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 gan_grid_idx(generator, discriminator,
batch_size=128, nb_z=20, reconstruct_fn=None):
nb_grid_params = nb_normalized_params()
z_shape = (batch_size, nb_z)
grid_params_shape = (nb_grid_params, )
g_graph = Graph()
g_graph.add_input('z', input_shape=z_shape[1:])
g_graph.add_input('grid_params', input_shape=grid_params_shape)
g_graph.add_node(generator, 'generator', inputs=['grid_params', 'z'])
g_graph.add_output('output', input='generator')
d_graph = asgraph(discriminator, input_name=GAN.d_input)
return GAN(g_graph, d_graph, z_shape, reconstruct_fn=reconstruct_fn)
def gan_grid_idx(generator,
discriminator,
batch_size=128,
nb_z=20,
reconstruct_fn=None):
nb_grid_params = nb_normalized_params()
z_shape = (batch_size, nb_z)
grid_params_shape = (nb_grid_params, )
g_graph = Graph()
g_graph.add_input('z', input_shape=z_shape[1:])
g_graph.add_input('grid_params', input_shape=grid_params_shape)
g_graph.add_node(generator, 'generator', inputs=['grid_params', 'z'])
g_graph.add_output('output', input='generator')
d_graph = asgraph(discriminator, input_name=GAN.d_input)
return GAN(g_graph, d_graph, z_shape, reconstruct_fn=reconstruct_fn)
def dcgan_pyramid_generator(nb_dcgan_units=64, z_dim=50, nb_fake=64):
nb_grid_params = nb_normalized_params()
g_input_dim = nb_grid_params + z_dim
g = Graph()
g.add_input(GAN.z_name, batch_input_shape=(nb_fake, z_dim))
g.add_input("gen_grid_params",
batch_input_shape=(nb_fake, nb_grid_params))
g.add_input("gen_grid_idx", batch_input_shape=(nb_fake, 1, 64, 64))
tag_mean = Dense(2, activation='relu', init=normal(0.005))
g.add_node(tag_mean, "tag_mean", inputs=[GAN.z_name, "gen_grid_params"])
g.add_node(dcgan_generator(nb_dcgan_units, input_dim=g_input_dim),
"dcgan_generator",
inputs=[GAN.z_name, "gen_grid_params"])
g.add_node(PyramidBlending(tag_mean), GAN.generator_name,
inputs=["dcgan_generator", "gen_grid_idx"], concat_axis=1)
return g
def dcgan_pyramid_generator(nb_dcgan_units=64, z_dim=50, nb_fake=64):
nb_grid_params = nb_normalized_params()
g_input_dim = nb_grid_params + z_dim
g = Graph()
g.add_input(GAN.z_name, batch_input_shape=(nb_fake, z_dim))
g.add_input("gen_grid_params", batch_input_shape=(nb_fake, nb_grid_params))
g.add_input("gen_grid_idx", batch_input_shape=(nb_fake, 1, 64, 64))
tag_mean = Dense(2, activation='relu', init=normal(0.005))
g.add_node(tag_mean, "tag_mean", inputs=[GAN.z_name, "gen_grid_params"])
g.add_node(dcgan_generator(nb_dcgan_units, input_dim=g_input_dim),
"dcgan_generator",
inputs=[GAN.z_name, "gen_grid_params"])
g.add_node(PyramidBlending(tag_mean),
GAN.generator_name,
inputs=["dcgan_generator", "gen_grid_idx"],
concat_axis=1)
return g