def main():
reader1 = our_reader.Reader(cfg['vocab_size'], cfg['buckets'])
reader2 = cornell_reader.Reader(cfg)
reader1.build_dict(cfg['dictionary_name'], cfg['reversed_dictionary_name'], cfg['path']['train'])
reader1.read_data(cfg['path']['train'])
encoder_inputs_10 = [i for i in reader.dataset_enc if len(i) == 10]
decoder_inputs_10 = [i for i in reader.dataset_dec if len(i) == 10]
encoder_inputs__toks_10 = [i for i in reader.dataset_enc_tok if len(i) == 10]
decoder_inputs__toks_10 = [i for i in reader.dataset_dec_tok if len(i) == 10]
print("done")
model.create_placeholders()
identifier = 'mnistfull'
settings = utils.load_settings_from_file(identifier)
samples, pdf, labels = data_utils.get_samples_and_labels(settings)
locals().update(settings)
# json.dump(settings, open('./experiments/settings/' + identifier + '.txt', 'w'), indent=0)
data_path = './experiments/data/' + identifier + '.data.npy'
np.save(data_path, {'samples': samples, 'pdf': pdf, 'labels': labels})
print('Saved training data to', data_path)
# --- build model --- #
Z, X, CG, CD, CS = model.create_placeholders(batch_size, seq_length,
latent_dim, num_signals,
cond_dim)
discriminator_vars = [
'hidden_units_d', 'seq_length', 'cond_dim', 'batch_size', 'batch_mean'
]
discriminator_settings = dict((k, settings[k]) for k in discriminator_vars)
generator_vars = [
'hidden_units_g', 'seq_length', 'batch_size', 'num_generated_features',
'cond_dim', 'learn_scale'
]
generator_settings = dict((k, settings[k]) for k in generator_vars)
CGAN = (cond_dim > 0)
print(CGAN)
D_loss, G_loss, accuracy = model.GAN_loss(Z,
num_variables = samples.shape[2]
print('num_variables:', num_variables)
# --- save settings, data --- #
print('Ready to run with settings:')
for (k, v) in settings.items():
print(v, '\t', k)
# add the settings to local environment
# WARNING: at this point a lot of variables appear
locals().update(settings)
json.dump(settings,
open('./experiments/settings/' + identifier + '.txt', 'w'),
indent=0)
# --- build model --- #
# preparation: data placeholders and model parameters
Z, X, T = model.create_placeholders(batch_size, seq_length, latent_dim,
num_variables)
discriminator_vars = [
'hidden_units_d', 'seq_length', 'batch_size', 'batch_mean'
]
discriminator_settings = dict((k, settings[k]) for k in discriminator_vars)
generator_vars = ['hidden_units_g', 'seq_length', 'batch_size', 'learn_scale']
generator_settings = dict((k, settings[k]) for k in generator_vars)
generator_settings['num_signals'] = num_variables
# model: GAN losses
D_loss, G_loss = model.GAN_loss(Z, X, generator_settings,
discriminator_settings)
D_solver, G_solver, priv_accountant = model.GAN_solvers(
D_loss,
G_loss,
learning_rate,
# --- training sample --- #
# --- save settings, data --- #
print('Ready to run with settings:')
for (k, v) in settings.items():
print(v, '\t', k)
# add the settings to local environment
# WARNING: at this point a lot of variables appear
locals().update(settings)
json.dump(settings,
open('./experiments/settings/' + identifier + '.txt', 'w'),
indent=0)
# --- build model --- #
Z, X, T = model.create_placeholders(batch_size, seq_length, latent_dim,
num_signals)
discriminator_vars = [
'hidden_units_d', 'seq_length', 'batch_size', 'batch_mean'
]
discriminator_settings = dict((k, settings[k]) for k in discriminator_vars)
generator_vars = [
'hidden_units_g', 'seq_length', 'batch_size', 'num_generated_features',
'learn_scale'
]
generator_settings = dict((k, settings[k]) for k in generator_vars)
D_loss, G_loss = model.GAN_loss(Z, X, generator_settings,
discriminator_settings)
D_solver, G_solver, priv_accountant = model.GAN_solvers(
# WARNING: at this point a lot of variables appear
locals().update(settings)
json.dump(settings,
open('./experiments/settings/' + identifier + '.txt', 'w'),
indent=0)
epoch = 150
#if not data == 'load':
# data_path = './experiments/data/' + identifier + '.data.npy'
# np.save(data_path, {'samples': samples, 'pdf': pdf, 'labels': labels})
# print('Saved training data to', data_path)
# --- build model --- #
Z, X, CG, CD, CS = model.create_placeholders(batch_size, seq_length,
latent_dim,
num_generated_features, cond_dim)
discriminator_vars = [
'hidden_units_d', 'seq_length', 'cond_dim', 'batch_size', 'batch_mean',
'latent_dim'
]
discriminator_settings = dict((k, settings[k]) for k in discriminator_vars)
generator_vars = [
'hidden_units_g', 'seq_length', 'batch_size', 'latent_dim',
'num_generated_features', 'cond_dim', 'learn_scale'
]
generator_settings = dict((k, settings[k]) for k in generator_vars)
CGAN = (cond_dim > 0)
if CGAN: assert not predict_labels
cond_path = './experiments/cond_data/' + identifier + '.data.npy'
np.save(cond_path, {'cond_samples': cond_samples_train})
print('Saved cond images to ', cond_path)
# --- build model --- #
if generate_test:
eval.sine_plot(identifier, 250)
exit()
if cond_dim > 0:
num_signals = 1
if info:
latent_C, Z, X, CG, CD, CS, cond_sine = model.create_placeholders(
batch_size, seq_length, latent_dim, num_signals, cond_dim, info,
latent_C_dim)
else:
Z, X, CG, CD, CS, cond_sine = model.create_placeholders(
batch_size, seq_length, latent_dim, num_signals, cond_dim, info)
latent_C = None
discriminator_vars = [
'hidden_units_d', 'seq_length', 'cond_dim', 'batch_size', 'batch_mean'
]
discriminator_settings = dict((k, settings[k]) for k in discriminator_vars)
generator_vars = [
'hidden_units_g', 'seq_length', 'batch_size', 'num_generated_features',
'cond_dim', 'learn_scale'
]
generator_settings = dict((k, settings[k]) for k in generator_vars)
