def train_g64_d64_pyramid_preprocess_2layer():
nb_units = 64
generator_input_dim = 100
preprocess_input_dim = 50
preprocess_nb_hidden = 256
batch_size = 128
nb_batches_per_epoch = 100
nb_epoch = 200
output_dir = "models/train_g64_d64_pyramid_preprocess_2layer"
os.makedirs(output_dir, exist_ok=True)
g = dcgan_generator(nb_units, generator_input_dim)
g.load_weights("models/dcgan_g64_d64_fine_tune/generator_0060.hdf5")
p = Sequential()
p.add(
Dense(preprocess_nb_hidden,
activation='relu',
input_dim=preprocess_input_dim))
p.add(
Dense(g.layers[0].input_shape[1],
activation='relu',
input_dim=preprocess_input_dim))
g.trainable = False
p.add(g)
save = SaveModels({"pyramid_{epoch:04d}.hdf5": p},
every_epoch=20,
output_dir=output_dir)
nb_z_param = preprocess_input_dim - nb_normalized_params()
def generator():
for z, (param, grid_idx) in zip(z_generator((batch_size, nb_z_param)),
grids_lecture_generator(batch_size)):
yield np.concatenate([param, z], axis=1), grid_idx
print(next(generator())[0].shape)
print("Compiling...")
start = time.time()
p.compile('adam', to_keras_loss(pyramid_loss))
print("Done Compiling in {0:.2f}s".format(time.time() - start))
history = p.fit_generator(generator(),
nb_batches_per_epoch * batch_size,
nb_epoch,
verbose=1,
callbacks=[save])
with open(os.path.join(output_dir, "history.json"), 'w+') as f:
json.dump(history.history, f)
with open(os.path.join(output_dir, "network_config.json"), 'w+') as f:
f.write(p.to_json())
def train_g64_d64_fine_tune():
nb_units = 64
generator_input_dim = 100
nb_real = 64
nb_fake = 128 + nb_real
lr = 0.00002
beta_1 = 0.5
nb_batches_per_epoch = 100
nb_epoch = 60
output_dir = "models/dcgan_g64_d64_fine_tune"
hdf5_fname = "/home/leon/data/tags_plain_t6.hdf5"
g = dcgan_generator(nb_units, generator_input_dim)
d = dcgan_discriminator(nb_units)
g.load_weights("models/dcgan_g64_d64/generator.hdf5")
d.load_weights("models/dcgan_g64_d64/fix_discriminator.hdf5")
gan = sequential_to_gan(g,
d,
nb_real,
nb_fake,
nb_fake_for_gen=128,
nb_fake_for_dis=nb_real)
save = SaveModels(
{
"generator_{epoch:04d}.hdf5": g,
"discriminator_{epoch:04d}.hdf5": d
},
every_epoch=20,
output_dir=output_dir)
visual = VisualiseGAN(nb_samples=13**2,
output_dir=output_dir,
preprocess=lambda x: np.clip(x, -1, 1))
real_z_gen = zip_real_z(real_generator(hdf5_fname, nb_real, range=(-1, 1)),
z_generator((nb_fake, generator_input_dim)))
history = train_dcgan(gan,
Adam(lr, beta_1),
Adam(lr, beta_1),
real_z_gen,
nb_batches_per_epoch=nb_batches_per_epoch,
nb_epoch=nb_epoch,
callbacks=[save, visual])
with open(os.path.join(output_dir, "history.json"), 'w+') as f:
json.dump(history.history, f)
def train_g64_d64_dct():
nb_units = 64
generator_input_dim = 25
nb_real = 64
nb_fake = 96
lr = 0.0002
beta_1 = 0.5
nb_batches_per_epoch = 100
nb_epoch = 1000
output_dir = "models/dcgan_g64_d64_dct"
hdf5_fname = "/home/leon/data/tags_plain_t6.hdf5"
g = dcgan_generator(nb_units, generator_input_dim)
d = dcgan_discriminator(nb_units)
gan = sequential_to_gan(g, d, nb_real, nb_fake)
save = SaveModels({
"generator.hdf5": g,
"discriminator.hdf5": d
},
output_dir=output_dir)
visual = VisualiseGAN(nb_samples=13**2,
output_dir=output_dir,
preprocess=lambda x: np.clip(x, -1, 1))
real_z_gen = zip_real_z(real_generator(hdf5_fname, nb_real),
z_generator((nb_fake, generator_input_dim)))
history = train_dcgan(gan,
Adam(lr, beta_1),
Adam(lr, beta_1),
real_z_gen,
nb_batches_per_epoch=nb_batches_per_epoch,
nb_epoch=nb_epoch,
callbacks=[save, visual])
with open(os.path.join(output_dir, "history.json"), 'w+') as f:
json.dump(history.history, f)