=(xtest, ytest), callbacks=callbacks, nb_epoch=epoch_start + epoch_count,
batch_size=32, initial_epoch = epoch_start, shuffle=True)
# save history to CSV
df = pd.DataFrame(history.history)
df.to_csv('./dcgan-v2-images/' + exp_dir + '/history.csv')
#save final models
generator.save('./dcgan-v2-model-weights/' + exp_dir + '/generator.h5')
discriminator.save('./dcgan-v2-model-weights/' + exp_dir + '/discriminator.h5')
def initialize_callbacks(path, generator, discriminator, latent_dim):
def generator_sampler():
labels = np.array([int(t / nb_labels) for t in range(100)])
zsamples = np.random.normal(size=(10 * 10, latent_dim))
generated_images = generator.predict([zsamples, labels])
xpred = dim_ordering_unfix(generated_images).transpose((0, 2, 3, 1))
return xpred.reshape((10, 10) + xpred.shape[1:])
generator_cb = ImageGridCallback(os.path.join(path, "images",
"epoch-{:03d}.png"),
generator_sampler,
cmap=None)
tensor_board = TensorBoard(log_dir=os.path.join(path, 'logs'),
histogram_freq=0,
write_graph=True,
write_images=True)
model_saver = AdversarialModelSaver(path, generator, discriminator)
callbacks = [generator_cb, tensor_board, model_saver]
return callbacks
],
player_names=["generator", "discriminator"])
model.adversarial_compile(
adversarial_optimizer=AdversarialOptimizerSimultaneous(),
player_optimizers=[Adam(1e-4, decay=1e-4),
Adam(1e-3, decay=1e-4)],
loss='binary_crossentropy',
player_compile_kwargs=[{
'metrics': ['accuracy']
}, {
'metrics': ['accuracy']
}])
# train model
generator_cb = ImageGridCallback(
"output/gan_convolutional/epoch-{:03d}.png",
generator_sampler(latent_dim, generator))
fname = "base_hiver_2008.pklgz"
with gzip.open(fname, "rb") as fp:
dictio = pickle.load(fp)
data = dictio['SSTMW']
x = data[:, :92, :92].astype(np.float32)
xtrain = x[:-10]
xtest = x[-10:]
mini = np.min(xtrain.ravel())
maxi = np.max(xtrain.ravel())
xtrain = (xtrain - mini) / (maxi - mini)
xtest = (xtest - mini) / (maxi - mini)
def example_aae(path, adversarial_optimizer):
# z \in R^100
latent_dim = 100
# x \in R^{28x28}
input_shape = (28, 28)
# generator (z -> x)
generator = model_generator(latent_dim, input_shape)
# encoder (x ->z)
encoder = model_encoder(latent_dim, input_shape)
# autoencoder (x -> x')
autoencoder = Model(encoder.inputs, generator(encoder(encoder.inputs)))
# discriminator (z -> y)
discriminator = model_discriminator(latent_dim)
# assemple AAE
x = encoder.inputs[0]
z = encoder(x)
xpred = generator(z)
zreal = normal_latent_sampling((latent_dim, ))(x)
yreal = discriminator(zreal)
yfake = discriminator(z)
aae = Model(x, fix_names([xpred, yfake, yreal],
["xpred", "yfake", "yreal"]))
# print summary of models
generator.summary()
encoder.summary()
discriminator.summary()
autoencoder.summary()
# build adversarial model
generative_params = generator.trainable_weights + encoder.trainable_weights
model = AdversarialModel(
base_model=aae,
player_params=[generative_params, discriminator.trainable_weights],
player_names=["generator", "discriminator"])
model.adversarial_compile(
adversarial_optimizer=adversarial_optimizer,
player_optimizers=[Adam(1e-4, decay=1e-4),
Adam(1e-3, decay=1e-4)],
loss={
"yfake": "binary_crossentropy",
"yreal": "binary_crossentropy",
"xpred": "mean_squared_error"
},
player_compile_kwargs=[{
"loss_weights": {
"yfake": 1e-2,
"yreal": 1e-2,
"xpred": 1
}
}] * 2)
# load mnist data
xtrain, xtest = mnist_data()
# callback for image grid of generated samples
def generator_sampler():
zsamples = np.random.normal(size=(10 * 10, latent_dim))
return generator.predict(zsamples).reshape((10, 10, 28, 28))
generator_cb = ImageGridCallback(
os.path.join(path, "generated-epoch-{:03d}.png"), generator_sampler)
# callback for image grid of autoencoded samples
def autoencoder_sampler():
xsamples = n_choice(xtest, 10)
xrep = np.repeat(xsamples, 9, axis=0)
xgen = autoencoder.predict(xrep).reshape((10, 9, 28, 28))
xsamples = xsamples.reshape((10, 1, 28, 28))
samples = np.concatenate((xsamples, xgen), axis=1)
return samples
autoencoder_cb = ImageGridCallback(
os.path.join(path, "autoencoded-epoch-{:03d}.png"),
autoencoder_sampler)
# train network
# generator, discriminator; pred, yfake, yreal
n = xtrain.shape[0]
y = [
xtrain,
np.ones((n, 1)),
np.zeros((n, 1)), xtrain,
np.zeros((n, 1)),
np.ones((n, 1))
]
ntest = xtest.shape[0]
ytest = [
xtest,
np.ones((ntest, 1)),
np.zeros((ntest, 1)), xtest,
np.zeros((ntest, 1)),
np.ones((ntest, 1))
]
history = model.fit(x=xtrain,
y=y,
validation_data=(xtest, ytest),
callbacks=[generator_cb, autoencoder_cb],
nb_epoch=100,
batch_size=32)
# save history
df = pd.DataFrame(history.history)
df.to_csv(os.path.join(path, "history.csv"))
# save model
encoder.save(os.path.join(path, "encoder.h5"))
generator.save(os.path.join(path, "generator.h5"))
discriminator.save(os.path.join(path, "discriminator.h5"))
def example_bigan(path, adversarial_optimizer):
# z \in R^100
latent_dim = 25
# x \in R^{28x28}
input_shape = (28, 28)
# generator (z -> x)
generator = model_generator(latent_dim, input_shape)
# encoder (x ->z)
encoder = model_encoder(latent_dim, input_shape)
# autoencoder (x -> x')
autoencoder = Model(encoder.inputs, generator(encoder(encoder.inputs)))
# discriminator (x -> y)
discriminator_train, discriminator_test = model_discriminator(
latent_dim, input_shape)
# bigan (z, x - > yfake, yreal)
bigan_generator = simple_bigan(generator, encoder, discriminator_test)
bigan_discriminator = simple_bigan(generator, encoder, discriminator_train)
# z generated on GPU based on batch dimension of x
x = bigan_generator.inputs[1]
z = normal_latent_sampling((latent_dim, ))(x)
# eliminate z from inputs
bigan_generator = Model([x],
fix_names(bigan_generator([z, x]),
bigan_generator.output_names))
bigan_discriminator = Model([x],
fix_names(bigan_discriminator([z, x]),
bigan_discriminator.output_names))
generative_params = generator.trainable_weights + encoder.trainable_weights
# print summary of models
generator.summary()
encoder.summary()
discriminator_train.summary()
bigan_discriminator.summary()
autoencoder.summary()
# build adversarial model
model = AdversarialModel(
player_models=[bigan_generator, bigan_discriminator],
player_params=[
generative_params, discriminator_train.trainable_weights
],
player_names=["generator", "discriminator"])
model.adversarial_compile(
adversarial_optimizer=adversarial_optimizer,
player_optimizers=[Adam(1e-4, decay=1e-4),
Adam(1e-3, decay=1e-4)],
loss='binary_crossentropy')
# load mnist data
xtrain, xtest = mnist_data()
# callback for image grid of generated samples
def generator_sampler():
zsamples = np.random.normal(size=(10 * 10, latent_dim))
return generator.predict(zsamples).reshape((10, 10, 28, 28))
generator_cb = ImageGridCallback(
os.path.join(path, "generated-epoch-{:03d}.png"), generator_sampler)
# callback for image grid of autoencoded samples
def autoencoder_sampler():
xsamples = n_choice(xtest, 10)
xrep = np.repeat(xsamples, 9, axis=0)
xgen = autoencoder.predict(xrep).reshape((10, 9, 28, 28))
xsamples = xsamples.reshape((10, 1, 28, 28))
x = np.concatenate((xsamples, xgen), axis=1)
return x
autoencoder_cb = ImageGridCallback(
os.path.join(path, "autoencoded-epoch-{:03d}.png"),
autoencoder_sampler)
# train network
y = gan_targets(xtrain.shape[0])
ytest = gan_targets(xtest.shape[0])
history = model.fit(x=xtrain,
y=y,
validation_data=(xtest, ytest),
callbacks=[generator_cb, autoencoder_cb],
nb_epoch=100,
batch_size=32)
# save history
df = pd.DataFrame(history.history)
df.to_csv(os.path.join(path, "history.csv"))
# save model
encoder.save(os.path.join(path, "encoder.h5"))
generator.save(os.path.join(path, "generator.h5"))
discriminator_train.save(os.path.join(path, "discriminator.h5"))
def example_aae(path, adversarial_optimizer):
# z \in R^100
latent_dim = 256
units = 512
# x \in R^{28x28}
##input_shape = dim_ordering_shape((3, 128, 170))
input_shape = dim_ordering_shape((3, 32, 32))
#input_shape = (3,32,32)
###input_shape = (48,48,3)
# generator (z -> x)
generator = model_generator(latent_dim, units=units)
# encoder (x ->z)
encoder = model_encoder(latent_dim, input_shape, units=units)
# autoencoder (x -> x')
autoencoder = Model(encoder.inputs, generator(encoder(encoder.inputs)))
# discriminator (z -> y)
discriminator = model_discriminator(latent_dim, units=units)
# build AAE
x = encoder.inputs[0]
z = encoder(x)
xpred = generator(z)
zreal = normal_latent_sampling((latent_dim, ))(x)
yreal = discriminator(zreal)
yfake = discriminator(z)
aae = Model(x, fix_names([xpred, yfake, yreal],
["xpred", "yfake", "yreal"]))
# print summary of models
print("generator (z -> x)")
generator.summary()
print("encoder (x ->z)")
encoder.summary()
print("autoencoder (x -> x')")
discriminator.summary()
print("discriminator (z -> y)")
autoencoder.summary()
# build adversarial model
generative_params = generator.trainable_weights + encoder.trainable_weights
model = AdversarialModel(
base_model=aae,
player_params=[generative_params, discriminator.trainable_weights],
player_names=["generator", "discriminator"])
model.adversarial_compile(
adversarial_optimizer=adversarial_optimizer,
player_optimizers=[Adam(3e-4, decay=1e-4),
Adam(1e-3, decay=1e-4)],
loss={
"yfake": "binary_crossentropy",
"yreal": "binary_crossentropy",
"xpred": "mean_squared_error"
},
player_compile_kwargs=[{
"loss_weights": {
"yfake": 1e-1,
"yreal": 1e-1,
"xpred": 1e2
}
}] * 2)
# load mnist data
xtrain, xtest = benthoz_data()
print("xtrain shapes {}".format(xtrain.shape))
print("xtrain mean val {}".format(np.mean(xtrain)))
# callback for image grid of generated samples
def generator_sampler():
zsamples = np.random.normal(size=(10 * 10, latent_dim))
return dim_ordering_unfix(generator.predict(zsamples)).transpose(
(0, 2, 3, 1)).reshape((10, 10, 32, 32, 3))
#return generator.predict(zsamples).reshape((10, 10, 32, 32, 3))
generator_cb = ImageGridCallback(
os.path.join(path, "generated-epoch-{:03d}.png"), generator_sampler)
# callback for image grid of autoencoded samples
def autoencoder_sampler():
xsamples = n_choice(xtest, 10)
xrep = np.repeat(xsamples, 9, axis=0)
xgen = dim_ordering_unfix(autoencoder.predict(xrep)).reshape(
(10, 9, 3, 32, 32))
xsamples = dim_ordering_unfix(xsamples).reshape((10, 1, 3, 32, 32))
#xgen = autoencoder.predict(xrep).reshape((10, 9, 32, 32, 3))
#xsamples = xsamples.reshape((10, 1, 32, 32,3))
samples = np.concatenate((xsamples, xgen), axis=1)
samples = samples.transpose((0, 1, 3, 4, 2))
return samples
autoencoder_cb = ImageGridCallback(os.path.join(
path, "autoencoded-epoch-{:03d}.png"),
autoencoder_sampler,
cmap=None)
# train network
# generator, discriminator; pred, yfake, yreal
n = xtrain.shape[0]
print("num train samples {}".format(n))
y = [
xtrain,
np.ones((n, 1)),
np.zeros((n, 1)), xtrain,
np.zeros((n, 1)),
np.ones((n, 1))
]
ntest = xtest.shape[0]
ytest = [
xtest,
np.ones((ntest, 1)),
np.zeros((ntest, 1)), xtest,
np.zeros((ntest, 1)),
np.ones((ntest, 1))
]
history = fit(model,
x=xtrain,
y=y,
validation_data=(xtest, ytest),
callbacks=[generator_cb, autoencoder_cb],
nb_epoch=100,
batch_size=32)
#history = fit(model, x=xtrain, y=y, validation_data=(xtest, ytest),nb_epoch=100, batch_size=32)
# save history
df = pd.DataFrame(history.history)
df.to_csv(os.path.join(path, "history.csv"))
# save model
encoder.save(os.path.join(path, "encoder.h5"))
generator.save(os.path.join(path, "generator.h5"))
discriminator.save(os.path.join(path, "discriminator.h5"))
def example_faae(path, adversarial_optimizer):
latent_dim = 256
units = 512
input_shape = dim_ordering_shape((3, 32, 32))
# generator (z -> x)
generator = model_generator(latent_dim, units=units)
# encoder (x ->z)
encoder = model_encoder(latent_dim, input_shape, units=units)
# autoencoder (x -> x')
autoencoder = Model(encoder.inputs, generator(encoder(encoder.inputs)))
# discriminator (z -> y)
discriminator = model_discriminator()
# build FAAE
zreal = discriminator.inputs[0]
x = generator.inputs[0]
z = generator(x)
xpred = encoder(z)
yreal = discriminator(zreal)
yfake = discriminator(z)
aae = Model([zreal, x],
fix_names([xpred, yfake, yreal], ["xpred", "yfake", "yreal"]))
# print summary of models
generator.summary()
encoder.summary()
discriminator.summary()
#encoder.load_weights(os.path.join(path, "encoder.h5"))
#generator.load_weights(os.path.join(path, "generator.h5"))
#discriminator.load_weights(os.path.join(path, "discriminator.h5"))
# build adversarial model
generative_params = generator.trainable_weights + encoder.trainable_weights
model = AdversarialModel(
base_model=aae,
player_params=[generative_params, discriminator.trainable_weights],
player_names=["generator", "discriminator"])
model.adversarial_compile(
adversarial_optimizer=adversarial_optimizer,
player_optimizers=[Adam(3e-4, decay=1e-4),
Adam(1e-3, decay=1e-4)],
loss={
"yfake": "binary_crossentropy",
"yreal": "binary_crossentropy",
"xpred": "mean_squared_error"
},
player_compile_kwargs=[{
"loss_weights": {
"yfake": 1,
"yreal": 1,
"xpred": 8
}
}] * 2)
xtrain, xtest = cifar10_data()
def generator_sampler():
zsamples = np.random.randn(10 * 10, latent_dim)
return dim_ordering_unfix(generator.predict(zsamples)).transpose(
(0, 2, 3, 1)).reshape((10, 10, 32, 32, 3))
generator_cb = ImageGridCallback(
os.path.join(path, "generated-epoch-{:03d}.png"), generator_sampler)
def autoencoder_sampler():
xsamples = n_choice(xtest, 10)
xrep = np.repeat(xsamples, 9, axis=0)
xgen = dim_ordering_unfix(autoencoder.predict(xrep)).reshape(
(10, 9, 3, 32, 32))
xsamples = dim_ordering_unfix(xsamples).reshape((10, 1, 3, 32, 32))
samples = np.concatenate((xsamples, xgen), axis=1)
samples = samples.transpose((0, 1, 3, 4, 2))
return samples
autoencoder_cb = ImageGridCallback(os.path.join(
path, "autoencoded-epoch-{:03d}.png"),
autoencoder_sampler,
cmap=None)
train_datagen = gen_sample(128, 256, False)
test_datagen = gen_sample(32, 256, True)
history = model.fit_generator(train_datagen,
epochs=200,
steps_per_epoch=1000,
validation_data=test_datagen,
validation_steps=100,
callbacks=[generator_cb, autoencoder_cb])
# save history
df = pd.DataFrame(history.history)
df.to_csv(os.path.join(path, "history.csv"))
# save model
encoder.save(os.path.join(path, "encoder.h5"))
generator.save(os.path.join(path, "generator.h5"))
discriminator.save(os.path.join(path, "discriminator.h5"))
