def train(options):
# Get logger
log = utils.get_logger(os.path.join(options['model_dir'], 'log.txt'))
options_file = open(os.path.join(options['dashboard_dir'], 'options'), 'w')
options_file.write(options['description'] + '\n')
options_file.write(
'DKL Weight: {}\nLog Sigma^2 clipped to: [{}, {}]\n\n'.format(
options['DKL_weight'], -options['sigma_clip'],
options['sigma_clip']))
for optn in options:
options_file.write(optn)
options_file.write(':\t')
options_file.write(str(options[optn]))
options_file.write('\n')
options_file.close()
# Dashboard Catalog
catalog = open(os.path.join(options['dashboard_dir'], 'catalog'), 'w')
catalog.write("""filename,type,name
options,plain,Options
train_loss.csv,csv,Train Loss
ll.csv,csv,Neg. Log-Likelihood
dec_log_sig_sq.csv,csv,Decoder Log Simga^2
dec_std_log_sig_sq.csv,csv,STD of Decoder Log Simga^2
dec_mean.csv,csv,Decoder Mean
dkl.csv,csv,DKL
enc_log_sig_sq.csv,csv,Encoder Log Sigma^2
enc_std_log_sig_sq.csv,csv,STD of Encoder Log Sigma^2
enc_mean.csv,csv,Encoder Mean
val_loss.csv,csv,Validation Loss
""")
catalog.flush()
train_log = open(os.path.join(options['dashboard_dir'], 'train_loss.csv'),
'w')
val_log = open(os.path.join(options['dashboard_dir'], 'val_loss.csv'), 'w')
dkl_log = open(os.path.join(options['dashboard_dir'], 'dkl.csv'), 'w')
ll_log = open(os.path.join(options['dashboard_dir'], 'll.csv'), 'w')
dec_sig_log = open(
os.path.join(options['dashboard_dir'], 'dec_log_sig_sq.csv'), 'w')
enc_sig_log = open(
os.path.join(options['dashboard_dir'], 'enc_log_sig_sq.csv'), 'w')
dec_std_sig_log = open(
os.path.join(options['dashboard_dir'], 'dec_std_log_sig_sq.csv'), 'w')
enc_std_sig_log = open(
os.path.join(options['dashboard_dir'], 'enc_std_log_sig_sq.csv'), 'w')
dec_mean_log = open(os.path.join(options['dashboard_dir'], 'dec_mean.csv'),
'w')
enc_mean_log = open(os.path.join(options['dashboard_dir'], 'enc_mean.csv'),
'w')
# val_sig_log = open(os.path.join(options['dashboard_dir'], 'val_log_sig_sq.csv'), 'w')
train_log.write('step,time,Train Loss\n')
val_log.write('step,time,Validation Loss\n')
dkl_log.write('step,time,DKL\n')
ll_log.write('step,time,-LL\n')
dec_sig_log.write('step,time,Decoder Log Sigma^2\n')
enc_sig_log.write('step,time,Encoder Log Sigma^2\n')
dec_std_sig_log.write('step,time,STD of Decoder Log Sigma^2\n')
enc_std_sig_log.write('step,time,STD of Encoder Log Sigma^2\n')
dec_mean_log.write('step,time,Decoder Mean\n')
enc_mean_log.write('step,time,Encoder Mean\n')
# Print options
utils.print_options(options, log)
# Load dataset ----------------------------------------------------------------------
# Train provider
if options['data_dir'] != 'MNIST':
num_data_points = len(
os.listdir(os.path.join(options['data_dir'], 'train', 'patches')))
num_data_points -= 2
train_provider = DataProvider(
num_data_points, options['batch_size'],
toolbox.ImageLoader(data_dir=os.path.join(options['data_dir'],
'train', 'patches'),
flat=True,
extension=options['file_extension']))
# Valid provider
num_data_points = len(
os.listdir(os.path.join(options['data_dir'], 'valid', 'patches')))
num_data_points -= 2
val_provider = DataProvider(
num_data_points, options['batch_size'],
toolbox.ImageLoader(data_dir=os.path.join(options['data_dir'],
'valid', 'patches'),
flat=True,
extension=options['file_extension']))
else:
train_provider = DataProvider(
55000, options['batch_size'],
toolbox.MNISTLoader(mode='train', flat=True))
val_provider = DataProvider(
5000, options['batch_size'],
toolbox.MNISTLoader(mode='validation', flat=True))
log.info('Data providers initialized.')
# Initialize model ------------------------------------------------------------------
with tf.device('/gpu:0'):
model = cupboard(options['model'])(
options['p_layers'], options['q_layers'],
np.prod(options['img_shape']), options['latent_dims'],
options['DKL_weight'], options['sigma_clip'], 'vanilla_vae')
log.info('Model initialized')
# Define inputs
model_input_batch = tf.placeholder(
tf.float32,
shape=[
options['batch_size'],
np.prod(np.array(options['img_shape']))
],
name='enc_inputs')
model_label_batch = tf.placeholder(
tf.float32,
shape=[options['batch_size'], options['num_classes']],
name='labels')
sampler_input_batch = tf.placeholder(
tf.float32,
shape=[options['batch_size'], options['latent_dims']],
name='dec_inputs')
log.info('Inputs defined')
# Define forward pass
cost_function = model(model_input_batch)
log.info('Forward pass graph built')
# Define sampler
sampler = model.build_sampler(sampler_input_batch)
log.info('Sampler graph built')
# Define optimizer
optimizer = tf.train.AdamOptimizer(learning_rate=options['lr'])
# optimizer = tf.train.GradientDescentOptimizer(learning_rate=options['lr'])
enc_std = tf.exp(tf.mul(0.5, model.enc_log_std_sq))
classifier = FC(
model.latent_dims,
options['num_classes'],
activation=None,
scale=0.01,
name='classifier_fc')(tf.add(
tf.mul(tf.random_normal([model.n_samples, model.latent_dims]),
enc_std), model.enc_mean))
classifier = tf.nn.softmax(classifier)
cost_function = -tf.mul(model_label_batch, tf.log(classifier))
cost_function = tf.reduce_sum(cost_function)
cost_function *= 1 / float(options['batch_size'])
train_step = optimizer.minimize(cost_function)
# Get gradients
grads = optimizer.compute_gradients(cost_function)
grads = [gv for gv in grads if gv[0] != None]
grad_tensors = [gv[0] for gv in grads]
# Clip gradients
clip_grads = [(tf.clip_by_norm(gv[0], 5.0,
name='grad_clipping'), gv[1])
for gv in grads]
# Update op
backpass = optimizer.apply_gradients(clip_grads)
log.info('Optimizer graph built')
# # Get gradients
# grad = optimizer.compute_gradients(cost_function)
# # Clip gradients
# clipped_grad = tf.clip_by_norm(grad, 5.0, name='grad_clipping')
# # Update op
# backpass = optimizer.apply_gradients(clipped_grad)
# Define init operation
init_op = tf.initialize_all_variables()
log.info('Variable initialization graph built')
# Define op to save and restore variables
saver = tf.train.Saver()
log.info('Save operation built')
# --------------------------------------------------------------------------
# Train loop ---------------------------------------------------------------
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
log.info('Session started')
# Initialize shared variables or restore
sess.run(init_op)
saver.restore(
sess, os.path.join(options['model_dir'], 'model_at_21000.ckpt'))
log.info('Shared variables restored')
# Define last losses to compute a running average
last_losses = np.zeros((10))
batch_abs_idx = 0
for epoch_idx in xrange(options['n_epochs']):
batch_rel_idx = 0
log.info('Epoch {}'.format(epoch_idx + 1))
for inputs, labels in train_provider:
batch_abs_idx += 1
batch_rel_idx += 1
result = sess.run(
# (cost_function, train_step, model.enc_std, model.enc_mean, model.encoder, model.dec_std, model.dec_mean, model.decoder, model.rec_loss, model.DKL),
# 0 1 2 3 4 5 6 7 8 9 10
[
cost_function, backpass, model.DKL, model.rec_loss,
model.dec_log_std_sq, model.enc_log_std_sq,
model.enc_mean, model.dec_mean, classifier
] + [gv[0] for gv in grads],
feed_dict={
model_input_batch: inputs,
model_label_batch: labels
})
cost = result[0]
if batch_abs_idx % 10 == 0:
train_log.write('{},{},{}\n'.format(
batch_abs_idx, '2016-04-22',
np.mean(
np.argmax(labels, axis=1) == np.argmax(result[8],
axis=1))))
dkl_log.write('{},{},{}\n'.format(batch_abs_idx,
'2016-04-22',
-np.mean(result[2])))
ll_log.write('{},{},{}\n'.format(batch_abs_idx,
'2016-04-22',
-np.mean(result[3])))
train_log.flush()
dkl_log.flush()
ll_log.flush()
dec_sig_log.write('{},{},{}\n'.format(
batch_abs_idx, '2016-04-22', np.mean(result[4])))
enc_sig_log.write('{},{},{}\n'.format(
batch_abs_idx, '2016-04-22', np.mean(result[5])))
# val_sig_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.mean(result[6])))
dec_sig_log.flush()
enc_sig_log.flush()
dec_std_sig_log.write('{},{},{}\n'.format(
batch_abs_idx, '2016-04-22', np.std(result[4])))
enc_std_sig_log.write('{},{},{}\n'.format(
batch_abs_idx, '2016-04-22', np.std(result[5])))
dec_mean_log.write('{},{},{}\n'.format(
batch_abs_idx, '2016-04-22', np.mean(result[7])))
enc_mean_log.write('{},{},{}\n'.format(
batch_abs_idx, '2016-04-22', np.mean(result[6])))
dec_std_sig_log.flush()
enc_std_sig_log.flush()
dec_mean_log.flush()
enc_mean_log.flush()
# val_sig_log.flush()
# print('\n\nENC_MEAN:')
# print(result[3])
# print('\n\nENC_STD:')
# print(result[2])
# print('\nDEC_MEAN:')
# print(result[6])
# print('\nDEC_STD:')
# print(result[5])
# print('\n\nENCODER WEIGHTS:')
# print(model._encoder.layers[0].weights['w'].eval())
# print('\n\DECODER WEIGHTS:')
# print(model._decoder.layers[0].weights['w'].eval())
# print(model._encoder.layers[0].weights['w'].eval())
# print(result[2])
# print(result[3])
# print(result[3])
# print(result[2])
# print(result[-2])
# print(result[-1])
# Check cost
if np.isnan(cost) or np.isinf(cost):
log.info('NaN detected')
for i in range(len(result)):
print("\n\nresult[%d]:" % i)
try:
print(np.any(np.isnan(result[i])))
except:
pass
print(result[i])
print(result[3].shape)
print(model._encoder.layers[0].weights['w'].eval())
print('\n\nAny:')
print(np.any(np.isnan(result[8])))
print(np.any(np.isnan(result[9])))
print(np.any(np.isnan(result[10])))
print(inputs)
return 1., 1., 1.
# Update last losses
last_losses = np.roll(last_losses, 1)
last_losses[0] = cost
# Display training information
if np.mod(epoch_idx, options['freq_logging']) == 0:
log.info(
'Epoch {:02}/{:02} Batch {:03} Current Loss: {:0>15.4f} Mean last losses: {:0>15.4f}'
.format(epoch_idx + 1, options['n_epochs'],
batch_abs_idx, float(cost),
np.mean(last_losses)))
log.info('Batch Mean LL: {:0>15.4f}'.format(
np.mean(result[3], axis=0)))
log.info('Batch Mean -DKL: {:0>15.4f}'.format(
np.mean(result[2], axis=0)))
# Save model
if np.mod(batch_abs_idx, options['freq_saving']) == 0:
saver.save(
sess,
os.path.join(options['model_dir'],
'model_at_%d.ckpt' % batch_abs_idx))
log.info('Model saved')
# Validate model
if np.mod(batch_abs_idx, options['freq_validation']) == 0:
valid_costs = []
seen_batches = 0
for val_batch, val_labels in val_provider:
val_result = sess.run(
[cost_function, classifier],
feed_dict={
model_input_batch: val_batch,
model_label_batch: val_labels
})
val_cost = val_result[0]
valid_costs.append(val_cost)
seen_batches += 1
if seen_batches == options['valid_batches']:
break
# Print results
log.info('Validation loss: {:0>15.4f}'.format(
float(np.mean(valid_costs))))
val_samples = sess.run(
sampler,
feed_dict={
sampler_input_batch:
MVN(np.zeros(options['latent_dims']),
np.diag(np.ones(options['latent_dims'])),
size=options['batch_size'])
})
val_log.write('{},{},{}\n'.format(
batch_abs_idx, '2016-04-22',
np.mean(
np.argmax(val_labels, axis=1) == np.argmax(
val_result[1], axis=1))))
val_log.flush()
save_ae_samples(catalog,
np.reshape(result[7],
[options['batch_size']] +
options['img_shape']),
np.reshape(inputs,
[options['batch_size']] +
options['img_shape']),
np.reshape(val_samples,
[options['batch_size']] +
options['img_shape']),
batch_abs_idx,
options['dashboard_dir'],
num_to_save=5,
save_gray=True)
# save_dash_samples(
# catalog,
# val_samples,
# batch_abs_idx,
# options['dashboard_dir'],
# flat_samples=True,
# img_shape=options['img_shape'],
# num_to_save=5
# )
save_samples(
val_samples,
int(batch_abs_idx / options['freq_validation']),
os.path.join(options['model_dir'], 'valid_samples'),
True, options['img_shape'], 5)
save_samples(
inputs,
int(batch_abs_idx / options['freq_validation']),
os.path.join(options['model_dir'], 'input_sanity'),
True,
options['img_shape'],
num_to_save=5)
save_samples(
result[7],
int(batch_abs_idx / options['freq_validation']),
os.path.join(options['model_dir'], 'rec_sanity'),
True,
options['img_shape'],
num_to_save=5)
log.info('End of epoch {}'.format(epoch_idx + 1))
def train(options):
# Get logger
log = utils.get_logger(os.path.join(options['model_dir'], 'log.txt'))
options_file = open(os.path.join(options['dashboard_dir'], 'options'), 'w')
options_file.write(options['description'] + '\n')
options_file.write(
'DKL Weight: {}\nLog Sigma^2 clipped to: [{}, {}]\n\n'.format(
options['DKL_weight'], -options['sigma_clip'],
options['sigma_clip']))
for optn in options:
options_file.write(optn)
options_file.write(':\t')
options_file.write(str(options[optn]))
options_file.write('\n')
options_file.close()
# Dashboard Catalog
catalog = open(os.path.join(options['dashboard_dir'], 'catalog'), 'w')
catalog.write("""filename,type,name
options,plain,Options
train_loss.csv,csv,Discriminator Cross-Entropy
ll.csv,csv,Neg. Log-Likelihood
dec_log_sig_sq.csv,csv,Decoder Log Simga^2
dec_std_log_sig_sq.csv,csv,STD of Decoder Log Simga^2
dec_mean.csv,csv,Decoder Mean
dkl.csv,csv,DKL
enc_log_sig_sq.csv,csv,Encoder Log Sigma^2
enc_std_log_sig_sq.csv,csv,STD of Encoder Log Sigma^2
enc_mean.csv,csv,Encoder Mean
val_loss.csv,csv,Validation Loss
""")
catalog.flush()
train_log = open(os.path.join(options['dashboard_dir'], 'train_loss.csv'),
'w')
val_log = open(os.path.join(options['dashboard_dir'], 'val_loss.csv'), 'w')
dkl_log = open(os.path.join(options['dashboard_dir'], 'dkl.csv'), 'w')
ll_log = open(os.path.join(options['dashboard_dir'], 'll.csv'), 'w')
dec_sig_log = open(
os.path.join(options['dashboard_dir'], 'dec_log_sig_sq.csv'), 'w')
enc_sig_log = open(
os.path.join(options['dashboard_dir'], 'enc_log_sig_sq.csv'), 'w')
dec_std_sig_log = open(
os.path.join(options['dashboard_dir'], 'dec_std_log_sig_sq.csv'), 'w')
enc_std_sig_log = open(
os.path.join(options['dashboard_dir'], 'enc_std_log_sig_sq.csv'), 'w')
dec_mean_log = open(os.path.join(options['dashboard_dir'], 'dec_mean.csv'),
'w')
enc_mean_log = open(os.path.join(options['dashboard_dir'], 'enc_mean.csv'),
'w')
# val_sig_log = open(os.path.join(options['dashboard_dir'], 'val_log_sig_sq.csv'), 'w')
train_log.write(
'step,time,Train CE (Training Vanilla),Train CE (Training Gen.),Train CE (Training Disc.)\n'
)
val_log.write(
'step,time,Validation CE (Training Vanilla),Validation CE (Training Gen.),Validation CE (Training Disc.)\n'
)
dkl_log.write(
'step,time,DKL (Training Vanilla),DKL (Training Gen.),DKL (Training Disc.)\n'
)
ll_log.write(
'step,time,-LL (Training Vanilla),-LL (Training Gen.),-LL (Training Disc.)\n'
)
dec_sig_log.write(
'step,time,Decoder Log Sigma^2 (Training Vanilla),Decoder Log Sigma^2 (Training Gen.),Decoder Log Sigma^2 (Training Disc.)\n'
)
enc_sig_log.write(
'step,time,Encoder Log Sigma^2 (Training Vanilla),Encoder Log Sigma^2 (Training Gen.),Encoder Log Sigma^2 (Training Disc.)\n'
)
dec_std_sig_log.write(
'step,time,STD of Decoder Log Sigma^2 (Training Vanilla),STD of Decoder Log Sigma^2 (Training Gen.),STD of Decoder Log Sigma^2 (Training Disc.)\n'
)
enc_std_sig_log.write(
'step,time,STD of Encoder Log Sigma^2 (Training Vanilla),STD of Encoder Log Sigma^2 (Training Gen.),STD of Encoder Log Sigma^2 (Training Disc.)\n'
)
dec_mean_log.write(
'step,time,Decoder Mean (Training Vanilla),Decoder Mean (Training Gen.),Decoder Mean (Training Disc.)\n'
)
enc_mean_log.write(
'step,time,Encoder Mean (Training Vanilla),Encoder Mean (Training Gen.),Encoder Mean (Training Disc.)\n'
)
# Print options
utils.print_options(options, log)
# Load dataset ----------------------------------------------------------------------
# Train provider
train_provider, val_provider, test_provider = get_providers(options,
log,
flat=True)
# Initialize model ------------------------------------------------------------------
with tf.device('/gpu:0'):
# Define inputs
model_input_batch = tf.placeholder(
tf.float32,
shape=[
options['batch_size'],
np.prod(np.array(options['img_shape']))
],
name='enc_inputs')
sampler_input_batch = tf.placeholder(
tf.float32,
shape=[options['batch_size'], options['latent_dims']],
name='dec_inputs')
log.info('Inputs defined')
# Define model
with tf.variable_scope('vae_scope'):
vae_model = cupboard('vanilla_vae')(
options['p_layers'], options['q_layers'],
np.prod(options['img_shape']), options['latent_dims'],
options['DKL_weight'], options['sigma_clip'], 'vae_model')
with tf.variable_scope('disc_scope'):
disc_model = cupboard('fixed_conv_disc')(
pickle.load(open(options['disc_params_path'], 'rb')),
options['num_feat_layers'],
name='disc_model')
vae_gan = cupboard('vae_gan')(vae_model,
disc_model,
options['disc_weight'],
options['img_shape'],
options['input_channels'],
'vae_scope',
'disc_scope',
name='vae_gan_model')
# Define Optimizers ---------------------------------------------------------------------
optimizer = tf.train.AdamOptimizer(learning_rate=options['lr'])
vae_backpass, disc_backpass, vanilla_backpass = vae_gan(
model_input_batch, sampler_input_batch, optimizer)
log.info('Optimizer graph built')
# --------------------------------------------------------------------------------------
# Define init operation
init_op = tf.initialize_all_variables()
log.info('Variable initialization graph built')
# Define op to save and restore variables
saver = tf.train.Saver()
log.info('Save operation built')
# --------------------------------------------------------------------------
# Train loop ---------------------------------------------------------------
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
log.info('Session started')
# Initialize shared variables or restore
if options['reload_all']:
saver.restore(sess, options['reload_file'])
log.info('Shared variables restored')
else:
sess.run(init_op)
log.info('Variables initialized')
if options['reload_vae']:
vae_model.reload_vae(options['vae_params_path'])
# Define last losses to compute a running average
last_losses = np.zeros((10))
batch_abs_idx = 0
D_to_G = options['D_to_G']
total_D2G = sum(D_to_G)
base = options['initial_G_iters'] + options['initial_D_iters']
for epoch_idx in xrange(options['n_epochs']):
batch_rel_idx = 0
log.info('Epoch {}'.format(epoch_idx + 1))
for inputs in train_provider:
if isinstance(inputs, tuple):
inputs = inputs[0]
batch_abs_idx += 1
batch_rel_idx += 1
if batch_abs_idx < options['initial_G_iters']:
backpass = vanilla_backpass
log_format_string = '{},{},{},,\n'
elif options['initial_G_iters'] <= batch_abs_idx < base:
backpass = disc_backpass
log_format_string = '{},{},,,{}\n'
else:
if (batch_abs_idx - base) % total_D2G < D_to_G[0]:
backpass = disc_backpass
log_format_string = '{},{},,,{}\n'
else:
backpass = vae_backpass
log_format_string = '{},{},,{},\n'
result = sess.run(
[
vae_gan.disc_CE, backpass, vae_gan._vae.DKL,
vae_gan._vae.rec_loss, vae_gan._vae.dec_log_std_sq,
vae_gan._vae.enc_log_std_sq, vae_gan._vae.enc_mean,
vae_gan._vae.dec_mean
],
feed_dict={
model_input_batch:
inputs,
sampler_input_batch:
MVN(np.zeros(options['latent_dims']),
np.diag(np.ones(options['latent_dims'])),
size=options['batch_size'])
})
cost = result[0]
if batch_abs_idx % 10 == 0:
train_log.write(
log_format_string.format(batch_abs_idx, '2016-04-22',
np.mean(last_losses)))
dkl_log.write(
log_format_string.format(batch_abs_idx, '2016-04-22',
-np.mean(result[2])))
ll_log.write(
log_format_string.format(batch_abs_idx, '2016-04-22',
-np.mean(result[3])))
train_log.flush()
dkl_log.flush()
ll_log.flush()
dec_sig_log.write(
log_format_string.format(batch_abs_idx, '2016-04-22',
np.mean(result[4])))
enc_sig_log.write(
log_format_string.format(batch_abs_idx, '2016-04-22',
np.mean(result[5])))
# val_sig_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.mean(result[6])))
dec_sig_log.flush()
enc_sig_log.flush()
dec_std_sig_log.write(
log_format_string.format(batch_abs_idx, '2016-04-22',
np.std(result[4])))
enc_std_sig_log.write(
log_format_string.format(batch_abs_idx, '2016-04-22',
np.std(result[5])))
dec_mean_log.write(
log_format_string.format(batch_abs_idx, '2016-04-22',
np.mean(result[7])))
enc_mean_log.write(
log_format_string.format(batch_abs_idx, '2016-04-22',
np.mean(result[6])))
dec_std_sig_log.flush()
enc_std_sig_log.flush()
dec_mean_log.flush()
enc_mean_log.flush()
# Check cost
if np.isnan(cost) or np.isinf(cost):
log.info('NaN detected')
for i in range(len(result)):
print("\n\nresult[%d]:" % i)
try:
print(np.any(np.isnan(result[i])))
except:
pass
print(result[i])
print(result[3].shape)
print(vae_gan._vae._encoder.layers[0].weights['w'].eval())
print('\n\nAny:')
print(np.any(np.isnan(result[8])))
print(np.any(np.isnan(result[9])))
print(np.any(np.isnan(result[10])))
print(inputs)
return 1., 1., 1.
# Update last losses
last_losses = np.roll(last_losses, 1)
last_losses[0] = cost
# Display training information
if np.mod(epoch_idx, options['freq_logging']) == 0:
log.info(
'Epoch {:02}/{:02} Batch {:03} Current Loss: {:0>15.4f} Mean last losses: {:0>15.4f}'
.format(epoch_idx + 1, options['n_epochs'],
batch_abs_idx, float(cost),
np.mean(last_losses)))
log.info('Batch Mean LL: {:0>15.4f}'.format(
np.mean(result[3], axis=0)))
log.info('Batch Mean -DKL: {:0>15.4f}'.format(
np.mean(result[2], axis=0)))
# Save model
if np.mod(batch_abs_idx, options['freq_saving']) == 0:
saver.save(
sess,
os.path.join(options['model_dir'],
'model_at_%d.ckpt' % batch_abs_idx))
log.info('Model saved')
save_dict = {}
# Save encoder params ------------------------------------------------------------------
for i in range(len(vae_gan._vae._encoder.layers)):
layer_dict = {
'input_dim':
vae_gan._vae._encoder.layers[i].input_dim,
'output_dim':
vae_gan._vae._encoder.layers[i].output_dim,
'act_fn':
vae_gan._vae._encoder.layers[i].activation,
'W':
vae_gan._vae._encoder.layers[i].weights['w'].eval(
),
'b':
vae_gan._vae._encoder.layers[i].weights['b'].eval(
)
}
save_dict['encoder'] = layer_dict
layer_dict = {
'input_dim': vae_gan._vae._enc_mean.input_dim,
'output_dim': vae_gan._vae._enc_mean.output_dim,
'act_fn': vae_gan._vae._enc_mean.activation,
'W': vae_gan._vae._enc_mean.weights['w'].eval(),
'b': vae_gan._vae._enc_mean.weights['b'].eval()
}
save_dict['enc_mean'] = layer_dict
layer_dict = {
'input_dim': vae_gan._vae._enc_log_std_sq.input_dim,
'output_dim': vae_gan._vae._enc_log_std_sq.output_dim,
'act_fn': vae_gan._vae._enc_log_std_sq.activation,
'W': vae_gan._vae._enc_log_std_sq.weights['w'].eval(),
'b': vae_gan._vae._enc_log_std_sq.weights['b'].eval()
}
save_dict['enc_log_std_sq'] = layer_dict
# Save decoder params ------------------------------------------------------------------
for i in range(len(vae_gan._vae._decoder.layers)):
layer_dict = {
'input_dim':
vae_gan._vae._decoder.layers[i].input_dim,
'output_dim':
vae_gan._vae._decoder.layers[i].output_dim,
'act_fn':
vae_gan._vae._decoder.layers[i].activation,
'W':
vae_gan._vae._decoder.layers[i].weights['w'].eval(
),
'b':
vae_gan._vae._decoder.layers[i].weights['b'].eval(
)
}
save_dict['decoder'] = layer_dict
layer_dict = {
'input_dim': vae_gan._vae._dec_mean.input_dim,
'output_dim': vae_gan._vae._dec_mean.output_dim,
'act_fn': vae_gan._vae._dec_mean.activation,
'W': vae_gan._vae._dec_mean.weights['w'].eval(),
'b': vae_gan._vae._dec_mean.weights['b'].eval()
}
save_dict['dec_mean'] = layer_dict
layer_dict = {
'input_dim': vae_gan._vae._dec_log_std_sq.input_dim,
'output_dim': vae_gan._vae._dec_log_std_sq.output_dim,
'act_fn': vae_gan._vae._dec_log_std_sq.activation,
'W': vae_gan._vae._dec_log_std_sq.weights['w'].eval(),
'b': vae_gan._vae._dec_log_std_sq.weights['b'].eval()
}
save_dict['dec_log_std_sq'] = layer_dict
pickle.dump(
save_dict,
open(
os.path.join(options['model_dir'],
'vae_dict_%d' % batch_abs_idx), 'wb'))
# Validate model
if np.mod(batch_abs_idx, options['freq_validation']) == 0:
vae_gan._vae._decoder.layers[0].weights['w'].eval()[:5, :5]
valid_costs = []
seen_batches = 0
for val_batch in val_provider:
if isinstance(val_batch, tuple):
val_batch = val_batch[0]
val_cost = sess.run(
vae_gan.disc_CE,
feed_dict={
model_input_batch:
val_batch,
sampler_input_batch:
MVN(np.zeros(options['latent_dims']),
np.diag(np.ones(options['latent_dims'])),
size=options['batch_size'])
})
valid_costs.append(val_cost)
seen_batches += 1
if seen_batches == options['valid_batches']:
break
# Print results
log.info('Validation loss: {:0>15.4f}'.format(
float(np.mean(valid_costs))))
val_samples = sess.run(
vae_gan.sampler,
feed_dict={
sampler_input_batch:
MVN(np.zeros(options['latent_dims']),
np.diag(np.ones(options['latent_dims'])),
size=options['batch_size'])
})
val_log.write(
log_format_string.format(batch_abs_idx, '2016-04-22',
np.mean(valid_costs)))
val_log.flush()
save_ae_samples(catalog,
np.reshape(result[7],
[options['batch_size']] +
options['img_shape']),
np.reshape(inputs,
[options['batch_size']] +
options['img_shape']),
np.reshape(val_samples,
[options['batch_size']] +
options['img_shape']),
batch_abs_idx,
options['dashboard_dir'],
num_to_save=5,
save_gray=True)
log.info('End of epoch {}'.format(epoch_idx + 1))
def train(options):
# Initialize model ------------------------------------------------------------------
# input_shape, input_channels, enc_params, dec_params, name=''
with tf.device('/gpu:0'):
if options['model'] == 'cnn_ae':
model = cupboard(options['model'])(
options['img_shape'],
options['input_channels'],
options['enc_params'],
options['dec_params'],
'cnn_ae'
)
# Define inputs
model_clean_input_batch = tf.placeholder(
tf.float32,
shape = [options['batch_size']] + options['img_shape'] + [options['input_channels']],
name = 'clean'
)
model_noisy_input_batch = tf.placeholder(
tf.float32,
shape = [options['batch_size']] + options['img_shape'] + [options['input_channels']],
name = 'noisy'
)
log.info('Inputs defined')
else:
model = cupboard(options['model'])(
np.prod(options['img_shape']) * options['input_channels'],
options['enc_params'],
options['dec_params'],
'ae'
)
# Define inputs
model_clean_input_batch = tf.placeholder(
tf.float32,
shape = [options['batch_size']] + [np.prod(options['img_shape']) * options['input_channels']],
name = 'clean'
)
model_noisy_input_batch = tf.placeholder(
tf.float32,
shape = [options['batch_size']] + [np.prod(options['img_shape']) * options['input_channels']],
name = 'noisy'
)
log.info('Inputs defined')
log.info('Model initialized')
# Define forward pass
print(model_clean_input_batch.get_shape())
print(model_noisy_input_batch.get_shape())
cost_function = model(model_clean_input_batch, model_noisy_input_batch)
log.info('Forward pass graph built')
log.info('Sampler graph built')
# Define optimizer
optimizer = tf.train.AdamOptimizer(
learning_rate=options['lr']
)
# optimizer = tf.train.GradientDescentOptimizer(learning_rate=options['lr'])
train_step = optimizer.minimize(cost_function)
log.info('Optimizer graph built')
# # Get gradients
# grad = optimizer.compute_gradients(cost_function)
# # Clip gradients
# clipped_grad = tf.clip_by_norm(grad, 5.0, name='grad_clipping')
# # Update op
# backpass = optimizer.apply_gradients(clipped_grad)
# Define init operation
init_op = tf.initialize_all_variables()
log.info('Variable initialization graph built')
# Define op to save and restore variables
saver = tf.train.Saver()
log.info('Save operation built')
# --------------------------------------------------------------------------
# Train loop ---------------------------------------------------------------
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
log.info('Session started')
# Initialize shared variables or restore
if options['reload']:
saver.restore(sess, os.path.join(options['model_dir'], 'model.ckpt'))
log.info('Shared variables restored')
else:
sess.run(init_op)
log.info('Shared variables initialized')
def train(options):
# Get logger
log = utils.get_logger(os.path.join(options['model_dir'], 'log.txt'))
options_file = open(os.path.join(options['dashboard_dir'], 'options'), 'w')
options_file.write(options['description'] + '\n')
options_file.write(
'Log Sigma^2 clipped to: [{}, {}]\n\n'.format(
-options['sigma_clip'],
options['sigma_clip']
)
)
for optn in options:
options_file.write(optn)
options_file.write(':\t')
options_file.write(str(options[optn]))
options_file.write('\n')
options_file.close()
# Dashboard Catalog
catalog = open(os.path.join(options['dashboard_dir'], 'catalog'), 'w')
catalog.write(
"""filename,type,name
options,plain,Options
train_loss.csv,csv,Discriminator Cross-Entropy
train_acc.csv,csv,Discriminator Accuracy
val_loss.csv,csv,Validation Cross-Entropy
val_acc.csv,csv,Validation Accuracy
"""
)
catalog.flush()
train_log = open(os.path.join(options['dashboard_dir'], 'train_loss.csv'), 'w')
val_log = open(os.path.join(options['dashboard_dir'], 'val_loss.csv'), 'w')
train_acc = open(os.path.join(options['dashboard_dir'], 'train_acc.csv'), 'w')
val_acc = open(os.path.join(options['dashboard_dir'], 'val_acc.csv'), 'w')
train_log.write('step,time,Train CE (Training Vanilla),Train CE (Training Gen.),Train CE (Training Disc.)\n')
val_log.write('step,time,Validation CE (Training Vanilla),Validation CE (Training Gen.),Validation CE (Training Disc.)\n')
train_acc.write('step,time,Train CE (Training Vanilla),Train CE (Training Gen.),Train CE (Training Disc.)\n')
val_acc.write('step,time,Validation CE (Training Vanilla),Validation CE (Training Gen.),Validation Acc. (Training Disc.)\n')
# Print options
utils.print_options(options, log)
# Load dataset ----------------------------------------------------------------------
# Train provider
train_provider, val_provider, test_provider = get_providers(options, log, flat=True)
# Initialize model ------------------------------------------------------------------
with tf.device('/gpu:0'):
# Define inputs -------------------------------------------------------------------------
real_batch = tf.placeholder(
tf.float32,
shape = [options['batch_size'], np.prod(np.array(options['img_shape']))],
name = 'real_inputs'
)
sampler_input_batch = tf.placeholder(
tf.float32,
shape = [options['batch_size'], options['latent_dims']],
name = 'noise_channel'
)
labels = tf.constant(
np.expand_dims(
np.concatenate(
(
np.ones(options['batch_size']),
np.zeros(options['batch_size'])
),
axis=0
).astype(np.float32),
axis=1
)
)
labels = tf.cast(labels, tf.float32)
log.info('Inputs defined')
# Define model --------------------------------------------------------------------------
with tf.variable_scope('gen_scope'):
generator = Sequential('generator')
generator += FullyConnected(options['latent_dims'], 60, tf.nn.tanh, name='fc_1')
generator += FullyConnected(60, 60, tf.nn.tanh, name='fc_2')
generator += FullyConnected(60, np.prod(options['img_shape']), tf.nn.tanh, name='fc_3')
sampler = generator(sampler_input_batch)
with tf.variable_scope('disc_scope'):
disc_model = cupboard('fixed_conv_disc')(
pickle.load(open(options['disc_params_path'], 'rb')),
options['num_feat_layers'],
name='disc_model'
)
disc_inputs = tf.concat(0, [real_batch, sampler])
disc_inputs = tf.reshape(
disc_inputs,
[disc_inputs.get_shape()[0].value] + options['img_shape'] + [options['input_channels']]
)
preds = disc_model(disc_inputs)
preds = tf.clip_by_value(preds, 0.00001, 0.99999)
# Disc Accuracy
disc_accuracy = (1 / float(labels.get_shape()[0].value)) * tf.reduce_sum(
tf.cast(
tf.equal(
tf.round(preds),
labels
),
tf.float32
)
)
# Define Losses -------------------------------------------------------------------------
# Discrimnator Cross-Entropy
disc_CE = (1 / float(labels.get_shape()[0].value)) * tf.reduce_sum(
-tf.add(
tf.mul(
labels,
tf.log(preds)
),
tf.mul(
1.0 - labels,
tf.log(1.0 - preds)
)
)
)
gen_loss = -tf.mul(
1.0 - labels,
tf.log(preds)
)
# Define Optimizers ---------------------------------------------------------------------
optimizer = tf.train.AdamOptimizer(
learning_rate=options['lr']
)
# Get Generator and Disriminator Trainable Variables
gen_train_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'gen_scope')
disc_train_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'disc_scope')
# Get generator gradients
grads = optimizer.compute_gradients(gen_loss, gen_train_vars)
grads = [gv for gv in grads if gv[0] != None]
clip_grads = [(tf.clip_by_norm(gv[0], 5.0, name='gen_grad_clipping'), gv[1]) for gv in grads]
gen_backpass = optimizer.apply_gradients(clip_grads)
# Get Dsicriminator gradients
grads = optimizer.compute_gradients(disc_CE, disc_train_vars)
grads = [gv for gv in grads if gv[0] != None]
clip_grads = [(tf.clip_by_norm(gv[0], 5.0, name='disc_grad_clipping'), gv[1]) for gv in grads]
disc_backpass = optimizer.apply_gradients(clip_grads)
log.info('Optimizer graph built')
# --------------------------------------------------------------------------------------
# Define init operation
init_op = tf.initialize_all_variables()
log.info('Variable initialization graph built')
# Define op to save and restore variables
saver = tf.train.Saver()
log.info('Save operation built')
# --------------------------------------------------------------------------
# Train loop ---------------------------------------------------------------
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
log.info('Session started')
# Initialize shared variables or restore
if options['reload_all']:
saver.restore(sess, options['reload_file'])
log.info('Shared variables restored')
else:
sess.run(init_op)
log.info('Variables initialized')
# Define last losses to compute a running average
last_losses = np.zeros((10))
last_accs = np.zeros((10))
disc_tracker = np.ones((5000))
batch_abs_idx = 0
D_to_G = options['D_to_G']
total_D2G = sum(D_to_G)
base = options['initial_G_iters'] + options['initial_D_iters']
# must_init = True
feat_params = pickle.load(open(options['disc_params_path'], 'rb'))
for epoch_idx in xrange(options['n_epochs']):
batch_rel_idx = 0
log.info('Epoch {}'.format(epoch_idx + 1))
for inputs in train_provider:
if isinstance(inputs, tuple):
inputs = inputs[0]
batch_abs_idx += 1
batch_rel_idx += 1
if batch_abs_idx < options['initial_G_iters']:
backpass = gen_backpass
log_format_string = '{},{},{},,\n'
elif options['initial_G_iters'] <= batch_abs_idx < base:
backpass = disc_backpass
log_format_string = '{},{},,,{}\n'
else:
# if np.mean(disc_tracker) < 0.95:
# disc_model._disc.layers[-2].re_init_weights(sess)
# disc_tracker = np.ones((5000))
if (batch_abs_idx - base) % total_D2G < D_to_G[0]:
# if must_init:
# # i = 0
# # for j in xrange(options['num_feat_layers']):
# # if feat_params[j]['layer_type'] == 'conv':
# # disc_model._disc.layers[i].re_init_weights(sess)
# # # print('@' * 1000)
# # # print(disc_model._disc.layers[i])
# # i += 1 # for dealing with activation function
# # elif feat_params[j]['layer_type'] == 'fc':
# # disc_model._disc.layers[i].re_init_weights(sess)
# # # print('@' * 1000)
# # # print(disc_model._disc.layers[i])
# # i += 1
# disc_model._disc.layers[-2].re_init_weights(sess)
# # print('@' * 1000)
# # print(disc_model._disc.layers[-2])
# must_init = False
backpass = disc_backpass
log_format_string = '{},{},,,{}\n'
else:
# must_init = True
backpass = gen_backpass
log_format_string = '{},{},,{},\n'
log_format_string = '{},{},{},,\n'
result = sess.run(
[
disc_CE,
backpass,
disc_accuracy
],
feed_dict = {
real_batch: inputs,
sampler_input_batch: MVN(
np.zeros(options['latent_dims']),
np.diag(np.ones(options['latent_dims'])),
size = options['batch_size']
)
}
)
cost = result[0]
if batch_abs_idx % 10 == 0:
train_log.write(log_format_string.format(batch_abs_idx, '2016-04-22', np.mean(last_losses)))
train_acc.write(log_format_string.format(batch_abs_idx, '2016-04-22', np.mean(last_accs)))
train_log.flush()
train_acc.flush()
# Check cost
if np.isnan(cost) or np.isinf(cost):
log.info('NaN detected')
# Update last losses
last_losses = np.roll(last_losses, 1)
last_losses[0] = cost
last_accs = np.roll(last_accs, 1)
last_accs[0] = result[-1]
disc_tracker = np.roll(disc_tracker, 1)
disc_tracker[0] = result[-1]
# Display training information
if np.mod(epoch_idx, options['freq_logging']) == 0:
log.info('Epoch {:02}/{:02} Batch {:03} Current Loss: {:0>15.4f} Mean last losses: {:0>15.4f}'.format(
epoch_idx + 1,
options['n_epochs'],
batch_abs_idx,
float(cost),
np.mean(last_losses)
))
log.info('Epoch {:02}/{:02} Batch {:03} Current Loss: {:0>15.4f} Mean last accuracies: {:0>15.4f}'.format(
epoch_idx + 1,
options['n_epochs'],
batch_abs_idx,
float(cost),
np.mean(last_accs)
))
# Save model
if np.mod(batch_abs_idx, options['freq_saving']) == 0:
saver.save(sess, os.path.join(options['model_dir'], 'model_at_%d.ckpt' % batch_abs_idx))
log.info('Model saved')
# Validate model
if np.mod(batch_abs_idx, options['freq_validation']) == 0:
valid_costs = []
valid_accs = []
seen_batches = 0
for val_batch in val_provider:
if isinstance(val_batch, tuple):
val_batch = val_batch[0]
result = sess.run(
[
disc_CE,
disc_accuracy
],
feed_dict = {
real_batch: val_batch,
sampler_input_batch: MVN(
np.zeros(options['latent_dims']),
np.diag(np.ones(options['latent_dims'])),
size = options['batch_size']
)
}
)
valid_costs.append(result[0])
valid_accs.append(result[-1])
seen_batches += 1
if seen_batches == options['valid_batches']:
break
# Print results
log.info('Validation loss: {:0>15.4f}'.format(
float(np.mean(valid_costs))
))
log.info('Validation accuracies: {:0>15.4f}'.format(
float(np.mean(valid_accs))
))
val_samples = sess.run(
sampler,
feed_dict = {
sampler_input_batch: MVN(
np.zeros(options['latent_dims']),
np.diag(np.ones(options['latent_dims'])),
size = options['batch_size']
)
}
)
val_log.write(log_format_string.format(batch_abs_idx, '2016-04-22', np.mean(valid_costs)))
val_acc.write(log_format_string.format(batch_abs_idx, '2016-04-22', np.mean(valid_accs)))
val_log.flush()
val_acc.flush()
save_ae_samples(
catalog,
np.ones([options['batch_size']]+options['img_shape']),
np.reshape(inputs, [options['batch_size']]+options['img_shape']),
np.reshape(val_samples, [options['batch_size']]+options['img_shape']),
batch_abs_idx,
options['dashboard_dir'],
num_to_save=5,
save_gray=True
)
log.info('End of epoch {}'.format(epoch_idx + 1))
def train(options):
# Get logger
log = utils.get_logger(os.path.join(options['model_dir'], 'log.txt'))
options_file = open(os.path.join(options['dashboard_dir'], 'options'), 'w')
options_file.write(options['description'] + '\n')
for optn in options:
options_file.write(optn)
options_file.write(':\t')
options_file.write(str(options[optn]))
options_file.write('\n')
options_file.close()
# Dashboard Catalog
catalog = open(os.path.join(options['dashboard_dir'], 'catalog'), 'w')
catalog.write(
"""filename,type,name
options,plain,Options
train_loss.csv,csv,Train Loss
train_acc.csv,csv,Train Accuracy
val_loss.csv,csv,Validation Loss
val_acc.csv,csv,Validation Accuracy
"""
)
catalog.flush()
train_log = open(os.path.join(options['dashboard_dir'], 'train_loss.csv'), 'w')
train_acc_log = open(os.path.join(options['dashboard_dir'], 'train_acc.csv'), 'w')
val_log = open(os.path.join(options['dashboard_dir'], 'val_loss.csv'), 'w')
val_acc_log = open(os.path.join(options['dashboard_dir'], 'val_acc.csv'), 'w')
train_log.write('step,time,Train Loss\n')
val_log.write('step,time,Validation Loss\n')
train_acc_log.write('step,time,Train Accuracy\n')
val_acc_log.write('step,time,Validation Accuracy\n')
# Print options
utils.print_options(options, log)
# Load dataset ----------------------------------------------------------------------
# Train provider
num_data_points = len(
os.listdir(
os.path.join(options['data_dir'], 'train', 'info')
)
)
num_data_points -= 2
train_provider = DataProvider(
num_data_points,
options['batch_size'],
toolbox.CIFARLoader(
data_dir = os.path.join(options['data_dir'], 'train'),
flat=False
)
)
# Valid provider
num_data_points = len(
os.listdir(
os.path.join(options['data_dir'], 'valid', 'info')
)
)
num_data_points -= 2
print(num_data_points)
val_provider = DataProvider(
num_data_points,
options['batch_size'],
toolbox.CIFARLoader(
data_dir = os.path.join(options['data_dir'], 'valid'),
flat=False
)
)
log.info('Data providers initialized.')
# Initialize model ------------------------------------------------------------------
with tf.device('/gpu:0'):
model = cupboard(options['model'])(
options['img_shape'],
options['input_channels'],
options['num_classes'],
options['conv_params'],
options['fc_params'],
'CIFAR_classifier'
)
log.info('Model initialized')
# Define inputs
input_batch = tf.placeholder(
tf.float32,
shape = [options['batch_size']] + options['img_shape'] + [options['input_channels']],
name = 'inputs'
)
label_batch = tf.placeholder(
tf.float32,
shape = [options['batch_size'], options['num_classes']],
name = 'labels'
)
log.info('Inputs defined')
# Define forward pass
cost_function, classifier = model(input_batch, label_batch)
log.info('Forward pass graph built')
# Define optimizer
optimizer = tf.train.AdamOptimizer(
learning_rate=options['lr']
)
train_step = optimizer.minimize(cost_function)
log.info('Optimizer graph built')
# # Get gradients
# grad = optimizer.compute_gradients(cost_function)
# # Clip gradients
# clipped_grad = tf.clip_by_norm(grad, 5.0, name='grad_clipping')
# # Update op
# backpass = optimizer.apply_gradients(clipped_grad)
# Define init operation
init_op = tf.initialize_all_variables()
log.info('Variable initialization graph built')
# Define op to save and restore variables
saver = tf.train.Saver()
log.info('Save operation built')
# --------------------------------------------------------------------------
# Train loop ---------------------------------------------------------------
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
log.info('Session started')
# Initialize shared variables or restore
if options['reload']:
saver.restore(sess, os.path.join(options['model_dir'], 'model.ckpt'))
log.info('Shared variables restored')
else:
sess.run(init_op)
log.info('Shared variables initialized')
# Define last losses to compute a running average
last_losses = np.zeros((10))
last_accs = np.zeros((10))
batch_abs_idx = 0
for epoch_idx in xrange(options['n_epochs']):
batch_rel_idx = 0
log.info('Epoch {}'.format(epoch_idx + 1))
for inputs, labels in train_provider:
batch_abs_idx += 1
batch_rel_idx += 1
results = sess.run(
[cost_function, classifier, train_step],
feed_dict = {
input_batch: inputs,
label_batch: labels
}
)
cost = results[0]
# Check cost
if np.isnan(cost) or np.isinf(cost):
log.info('NaN detected')
return 1., 1., 1.
accuracy = np.mean(np.argmax(results[1], axis=1) == np.argmax(labels, axis=1))
# Update last losses
last_losses = np.roll(last_losses, 1)
last_losses[0] = cost
last_accs = np.roll(last_accs, 1)
last_accs[0] = accuracy
if batch_abs_idx % 10 == 0:
train_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.mean(last_losses)))
train_acc_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.mean(last_accs)))
train_log.flush()
train_acc_log.flush()
# Display training information
if np.mod(epoch_idx, options['freq_logging']) == 0:
log.info('Epoch {:02}/{:02} Batch {:03} Current Loss: {:0>15.4f} Mean last losses: {:0>15.4f}'.format(
epoch_idx + 1,
options['n_epochs'],
batch_abs_idx,
float(cost),
np.mean(last_losses)
))
log.info('Epoch {:02}/{:02} Batch {:03} Current Accuracy: {:0>15.4f}'.format(
epoch_idx + 1,
options['n_epochs'],
batch_abs_idx,
np.mean(last_accs)
))
# Save model
if np.mod(batch_abs_idx, options['freq_saving']) == 0:
saver.save(sess, os.path.join(options['model_dir'], 'model_at_%d.ckpt' % batch_abs_idx))
save_dict = []
for c_ind in xrange(0, len(model._classifier_conv.layers), 2):
layer_dict = {
'n_filters_in': model._classifier_conv.layers[c_ind].n_filters_in,
'n_filters_out': model._classifier_conv.layers[c_ind].n_filters_out,
'input_dim': model._classifier_conv.layers[c_ind].input_dim,
'filter_dim': model._classifier_conv.layers[c_ind].filter_dim,
'strides': model._classifier_conv.layers[c_ind].strides,
'padding': model._classifier_conv.layers[c_ind].padding,
}
save_dict.append(layer_dict)
pickle.dump(save_dict, open(os.path.join(options['model_dir'], 'class_dict_%d' % batch_abs_idx), 'wb'))
log.info('Model saved')
# Save params for feature vae training later
# conv_feat = deepcopy(model._classifier_conv)
# for lay_ind in range(0,len(conv_feat.layers),2):
# conv_feat[lay_ind].weights['W'] = tf.constant(conv_feat[lay_ind].weights['W'].eval())
# conv_feat[lay_ind].weights['b'] = tf.constant(conv_feat[lay_ind].weights['b'].eval())
# pickle(conv_feat, open(os.path.join(options['model_dir'], 'classifier_conv_feat_%d' % batch_abs_idx), 'wb'))
# Validate model
if np.mod(batch_abs_idx, options['freq_validation']) == 0:
valid_costs = []
val_accuracies = []
seen_batches = 0
for val_batch, val_label in val_provider:
# Break if 10 batches seen for now
if seen_batches == options['valid_batches']:
break
val_results = sess.run(
[cost_function, classifier],
feed_dict = {
input_batch: val_batch,
label_batch: val_label
}
)
val_cost = val_results[0]
valid_costs.append(val_cost)
seen_batches += 1
val_accuracies.append(np.mean(np.argmax(val_results[1], axis=1) == np.argmax(val_label, axis=1)))
# Print results
log.info('Validation loss: {:0>15.4f}'.format(
float(np.mean(valid_costs))
))
log.info('Validation Accuracy: {:0>15.4f}'.format(
np.mean(val_accuracies)
))
val_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', float(np.mean(valid_costs))))
val_acc_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.mean(val_accuracies)))
val_log.flush()
val_acc_log.flush()
log.info('End of epoch {}'.format(epoch_idx + 1))
def train(options):
# Get logger
log = utils.get_logger(os.path.join(options['model_dir'], 'log.txt'))
options_file = open(os.path.join(options['dashboard_dir'], 'options'), 'w')
options_file.write(options['description'] + '\n')
options_file.write(
'DKL Weight: {}\nLog Sigma^2 clipped to: [{}, {}]\n\n'.format(
options['DKL_weight'], -options['sigma_clip'],
options['sigma_clip']))
for optn in options:
options_file.write(optn)
options_file.write(':\t')
options_file.write(str(options[optn]))
options_file.write('\n')
options_file.close()
# Dashboard Catalog
catalog = open(os.path.join(options['dashboard_dir'], 'catalog'), 'w')
catalog.write("""filename,type,name
options,plain,Options
train_loss.csv,csv,Train Loss
ll.csv,csv,Neg. Log-Likelihood
dec_log_sig_sq.csv,csv,Decoder Log Simga^2
dec_std_log_sig_sq.csv,csv,STD of Decoder Log Simga^2
dec_mean.csv,csv,Decoder Mean
dkl.csv,csv,DKL
enc_log_sig_sq.csv,csv,Encoder Log Sigma^2
enc_std_log_sig_sq.csv,csv,STD of Encoder Log Sigma^2
enc_mean.csv,csv,Encoder Mean
val_loss.csv,csv,Validation Loss
""")
catalog.flush()
train_log = open(os.path.join(options['dashboard_dir'], 'train_loss.csv'),
'w')
val_log = open(os.path.join(options['dashboard_dir'], 'val_loss.csv'), 'w')
dkl_log = open(os.path.join(options['dashboard_dir'], 'dkl.csv'), 'w')
ll_log = open(os.path.join(options['dashboard_dir'], 'll.csv'), 'w')
dec_sig_log = open(
os.path.join(options['dashboard_dir'], 'dec_log_sig_sq.csv'), 'w')
enc_sig_log = open(
os.path.join(options['dashboard_dir'], 'enc_log_sig_sq.csv'), 'w')
dec_std_sig_log = open(
os.path.join(options['dashboard_dir'], 'dec_std_log_sig_sq.csv'), 'w')
enc_std_sig_log = open(
os.path.join(options['dashboard_dir'], 'enc_std_log_sig_sq.csv'), 'w')
dec_mean_log = open(os.path.join(options['dashboard_dir'], 'dec_mean.csv'),
'w')
enc_mean_log = open(os.path.join(options['dashboard_dir'], 'enc_mean.csv'),
'w')
# val_sig_log = open(os.path.join(options['dashboard_dir'], 'val_log_sig_sq.csv'), 'w')
train_log.write('step,time,Train Loss\n')
val_log.write('step,time,Validation Loss\n')
dkl_log.write('step,time,DKL\n')
ll_log.write('step,time,-LL\n')
dec_sig_log.write('step,time,Decoder Log Sigma^2\n')
enc_sig_log.write('step,time,Encoder Log Sigma^2\n')
dec_std_sig_log.write('step,time,STD of Decoder Log Sigma^2\n')
enc_std_sig_log.write('step,time,STD of Encoder Log Sigma^2\n')
dec_mean_log.write('step,time,Decoder Mean\n')
enc_mean_log.write('step,time,Encoder Mean\n')
# Print options
utils.print_options(options, log)
# Load dataset ----------------------------------------------------------------------
# Train provider
train_provider, val_provider, test_provider = get_providers(options,
log,
flat=True)
# Initialize model ------------------------------------------------------------------
with tf.device('/gpu:0'):
model = cupboard(options['model'])(
options['p_layers'], options['q_layers'],
np.prod(options['img_shape']), options['latent_dims'],
options['DKL_weight'], options['sigma_clip'], 'vanilla_vae')
log.info('Model initialized')
# Define inputs
model_input_batch = tf.placeholder(
tf.float32,
shape=[
options['batch_size'],
np.prod(np.array(options['img_shape']))
],
name='enc_inputs')
sampler_input_batch = tf.placeholder(
tf.float32,
shape=[options['batch_size'], options['latent_dims']],
name='dec_inputs')
log.info('Inputs defined')
# Define forward pass
cost_function = model(model_input_batch)
log.info('Forward pass graph built')
# Define sampler
sampler = model.build_sampler(sampler_input_batch)
log.info('Sampler graph built')
# Define optimizer
optimizer = tf.train.AdamOptimizer(learning_rate=options['lr'])
# optimizer = tf.train.GradientDescentOptimizer(learning_rate=options['lr'])
train_step = optimizer.minimize(cost_function)
# Get gradients
grads = optimizer.compute_gradients(cost_function)
grad_tensors = [gv[0] for gv in grads]
# Clip gradients
clip_grads = [(tf.clip_by_norm(gv[0], 5.0,
name='grad_clipping'), gv[1])
for gv in grads]
# Update op
backpass = optimizer.apply_gradients(clip_grads)
log.info('Optimizer graph built')
# # Get gradients
# grad = optimizer.compute_gradients(cost_function)
# # Clip gradients
# clipped_grad = tf.clip_by_norm(grad, 5.0, name='grad_clipping')
# # Update op
# backpass = optimizer.apply_gradients(clipped_grad)
# Define init operation
init_op = tf.initialize_all_variables()
log.info('Variable initialization graph built')
# Define op to save and restore variables
saver = tf.train.Saver()
log.info('Save operation built')
# --------------------------------------------------------------------------
# Train loop ---------------------------------------------------------------
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
log.info('Session started')
# Initialize shared variables or restore
if options['reload']:
saver.restore(sess, options['reload_file'])
log.info('Shared variables restored')
# test_LL_and_DKL(sess, test_provider, model.DKL, model.rec_loss, options, model_input_batch)
# return
# if options['data_dir'] == 'MNIST':
# mean_img = np.zeros(np.prod(options['img_shape']))
# std_img = np.ones(np.prod(options['img_shape']))
# else:
# mean_img = np.load(os.path.join(options['data_dir'], 'mean' + options['extension']))
# std_img = np.load(os.path.join(options['data_dir'], 'std' + options['extension']))
# visualize(model.sampler_mean, sess, model.dec_mean, model.dec_log_std_sq, sampler, sampler_input_batch,
# model_input_batch, model.enc_mean, model.enc_log_std_sq,
# train_provider, val_provider, options, catalog, mean_img, std_img)
# return
else:
sess.run(init_op)
log.info('Shared variables initialized')
# Define last losses to compute a running average
last_losses = np.zeros((10))
batch_abs_idx = 0
for epoch_idx in xrange(options['n_epochs']):
batch_rel_idx = 0
log.info('Epoch {}'.format(epoch_idx + 1))
for inputs in train_provider:
if isinstance(inputs, tuple):
inputs = inputs[0]
batch_abs_idx += 1
batch_rel_idx += 1
result = sess.run(
# (cost_function, train_step, model.enc_std, model.enc_mean, model.encoder, model.dec_std, model.dec_mean, model.decoder, model.rec_loss, model.DKL),
# 0 1 2 3 4 5 6 7 8 9 10
[
cost_function, backpass, model.DKL, model.rec_loss,
model.dec_log_std_sq, model.enc_log_std_sq,
model.enc_mean, model.dec_mean
],
feed_dict={model_input_batch: inputs})
cost = result[0]
if batch_abs_idx % 10 == 0:
train_log.write('{},{},{}\n'.format(
batch_abs_idx, '2016-04-22', np.mean(last_losses)))
dkl_log.write('{},{},{}\n'.format(batch_abs_idx,
'2016-04-22',
-np.mean(result[2])))
ll_log.write('{},{},{}\n'.format(batch_abs_idx,
'2016-04-22',
-np.mean(result[3])))
train_log.flush()
dkl_log.flush()
ll_log.flush()
dec_sig_log.write('{},{},{}\n'.format(
batch_abs_idx, '2016-04-22', np.mean(result[4])))
enc_sig_log.write('{},{},{}\n'.format(
batch_abs_idx, '2016-04-22', np.mean(result[5])))
# val_sig_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.mean(result[6])))
dec_sig_log.flush()
enc_sig_log.flush()
dec_std_sig_log.write('{},{},{}\n'.format(
batch_abs_idx, '2016-04-22', np.std(result[4])))
enc_std_sig_log.write('{},{},{}\n'.format(
batch_abs_idx, '2016-04-22', np.std(result[5])))
dec_mean_log.write('{},{},{}\n'.format(
batch_abs_idx, '2016-04-22', np.mean(result[7])))
enc_mean_log.write('{},{},{}\n'.format(
batch_abs_idx, '2016-04-22', np.mean(result[6])))
dec_std_sig_log.flush()
enc_std_sig_log.flush()
dec_mean_log.flush()
enc_mean_log.flush()
# val_sig_log.flush()
# print('\n\nENC_MEAN:')
# print(result[3])
# print('\n\nENC_STD:')
# print(result[2])
# print('\nDEC_MEAN:')
# print(result[6])
# print('\nDEC_STD:')
# print(result[5])
# print('\n\nENCODER WEIGHTS:')
# print(model._encoder.layers[0].weights['w'].eval())
# print('\n\DECODER WEIGHTS:')
# print(model._decoder.layers[0].weights['w'].eval())
# print(model._encoder.layers[0].weights['w'].eval())
# print(result[2])
# print(result[3])
# print(result[3])
# print(result[2])
# print(result[-2])
# print(result[-1])
# Check cost
if np.isnan(cost) or np.isinf(cost):
log.info('NaN detected')
for i in range(len(result)):
print("\n\nresult[%d]:" % i)
try:
print(np.any(np.isnan(result[i])))
except:
pass
print(result[i])
print(result[3].shape)
print(model._encoder.layers[0].weights['w'].eval())
print('\n\nAny:')
print(np.any(np.isnan(result[8])))
print(np.any(np.isnan(result[9])))
print(np.any(np.isnan(result[10])))
print(inputs)
return 1., 1., 1.
# Update last losses
last_losses = np.roll(last_losses, 1)
last_losses[0] = cost
# Display training information
if np.mod(epoch_idx, options['freq_logging']) == 0:
log.info(
'Epoch {:02}/{:02} Batch {:03} Current Loss: {:0>15.4f} Mean last losses: {:0>15.4f}'
.format(epoch_idx + 1, options['n_epochs'],
batch_abs_idx, float(cost),
np.mean(last_losses)))
log.info('Batch Mean LL: {:0>15.4f}'.format(
np.mean(result[3], axis=0)))
log.info('Batch Mean -DKL: {:0>15.4f}'.format(
np.mean(result[2], axis=0)))
# Save model
if np.mod(batch_abs_idx, options['freq_saving']) == 0:
saver.save(
sess,
os.path.join(options['model_dir'],
'model_at_%d.ckpt' % batch_abs_idx))
log.info('Model saved')
save_dict = {}
# Save encoder params ------------------------------------------------------------------
for i in range(len(model._encoder.layers)):
layer_dict = {
'input_dim': model._encoder.layers[i].input_dim,
'output_dim': model._encoder.layers[i].output_dim,
'act_fn': model._encoder.layers[i].activation,
'W': model._encoder.layers[i].weights['w'].eval(),
'b': model._encoder.layers[i].weights['b'].eval()
}
save_dict['encoder'] = layer_dict
layer_dict = {
'input_dim': model._enc_mean.input_dim,
'output_dim': model._enc_mean.output_dim,
'act_fn': model._enc_mean.activation,
'W': model._enc_mean.weights['w'].eval(),
'b': model._enc_mean.weights['b'].eval()
}
save_dict['enc_mean'] = layer_dict
layer_dict = {
'input_dim': model._enc_log_std_sq.input_dim,
'output_dim': model._enc_log_std_sq.output_dim,
'act_fn': model._enc_log_std_sq.activation,
'W': model._enc_log_std_sq.weights['w'].eval(),
'b': model._enc_log_std_sq.weights['b'].eval()
}
save_dict['enc_log_std_sq'] = layer_dict
# Save decoder params ------------------------------------------------------------------
for i in range(len(model._decoder.layers)):
layer_dict = {
'input_dim': model._decoder.layers[i].input_dim,
'output_dim': model._decoder.layers[i].output_dim,
'act_fn': model._decoder.layers[i].activation,
'W': model._decoder.layers[i].weights['w'].eval(),
'b': model._decoder.layers[i].weights['b'].eval()
}
save_dict['decoder'] = layer_dict
layer_dict = {
'input_dim': model._dec_mean.input_dim,
'output_dim': model._dec_mean.output_dim,
'act_fn': model._dec_mean.activation,
'W': model._dec_mean.weights['w'].eval(),
'b': model._dec_mean.weights['b'].eval()
}
save_dict['dec_mean'] = layer_dict
layer_dict = {
'input_dim': model._dec_log_std_sq.input_dim,
'output_dim': model._dec_log_std_sq.output_dim,
'act_fn': model._dec_log_std_sq.activation,
'W': model._dec_log_std_sq.weights['w'].eval(),
'b': model._dec_log_std_sq.weights['b'].eval()
}
save_dict['dec_log_std_sq'] = layer_dict
pickle.dump(
save_dict,
open(
os.path.join(options['model_dir'],
'vae_dict_%d' % batch_abs_idx), 'wb'))
# Validate model
if np.mod(batch_abs_idx, options['freq_validation']) == 0:
model._decoder.layers[0].weights['w'].eval()[:5, :5]
valid_costs = []
seen_batches = 0
for val_batch in val_provider:
if isinstance(val_batch, tuple):
val_batch = val_batch[0]
val_cost = sess.run(
cost_function,
feed_dict={model_input_batch: val_batch})
valid_costs.append(val_cost)
seen_batches += 1
if seen_batches == options['valid_batches']:
break
# Print results
log.info('Validation loss: {:0>15.4f}'.format(
float(np.mean(valid_costs))))
val_samples = sess.run(
sampler,
feed_dict={
sampler_input_batch:
MVN(np.zeros(options['latent_dims']),
np.diag(np.ones(options['latent_dims'])),
size=options['batch_size'])
})
val_log.write('{},{},{}\n'.format(batch_abs_idx,
'2016-04-22',
np.mean(valid_costs)))
val_log.flush()
save_ae_samples(catalog,
np.reshape(result[7],
[options['batch_size']] +
options['img_shape']),
np.reshape(inputs,
[options['batch_size']] +
options['img_shape']),
np.reshape(val_samples,
[options['batch_size']] +
options['img_shape']),
batch_abs_idx,
options['dashboard_dir'],
num_to_save=5,
save_gray=True)
# save_dash_samples(
# catalog,
# val_samples,
# batch_abs_idx,
# options['dashboard_dir'],
# flat_samples=True,
# img_shape=options['img_shape'],
# num_to_save=5
# )
# save_samples(
# val_samples,
# int(batch_abs_idx/options['freq_validation']),
# os.path.join(options['model_dir'], 'valid_samples'),
# True,
# options['img_shape'],
# 5
# )
# save_samples(
# inputs,
# int(batch_abs_idx/options['freq_validation']),
# os.path.join(options['model_dir'], 'input_sanity'),
# True,
# options['img_shape'],
# num_to_save=5
# )
# save_samples(
# result[7],
# int(batch_abs_idx/options['freq_validation']),
# os.path.join(options['model_dir'], 'rec_sanity'),
# True,
# options['img_shape'],
# num_to_save=5
# )
log.info('End of epoch {}'.format(epoch_idx + 1))
def train(options):
# Get logger
log = utils.get_logger(os.path.join(options['model_dir'], 'log.txt'))
options_file = open(os.path.join(options['dashboard_dir'], 'options'), 'w')
options_file.write(options['description'] + '\n')
options_file.write(
'DKL Weight: {}\nLog Sigma^2 clipped to: [{}, {}]\n\n'.format(
options['DKL_weight'], -options['sigma_clip'],
options['sigma_clip']))
for optn in options:
options_file.write(optn)
options_file.write(':\t')
options_file.write(str(options[optn]))
options_file.write('\n')
options_file.close()
with open(os.path.join(options['dashboard_dir'], 'description'),
'w') as desc_file:
desc_file.write(options['description'])
# Dashboard Catalog
catalog = open(os.path.join(options['dashboard_dir'], 'catalog'), 'w')
catalog.write("""filename,type,name
description,plain,Description
options,plain,Options
train_loss.csv,csv,Train Loss
ll.csv,csv,Neg. Log-Likelihood
dec_log_sig_sq.csv,csv,Decoder Log Simga^2
dec_std_log_sig_sq.csv,csv,STD of Decoder Log Simga^2
dec_mean.csv,csv,Decoder Mean
dkl.csv,csv,DKL
enc_log_sig_sq.csv,csv,Encoder Log Sigma^2
enc_std_log_sig_sq.csv,csv,STD of Encoder Log Sigma^2
enc_mean.csv,csv,Encoder Mean
val_loss.csv,csv,Validation Loss
""")
catalog.flush()
train_log = open(os.path.join(options['dashboard_dir'], 'train_loss.csv'),
'w')
val_log = open(os.path.join(options['dashboard_dir'], 'val_loss.csv'), 'w')
dkl_log = open(os.path.join(options['dashboard_dir'], 'dkl.csv'), 'w')
ll_log = open(os.path.join(options['dashboard_dir'], 'll.csv'), 'w')
dec_sig_log = open(
os.path.join(options['dashboard_dir'], 'dec_log_sig_sq.csv'), 'w')
enc_sig_log = open(
os.path.join(options['dashboard_dir'], 'enc_log_sig_sq.csv'), 'w')
dec_std_sig_log = open(
os.path.join(options['dashboard_dir'], 'dec_std_log_sig_sq.csv'), 'w')
enc_std_sig_log = open(
os.path.join(options['dashboard_dir'], 'enc_std_log_sig_sq.csv'), 'w')
dec_mean_log = open(os.path.join(options['dashboard_dir'], 'dec_mean.csv'),
'w')
enc_mean_log = open(os.path.join(options['dashboard_dir'], 'enc_mean.csv'),
'w')
# val_sig_log = open(os.path.join(options['dashboard_dir'], 'val_log_sig_sq.csv'), 'w')
train_log.write('step,time,Train Loss\n')
val_log.write('step,time,Validation Loss\n')
dkl_log.write('step,time,DKL\n')
ll_log.write('step,time,-LL\n')
dec_sig_log.write('step,time,Decoder Log Sigma^2\n')
enc_sig_log.write('step,time,Encoder Log Sigma^2\n')
dec_std_sig_log.write('step,time,STD of Decoder Log Sigma^2\n')
enc_std_sig_log.write('step,time,STD of Encoder Log Sigma^2\n')
dec_mean_log.write('step,time,Decoder Mean\n')
enc_mean_log.write('step,time,Encoder Mean\n')
# Print options
utils.print_options(options, log)
# Load dataset ----------------------------------------------------------------------
# Train provider
train_provider, val_provider, test_provider = get_providers(options,
log,
flat=True)
# Initialize model ------------------------------------------------------------------
with tf.device('/gpu:0'):
# Define inputs ----------------------------------------------------------
model_input_batch = tf.placeholder(
tf.float32,
shape=[
options['batch_size'],
np.prod(np.array(options['img_shape']))
],
name='enc_inputs')
sampler_input_batch = tf.placeholder(
tf.float32,
shape=[options['batch_size'], options['latent_dims']],
name='dec_inputs')
log.info('Inputs defined')
# Discriminator ---------------------------------------------------------
# with tf.variable_scope('disc_scope'):
# disc_model = cupboard('fixed_conv_disc')(
# pickle.load(open(options['feat_params_path'], 'rb')),
# options['num_feat_layers'],
# 'discriminator'
# )
# VAE -------------------------------------------------------------------
# VAE model
# with tf.variable_scope('vae_scope'):
vae_model = cupboard('vanilla_vae')(
options['p_layers'], options['q_layers'],
np.prod(options['img_shape']), options['latent_dims'],
options['DKL_weight'], options['sigma_clip'], 'vanilla_vae')
# VAE/GAN ---------------------------------------------------------------
# vae_gan = cupboard('vae_gan')(
# vae_model,
# disc_model,
# options['img_shape'],
# options['input_channels'],
# 'vae_scope',
# 'disc_scope',
# name = 'vae_gan_model'
# )
log.info('Model initialized')
# Define optimizer
optimizer = tf.train.AdamOptimizer(learning_rate=options['lr'])
# Define forward pass
cost_function = vae_model(model_input_batch)
# backpass, grads = vae_gan(model_input_batch, sampler_input_batch, optimizer)
log.info('Forward pass graph built')
# Define sampler
# sampler = vae_gan.sampler
sampler = vae_model.build_sampler(sampler_input_batch)
log.info('Sampler graph built')
# optimizer = tf.train.GradientDescentOptimizer(learning_rate=options['lr'])
# train_step = optimizer.minimize(cost_function)
log.info('Optimizer graph built')
# Get gradients
grads = optimizer.compute_gradients(cost_function)
grads = [gv for gv in grads if gv[0] != None]
grad_tensors = [gv[0] for gv in grads]
# Clip gradients
clip_grads = [(tf.clip_by_norm(gv[0], 5.0,
name='grad_clipping'), gv[1])
for gv in grads]
# Update op
backpass = optimizer.apply_gradients(clip_grads)
# Define init operation
init_op = tf.initialize_all_variables()
log.info('Variable initialization graph built')
# Define op to save and restore variables
saver = tf.train.Saver()
log.info('Save operation built')
# --------------------------------------------------------------------------
# Train loop ---------------------------------------------------------------
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
log.info('Session started')
# Initialize shared variables or restore
if options['reload']:
saver.restore(sess, options['reload_file'])
log.info('Shared variables restored')
test_LL_and_DKL(sess, test_provider, feat_vae.vae.DKL,
feat_vae.vae.rec_loss, options, model_input_batch)
return
mean_img = np.load(
os.path.join(options['data_dir'],
'mean' + options['extension']))
std_img = np.load(
os.path.join(options['data_dir'],
'std' + options['extension']))
visualize(sess, feat_vae.vae.dec_mean, feat_vae.vae.dec_log_std_sq,
sampler, sampler_input_batch, model_input_batch,
feat_vae.vae.enc_mean, feat_vae.vae.enc_log_std_sq,
train_provider, val_provider, options, catalog, mean_img,
std_img)
return
else:
sess.run(init_op)
log.info('Shared variables initialized')
# Define last losses to compute a running average
last_losses = np.zeros((10))
batch_abs_idx = 0
D_to_G = options['D_to_G']
total_D2G = sum(D_to_G)
for epoch_idx in xrange(options['n_epochs']):
batch_rel_idx = 0
log.info('Epoch {}'.format(epoch_idx + 1))
for inputs, _ in train_provider:
batch_abs_idx += 1
batch_rel_idx += 1
# if batch_abs_idx < options['initial_G_iters']:
# optimizer = vae_optimizer
# else:
# optimizer = disc_optimizer
# if batch_abs_idx % total_D2G < D_to_G[0]:
# optimizer = disc_optimizer
# else:
# optimizer = vae_optimizer
result = sess.run([
cost_function,
backpass,
vae_model.DKL,
vae_model.rec_loss,
vae_model.dec_log_std_sq,
vae_model.enc_log_std_sq,
vae_model.enc_mean,
vae_model.dec_mean,
] + [gv[0] for gv in grads],
feed_dict={model_input_batch: inputs})
# print('#'*80)
# print(result[-1])
# print('#'*80)
cost = result[0]
if batch_abs_idx % 10 == 0:
train_log.write('{},{},{}\n'.format(
batch_abs_idx, '2016-04-22', np.mean(last_losses)))
dkl_log.write('{},{},{}\n'.format(batch_abs_idx,
'2016-04-22',
-np.mean(result[2])))
ll_log.write('{},{},{}\n'.format(batch_abs_idx,
'2016-04-22',
-np.mean(result[3])))
train_log.flush()
dkl_log.flush()
ll_log.flush()
dec_sig_log.write('{},{},{}\n'.format(
batch_abs_idx, '2016-04-22', np.mean(result[4])))
enc_sig_log.write('{},{},{}\n'.format(
batch_abs_idx, '2016-04-22', np.mean(result[5])))
# val_sig_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.mean(result[6])))
dec_sig_log.flush()
enc_sig_log.flush()
dec_std_sig_log.write('{},{},{}\n'.format(
batch_abs_idx, '2016-04-22', np.std(result[4])))
enc_std_sig_log.write('{},{},{}\n'.format(
batch_abs_idx, '2016-04-22', np.std(result[5])))
dec_mean_log.write('{},{},{}\n'.format(
batch_abs_idx, '2016-04-22', np.mean(result[7])))
enc_mean_log.write('{},{},{}\n'.format(
batch_abs_idx, '2016-04-22', np.mean(result[6])))
dec_std_sig_log.flush()
enc_std_sig_log.flush()
dec_mean_log.flush()
enc_mean_log.flush()
# val_sig_log.flush()
# Check cost
if np.isnan(cost) or np.isinf(cost):
log.info('NaN detected')
for i in range(len(result)):
print("\n\nresult[%d]:" % i)
try:
print(np.any(np.isnan(result[i])))
except:
pass
print(result[i])
print(result[3].shape)
print(model._encoder.layers[0].weights['w'].eval())
print('\n\nAny:')
print(np.any(np.isnan(result[8])))
print(np.any(np.isnan(result[9])))
print(np.any(np.isnan(result[10])))
print(inputs)
return 1., 1., 1.
# Update last losses
last_losses = np.roll(last_losses, 1)
last_losses[0] = cost
# Display training information
if np.mod(epoch_idx, options['freq_logging']) == 0:
log.info(
'Epoch {:02}/{:02} Batch {:03} Current Loss: {:0>15.4f} Mean last losses: {:0>15.4f}'
.format(epoch_idx + 1, options['n_epochs'],
batch_abs_idx, float(cost),
np.mean(last_losses)))
log.info('Batch Mean LL: {:0>15.4f}'.format(
np.mean(result[3], axis=0)))
log.info('Batch Mean -DKL: {:0>15.4f}'.format(
np.mean(result[2], axis=0)))
# log.info('Batch Mean Acc.: {:0>15.4f}'.format(result[-2], axis=0))
# Save model
if np.mod(batch_abs_idx, options['freq_saving']) == 0:
saver.save(
sess,
os.path.join(options['model_dir'],
'model_at_%d.ckpt' % batch_abs_idx))
log.info('Model saved')
# Validate model
if np.mod(batch_abs_idx, options['freq_validation']) == 0:
valid_costs = []
seen_batches = 0
for val_batch, _ in val_provider:
val_cost = sess.run(
vae_model.cost,
feed_dict={
model_input_batch:
val_batch,
sampler_input_batch:
MVN(np.zeros(options['latent_dims']),
np.diag(np.ones(options['latent_dims'])),
size=options['batch_size'])
})
valid_costs.append(val_cost)
seen_batches += 1
if seen_batches == options['valid_batches']:
break
# Print results
log.info('Validation loss: {:0>15.4f}'.format(
float(np.mean(valid_costs))))
val_samples = sess.run(
sampler,
feed_dict={
sampler_input_batch:
MVN(np.zeros(options['latent_dims']),
np.diag(np.ones(options['latent_dims'])),
size=options['batch_size'])
})
val_log.write('{},{},{}\n'.format(batch_abs_idx,
'2016-04-22',
np.mean(valid_costs)))
val_log.flush()
save_ae_samples(catalog,
np.reshape(result[7],
[options['batch_size']] +
options['img_shape']),
np.reshape(inputs,
[options['batch_size']] +
options['img_shape']),
np.reshape(val_samples,
[options['batch_size']] +
options['img_shape']),
batch_abs_idx,
options['dashboard_dir'],
num_to_save=5,
save_gray=True)
# save_samples(
# val_samples,
# int(batch_abs_idx/options['freq_validation']),
# os.path.join(options['model_dir'], 'valid_samples'),
# True,
# options['img_shape'],
# 5
# )
# save_samples(
# inputs,
# int(batch_abs_idx/options['freq_validation']),
# os.path.join(options['model_dir'], 'input_sanity'),
# True,
# options['img_shape'],
# num_to_save=5
# )
# save_samples(
# result[8],
# int(batch_abs_idx/options['freq_validation']),
# os.path.join(options['model_dir'], 'rec_sanity'),
# True,
# options['img_shape'],
# num_to_save=5
# )
log.info('End of epoch {}'.format(epoch_idx + 1))
# Test Model --------------------------------------------------------------------------
test_results = []
for inputs in test_provider:
if isinstance(inputs, tuple):
inputs = inputs[0]
batch_results = sess.run([
feat_vae.vae.DKL, feat_vae.vae.rec_loss,
feat_vae.vae.dec_log_std_sq, feat_vae.vae.enc_log_std_sq,
feat_vae.vae.dec_mean, feat_vae.vae.enc_mean
],
feed_dict={model_input_batch: inputs})
test_results.append(
map(
lambda p: np.mean(p, axis=1)
if len(p.shape) > 1 else np.mean(p), batch_results))
test_results = map(list, zip(*test_results))
# Print results
log.info('Test Mean Rec. Loss: {:0>15.4f}'.format(
float(np.mean(test_results[1]))))
log.info('Test DKL: {:0>15.4f}'.format(float(np.mean(
test_results[0]))))
log.info('Test Dec. Mean Log Std Sq: {:0>15.4f}'.format(
float(np.mean(test_results[2]))))
log.info('Test Enc. Mean Log Std Sq: {:0>15.4f}'.format(
float(np.mean(test_results[3]))))
log.info('Test Dec. Mean Mean: {:0>15.4f}'.format(
float(np.mean(test_results[4]))))
log.info('Test Enc. Mean Mean: {:0>15.4f}'.format(
float(np.mean(test_results[5]))))
def train(options):
# Get logger
log = utils.get_logger(os.path.join(options['model_dir'], 'log.txt'))
options_file = open(os.path.join(options['dashboard_dir'], 'options'), 'w')
options_file.write(options['description'] + '\n')
for optn in options:
options_file.write(optn)
options_file.write(':\t')
options_file.write(str(options[optn]))
options_file.write('\n')
options_file.close()
# Dashboard Catalog
catalog = open(os.path.join(options['dashboard_dir'], 'catalog'), 'w')
catalog.write(
"""filename,type,name
options,plain,Options
train_loss.csv,csv,Train Loss
val_loss.csv,csv,Validation Loss
"""
)
catalog.flush()
train_log = open(os.path.join(options['dashboard_dir'], 'train_loss.csv'), 'w')
val_log = open(os.path.join(options['dashboard_dir'], 'val_loss.csv'), 'w')
train_log.write('step,time,Train Loss\n')
val_log.write('step,time,Validation Loss\n')
# Print options
utils.print_options(options, log)
# Load dataset ----------------------------------------------------------------------
# Train provider
train_provider, val_provider, test_provider = get_providers(options, log, flat=True)
# Initialize model ------------------------------------------------------------------
with tf.device('/gpu:0'):
model = cupboard(options['model'])(
np.prod(options['img_shape']) * options['input_channels'],
options['enc_params'],
options['dec_params'],
'ae'
)
log.info('Model initialized')
# Define inputs
model_input_batch = tf.placeholder(
tf.float32,
shape = [options['batch_size'], np.prod(np.array(options['img_shape']))],
name = 'inputs'
)
model_label_batch = tf.placeholder(
tf.float32,
shape = [options['batch_size'], options['num_classes']],
name = 'labels'
)
log.info('Inputs defined')
# Load VAE
model(model_input_batch, model_input_batch)
feat_params = pickle.load(open(options['feat_params_path'], 'rb'))
for i in range(len(model._encoder.layers)):
model._encoder.layers[i].weights['w'] = tf.constant(feat_params['enc_W'][i])
model._encoder.layers[i].weights['b'] = tf.constant(feat_params['enc_b'][i])
classifier = FC(
options['latent_dims'],
options['num_classes'],
activation=None,
scale=0.01,
name='classifier_fc'
)(model.encoder)
classifier = tf.nn.softmax(classifier)
cost_function = -tf.mul(model_label_batch, tf.log(classifier))
cost_function = tf.reduce_sum(cost_function)
cost_function *= 1 / float(options['batch_size'])
log.info('Forward pass graph built')
# Define optimizer
optimizer = tf.train.AdamOptimizer(
learning_rate=options['lr']
)
# optimizer = tf.train.GradientDescentOptimizer(learning_rate=options['lr'])
# train_step = optimizer.minimize(cost_function)
# Get gradients
grads = optimizer.compute_gradients(cost_function)
grads = [gv for gv in grads if gv[0] != None]
grad_tensors = [gv[0] for gv in grads]
# Clip gradients
clip_grads = [(tf.clip_by_norm(gv[0], 5.0, name='grad_clipping'), gv[1]) for gv in grads]
# Update op
backpass = optimizer.apply_gradients(clip_grads)
log.info('Optimizer graph built')
# # Get gradients
# grad = optimizer.compute_gradients(cost_function)
# # Clip gradients
# clipped_grad = tf.clip_by_norm(grad, 5.0, name='grad_clipping')
# # Update op
# backpass = optimizer.apply_gradients(clipped_grad)
# Define init operation
init_op = tf.initialize_all_variables()
log.info('Variable initialization graph built')
# Define op to save and restore variables
saver = tf.train.Saver()
log.info('Save operation built')
# --------------------------------------------------------------------------
# Train loop ---------------------------------------------------------------
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
log.info('Session started')
# Initialize shared variables or restore
if options['reload']:
saver.restore(sess, os.path.join(options['model_dir'], 'model.ckpt'))
log.info('Shared variables restored')
else:
sess.run(init_op)
log.info('Shared variables initialized')
# Define last losses to compute a running average
last_losses = np.zeros((10))
last_accs = np.zeros((10))
batch_abs_idx = 0
for epoch_idx in xrange(options['n_epochs']):
batch_rel_idx = 0
log.info('Epoch {}'.format(epoch_idx + 1))
for inputs, labels in train_provider:
batch_abs_idx += 1
batch_rel_idx += 1
result = sess.run(
# (cost_function, train_step, model.enc_std, model.enc_mean, model.encoder, model.dec_std, model.dec_mean, model.decoder, model.rec_loss, model.DKL),
# 0 1 2 3 4 5 6 7 8 9 10
[cost_function, backpass, classifier] + [gv[0] for gv in grads],
feed_dict = {
model_input_batch: inputs,
model_label_batch: labels
}
)
cost = result[0]
if batch_abs_idx % 10 == 0:
train_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.mean(last_losses)))
train_log.flush()
# Check cost
if np.isnan(cost) or np.isinf(cost):
log.info('NaN detected')
for i in range(len(result)):
print("\n\nresult[%d]:" % i)
try:
print(np.any(np.isnan(result[i])))
except:
pass
print(result[i])
print(result[3].shape)
print(model._encoder.layers[0].weights['w'].eval())
print('\n\nAny:')
print(np.any(np.isnan(result[8])))
print(np.any(np.isnan(result[9])))
print(np.any(np.isnan(result[10])))
print(inputs)
return 1., 1., 1.
# Update last losses
last_losses = np.roll(last_losses, 1)
last_losses[0] = cost
last_accs = np.roll(last_accs, 1)
last_accs[0] = np.mean(np.argmax(labels, axis=1) == np.argmax(result[2], axis=1))
# Display training information
if np.mod(epoch_idx, options['freq_logging']) == 0:
log.info('Epoch {:02}/{:02} Batch {:03} Current Acc.: {:0>15.4f} Mean last accs: {:0>15.4f}'.format(
epoch_idx + 1,
options['n_epochs'],
batch_abs_idx,
last_accs[0],
np.mean(last_accs)
))
log.info('Batch Mean Loss: {:0>15.4f}'.format(np.mean(last_losses)))
# Save model
if np.mod(batch_abs_idx, options['freq_saving']) == 0:
saver.save(sess, os.path.join(options['model_dir'], 'model_at_%d.ckpt' % batch_abs_idx))
log.info('Model saved')
# Validate model
if np.mod(batch_abs_idx, options['freq_validation']) == 0:
valid_costs = []
seen_batches = 0
for val_batch, labels in val_provider:
val_result = sess.run(
[cost_function, classifier],
feed_dict = {
model_input_batch: val_batch,
model_label_batch: labels
}
)
val_cost = np.mean(np.argmax(labels, axis=1) == np.argmax(val_result[1], axis=1))
valid_costs.append(val_cost)
seen_batches += 1
if seen_batches == options['valid_batches']:
break
# Print results
log.info('Validation acc.: {:0>15.4f}'.format(
float(np.mean(valid_costs))
))
val_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.mean(valid_costs)))
val_log.flush()
log.info('End of epoch {}'.format(epoch_idx + 1))
# --------------------------------------------------------------------------
test_results = []
for inputs, labels in test_provider:
if isinstance(inputs, tuple):
inputs = inputs[0]
batch_results = sess.run(
[cost_function, classifier],
feed_dict = {
model_input_batch: inputs,
model_label_batch: labels
}
)
test_results.append(np.mean(np.argmax(labels, axis=1) == np.argmax(batch_results[1], axis=1)))
# Print results
log.info('Test Accuracy: {:0>15.4f}'.format(
np.mean(test_results)
))
def train(options):
# Get logger
log = utils.get_logger(os.path.join(options['model_dir'], 'log.txt'))
options_file = open(os.path.join(options['dashboard_dir'], 'options'), 'w')
for optn in options:
options_file.write(optn)
options_file.write(':\t')
options_file.write(str(options[optn]))
options_file.write('\n')
options_file.close()
# Dashboard Catalog
catalog = open(os.path.join(options['dashboard_dir'], 'catalog'), 'w')
catalog.write(
"""filename,type,name
options,plain,Options
train_loss.csv,csv,Train Loss
val_loss.csv,csv,Validation Loss
"""
)
catalog.flush()
train_log = open(os.path.join(options['dashboard_dir'], 'train_loss.csv'), 'w')
val_log = open(os.path.join(options['dashboard_dir'], 'val_loss.csv'), 'w')
train_log.write('step,time,Train Loss\n')
val_log.write('step,time,Validation Loss\n')
# Print options
utils.print_options(options, log)
# Load dataset ----------------------------------------------------------------------
# Train provider
train_provider, val_provider, test_provider = get_providers(options, log)
# Initialize model ------------------------------------------------------------------
# input_shape, input_channels, enc_params, dec_params, name=''
with tf.device('/gpu:0'):
if options['model'] == 'cnn_ae':
model = cupboard(options['model'])(
options['img_shape'],
options['input_channels'],
options['enc_params'],
options['dec_params'],
'cnn_ae'
)
# Define inputs
model_clean_input_batch = tf.placeholder(
tf.float32,
shape = [options['batch_size']] + options['img_shape'] + [options['input_channels']],
name = 'clean'
)
model_noisy_input_batch = tf.placeholder(
tf.float32,
shape = [options['batch_size']] + options['img_shape'] + [options['input_channels']],
name = 'noisy'
)
log.info('Inputs defined')
else:
model = cupboard(options['model'])(
np.prod(options['img_shape']) * options['input_channels'],
options['enc_params'],
options['dec_params'],
'ae'
)
# Define inputs
model_clean_input_batch = tf.placeholder(
tf.float32,
shape = [options['batch_size']] + [np.prod(options['img_shape']) * options['input_channels']],
name = 'clean'
)
model_noisy_input_batch = tf.placeholder(
tf.float32,
shape = [options['batch_size']] + [np.prod(options['img_shape']) * options['input_channels']],
name = 'noisy'
)
log.info('Inputs defined')
log.info('Model initialized')
# Define forward pass
print(model_clean_input_batch.get_shape())
print(model_noisy_input_batch.get_shape())
cost_function = model(model_clean_input_batch, model_noisy_input_batch)
log.info('Forward pass graph built')
log.info('Sampler graph built')
# Define optimizer
optimizer = tf.train.AdamOptimizer(
learning_rate=options['lr']
)
# optimizer = tf.train.GradientDescentOptimizer(learning_rate=options['lr'])
# train_step = optimizer.minimize(cost_function)
log.info('Optimizer graph built')
# Get gradients
grads = optimizer.compute_gradients(cost_function)
grads = [gv for gv in grads if gv[0] != None]
grad_tensors = [gv[0] for gv in grads]
# Clip gradients
clip_grads = [(tf.clip_by_norm(gv[0], 5.0, name='grad_clipping'), gv[1]) for gv in grads]
# Update op
backpass = optimizer.apply_gradients(clip_grads)
# Define init operation
init_op = tf.initialize_all_variables()
log.info('Variable initialization graph built')
# Define op to save and restore variables
saver = tf.train.Saver()
log.info('Save operation built')
# --------------------------------------------------------------------------
# Train loop ---------------------------------------------------------------
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
log.info('Session started')
# Initialize shared variables or restore
if options['reload']:
saver.restore(sess, os.path.join(options['model_dir'], 'model.ckpt'))
log.info('Shared variables restored')
else:
sess.run(init_op)
log.info('Shared variables initialized')
# Define last losses to compute a running average
last_losses = np.zeros((10))
batch_abs_idx = 0
for epoch_idx in xrange(options['n_epochs']):
batch_rel_idx = 0
log.info('Epoch {}'.format(epoch_idx + 1))
for inputs,_ in train_provider:
batch_abs_idx += 1
batch_rel_idx += 1
result = sess.run(
[cost_function, backpass] + [gv[0] for gv in grads],
feed_dict = {
model_clean_input_batch: inputs,
model_noisy_input_batch: np.float32(inputs) + \
normal(
loc=0.0,
scale=np.float32(options['noise_std']),
size=inputs.shape
)
}
)
cost = result[0]
if batch_abs_idx % 10 == 0:
train_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.mean(last_losses)))
train_log.flush()
# Check cost
if np.isnan(cost) or np.isinf(cost):
log.info('NaN detected')
for i in range(len(result)):
print("\n\nresult[%d]:" % i)
try:
print(np.any(np.isnan(result[i])))
except:
pass
print(result[i])
print(result[3].shape)
print(model._encoder.layers[0].weights['w'].eval())
print('\n\nAny:')
print(np.any(np.isnan(result[8])))
print(np.any(np.isnan(result[9])))
print(np.any(np.isnan(result[10])))
print(inputs)
return 1., 1., 1.
# Update last losses
last_losses = np.roll(last_losses, 1)
last_losses[0] = cost
# Display training information
if np.mod(epoch_idx, options['freq_logging']) == 0:
log.info('Epoch {:02}/{:02} Batch {:03} Current Loss: {:0>15.4f} Mean last losses: {:0>15.4f}'.format(
epoch_idx + 1,
options['n_epochs'],
batch_abs_idx,
float(cost),
np.mean(last_losses)
))
# Save model
if np.mod(batch_abs_idx, options['freq_saving']) == 0:
saver.save(sess, os.path.join(options['model_dir'], 'model_at_%d.ckpt' % batch_abs_idx))
log.info('Model saved')
# Save Encoder Params
save_dict = {
'enc_W': [],
'enc_b': [],
'enc_act_fn': [],
}
if options['model'] == 'cnn_ae':
pass
else:
for i in range(len(model._encoder.layers)):
save_dict['enc_W'].append(model._encoder.layers[i].weights['w'].eval())
save_dict['enc_b'].append(model._encoder.layers[i].weights['b'].eval())
save_dict['enc_act_fn'].append(options['enc_params']['act_fn'][i])
pickle.dump(save_dict, open(os.path.join(options['model_dir'], 'enc_dict_%d' % batch_abs_idx), 'wb'))
# Validate model
if np.mod(batch_abs_idx, options['freq_validation']) == 0:
model._decoder.layers[0].weights['w'].eval()[:5,:5]
valid_costs = []
seen_batches = 0
for val_batch,_ in val_provider:
noisy_val_batch = val_batch + \
normal(
loc=0.0,
scale=np.float32(options['noise_std']),
size=val_batch.shape
)
val_results = sess.run(
(cost_function, model.decoder),
feed_dict = {
model_clean_input_batch: val_batch,
model_noisy_input_batch: noisy_val_batch
}
)
valid_costs.append(val_results[0])
seen_batches += 1
if seen_batches == options['valid_batches']:
break
# Print results
log.info('Validation loss: {:0>15.4f}'.format(
float(np.mean(valid_costs))
))
val_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.mean(valid_costs)))
val_log.flush()
if options['model'] == 'conv_ae':
val_recon = np.reshape(
val_results[-1],
val_batch.shape
)
else:
val_batch = np.reshape(
val_batch,
[val_batch.shape[0]] + options['img_shape'] + [options['input_channels']]
)
noisy_val_batch = np.reshape(
noisy_val_batch,
[val_batch.shape[0]] + options['img_shape'] + [options['input_channels']]
)
val_recon = np.reshape(
val_results[-1],
[val_batch.shape[0]] + options['img_shape'] + [options['input_channels']]
)
save_ae_samples(
catalog,
val_batch,
noisy_val_batch,
val_recon,
batch_abs_idx,
options['dashboard_dir'],
num_to_save=5,
save_gray=True
)
# save_samples(
# val_recon,
# int(batch_abs_idx/options['freq_validation']),
# os.path.join(options['model_dir'], 'valid_samples'),
# False,
# options['img_shape'],
# 5
# )
# save_samples(
# inputs,
# int(batch_abs_idx/options['freq_validation']),
# os.path.join(options['model_dir'], 'input_sanity'),
# False,
# options['img_shape'],
# num_to_save=5
# )
log.info('End of epoch {}'.format(epoch_idx + 1))
def train(options):
# Get logger
log = utils.get_logger(os.path.join(options['model_dir'], 'log.txt'))
options_file = open(os.path.join(options['dashboard_dir'], 'options'), 'w')
options_file.write(options['description'] + '\n')
for optn in options:
options_file.write(optn)
options_file.write(':\t')
options_file.write(str(options[optn]))
options_file.write('\n')
options_file.close()
# Dashboard Catalog
catalog = open(os.path.join(options['dashboard_dir'], 'catalog'), 'w')
catalog.write("""filename,type,name
options,plain,Options
train_loss.csv,csv,Train Loss
train_acc.csv,csv,Train Accuracy
val_loss.csv,csv,Validation Loss
val_acc.csv,csv,Validation Accuracy
""")
catalog.flush()
train_log = open(os.path.join(options['dashboard_dir'], 'train_loss.csv'),
'w')
train_acc_log = open(
os.path.join(options['dashboard_dir'], 'train_acc.csv'), 'w')
val_log = open(os.path.join(options['dashboard_dir'], 'val_loss.csv'), 'w')
val_acc_log = open(os.path.join(options['dashboard_dir'], 'val_acc.csv'),
'w')
train_log.write('step,time,Train Loss\n')
val_log.write('step,time,Validation Loss\n')
train_acc_log.write('step,time,Train Accuracy\n')
val_acc_log.write('step,time,Validation Accuracy\n')
# Print options
utils.print_options(options, log)
# Load dataset ----------------------------------------------------------------------
# Train provider
num_data_points = len(
os.listdir(os.path.join(options['data_dir'], 'train', 'info')))
num_data_points -= 2
train_provider = DataProvider(
num_data_points, options['batch_size'],
toolbox.CIFARLoader(data_dir=os.path.join(options['data_dir'],
'train'),
flat=False))
# Valid provider
num_data_points = len(
os.listdir(os.path.join(options['data_dir'], 'valid', 'info')))
num_data_points -= 2
print(num_data_points)
val_provider = DataProvider(
num_data_points, options['batch_size'],
toolbox.CIFARLoader(data_dir=os.path.join(options['data_dir'],
'valid'),
flat=False))
log.info('Data providers initialized.')
# Initialize model ------------------------------------------------------------------
with tf.device('/gpu:0'):
model = cupboard(
options['model'])(options['img_shape'], options['input_channels'],
options['num_classes'], options['conv_params'],
options['fc_params'], 'CIFAR_classifier')
log.info('Model initialized')
# Define inputs
input_batch = tf.placeholder(tf.float32,
shape=[options['batch_size']] +
options['img_shape'] +
[options['input_channels']],
name='inputs')
label_batch = tf.placeholder(
tf.float32,
shape=[options['batch_size'], options['num_classes']],
name='labels')
log.info('Inputs defined')
# Define forward pass
cost_function, classifier = model(input_batch, label_batch)
log.info('Forward pass graph built')
# Define optimizer
optimizer = tf.train.AdamOptimizer(learning_rate=options['lr'])
train_step = optimizer.minimize(cost_function)
log.info('Optimizer graph built')
# # Get gradients
# grad = optimizer.compute_gradients(cost_function)
# # Clip gradients
# clipped_grad = tf.clip_by_norm(grad, 5.0, name='grad_clipping')
# # Update op
# backpass = optimizer.apply_gradients(clipped_grad)
# Define init operation
init_op = tf.initialize_all_variables()
log.info('Variable initialization graph built')
# Define op to save and restore variables
saver = tf.train.Saver()
log.info('Save operation built')
# --------------------------------------------------------------------------
# Train loop ---------------------------------------------------------------
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
log.info('Session started')
# Initialize shared variables or restore
if options['reload']:
saver.restore(sess, os.path.join(options['model_dir'],
'model.ckpt'))
log.info('Shared variables restored')
else:
sess.run(init_op)
log.info('Shared variables initialized')
# Define last losses to compute a running average
last_losses = np.zeros((10))
last_accs = np.zeros((10))
batch_abs_idx = 0
for epoch_idx in xrange(options['n_epochs']):
batch_rel_idx = 0
log.info('Epoch {}'.format(epoch_idx + 1))
for inputs, labels in train_provider:
batch_abs_idx += 1
batch_rel_idx += 1
results = sess.run([cost_function, classifier, train_step],
feed_dict={
input_batch: inputs,
label_batch: labels
})
cost = results[0]
# Check cost
if np.isnan(cost) or np.isinf(cost):
log.info('NaN detected')
return 1., 1., 1.
accuracy = np.mean(
np.argmax(results[1], axis=1) == np.argmax(labels, axis=1))
# Update last losses
last_losses = np.roll(last_losses, 1)
last_losses[0] = cost
last_accs = np.roll(last_accs, 1)
last_accs[0] = accuracy
if batch_abs_idx % 10 == 0:
train_log.write('{},{},{}\n'.format(
batch_abs_idx, '2016-04-22', np.mean(last_losses)))
train_acc_log.write('{},{},{}\n'.format(
batch_abs_idx, '2016-04-22', np.mean(last_accs)))
train_log.flush()
train_acc_log.flush()
# Display training information
if np.mod(epoch_idx, options['freq_logging']) == 0:
log.info(
'Epoch {:02}/{:02} Batch {:03} Current Loss: {:0>15.4f} Mean last losses: {:0>15.4f}'
.format(epoch_idx + 1, options['n_epochs'],
batch_abs_idx, float(cost),
np.mean(last_losses)))
log.info(
'Epoch {:02}/{:02} Batch {:03} Current Accuracy: {:0>15.4f}'
.format(epoch_idx + 1, options['n_epochs'],
batch_abs_idx, np.mean(last_accs)))
# Save model
if np.mod(batch_abs_idx, options['freq_saving']) == 0:
saver.save(
sess,
os.path.join(options['model_dir'],
'model_at_%d.ckpt' % batch_abs_idx))
save_dict = []
for c_ind in xrange(0, len(model._classifier_conv.layers),
2):
layer_dict = {
'n_filters_in':
model._classifier_conv.layers[c_ind].n_filters_in,
'n_filters_out':
model._classifier_conv.layers[c_ind].n_filters_out,
'input_dim':
model._classifier_conv.layers[c_ind].input_dim,
'filter_dim':
model._classifier_conv.layers[c_ind].filter_dim,
'strides':
model._classifier_conv.layers[c_ind].strides,
'padding':
model._classifier_conv.layers[c_ind].padding,
}
save_dict.append(layer_dict)
pickle.dump(
save_dict,
open(
os.path.join(options['model_dir'],
'class_dict_%d' % batch_abs_idx),
'wb'))
log.info('Model saved')
# Save params for feature vae training later
# conv_feat = deepcopy(model._classifier_conv)
# for lay_ind in range(0,len(conv_feat.layers),2):
# conv_feat[lay_ind].weights['W'] = tf.constant(conv_feat[lay_ind].weights['W'].eval())
# conv_feat[lay_ind].weights['b'] = tf.constant(conv_feat[lay_ind].weights['b'].eval())
# pickle(conv_feat, open(os.path.join(options['model_dir'], 'classifier_conv_feat_%d' % batch_abs_idx), 'wb'))
# Validate model
if np.mod(batch_abs_idx, options['freq_validation']) == 0:
valid_costs = []
val_accuracies = []
seen_batches = 0
for val_batch, val_label in val_provider:
# Break if 10 batches seen for now
if seen_batches == options['valid_batches']:
break
val_results = sess.run([cost_function, classifier],
feed_dict={
input_batch: val_batch,
label_batch: val_label
})
val_cost = val_results[0]
valid_costs.append(val_cost)
seen_batches += 1
val_accuracies.append(
np.mean(
np.argmax(val_results[1], axis=1) == np.argmax(
val_label, axis=1)))
# Print results
log.info('Validation loss: {:0>15.4f}'.format(
float(np.mean(valid_costs))))
log.info('Validation Accuracy: {:0>15.4f}'.format(
np.mean(val_accuracies)))
val_log.write('{},{},{}\n'.format(
batch_abs_idx, '2016-04-22',
float(np.mean(valid_costs))))
val_acc_log.write('{},{},{}\n'.format(
batch_abs_idx, '2016-04-22', np.mean(val_accuracies)))
val_log.flush()
val_acc_log.flush()
log.info('End of epoch {}'.format(epoch_idx + 1))
def train(options):
# Get logger
log = utils.get_logger(os.path.join(options['model_dir'], 'log.txt'))
options_file = open(os.path.join(options['dashboard_dir'], 'options'), 'w')
options_file.write(options['description'] + '\n')
options_file.write('Log Sigma^2 clipped to: [{}, {}]\n\n'.format(
-options['sigma_clip'], options['sigma_clip']))
for optn in options:
options_file.write(optn)
options_file.write(':\t')
options_file.write(str(options[optn]))
options_file.write('\n')
options_file.close()
# Dashboard Catalog
catalog = open(os.path.join(options['dashboard_dir'], 'catalog'), 'w')
catalog.write("""filename,type,name
options,plain,Options
train_loss.csv,csv,Discriminator Cross-Entropy
train_acc.csv,csv,Discriminator Accuracy
val_loss.csv,csv,Validation Cross-Entropy
val_acc.csv,csv,Validation Accuracy
""")
catalog.flush()
train_log = open(os.path.join(options['dashboard_dir'], 'train_loss.csv'),
'w')
val_log = open(os.path.join(options['dashboard_dir'], 'val_loss.csv'), 'w')
train_acc = open(os.path.join(options['dashboard_dir'], 'train_acc.csv'),
'w')
val_acc = open(os.path.join(options['dashboard_dir'], 'val_acc.csv'), 'w')
train_log.write(
'step,time,Train CE (Training Vanilla),Train CE (Training Gen.),Train CE (Training Disc.)\n'
)
val_log.write(
'step,time,Validation CE (Training Vanilla),Validation CE (Training Gen.),Validation CE (Training Disc.)\n'
)
train_acc.write(
'step,time,Train CE (Training Vanilla),Train CE (Training Gen.),Train CE (Training Disc.)\n'
)
val_acc.write(
'step,time,Validation CE (Training Vanilla),Validation CE (Training Gen.),Validation Acc. (Training Disc.)\n'
)
# Print options
utils.print_options(options, log)
# Load dataset ----------------------------------------------------------------------
# Train provider
train_provider, val_provider, test_provider = get_providers(options,
log,
flat=True)
# Initialize model ------------------------------------------------------------------
with tf.device('/gpu:0'):
# Define inputs -------------------------------------------------------------------------
real_batch = tf.placeholder(tf.float32,
shape=[
options['batch_size'],
np.prod(np.array(options['img_shape']))
],
name='real_inputs')
sampler_input_batch = tf.placeholder(
tf.float32,
shape=[options['batch_size'], options['latent_dims']],
name='noise_channel')
labels = tf.constant(
np.expand_dims(np.concatenate((np.ones(
options['batch_size']), np.zeros(options['batch_size'])),
axis=0).astype(np.float32),
axis=1))
labels = tf.cast(labels, tf.float32)
log.info('Inputs defined')
# Define model --------------------------------------------------------------------------
with tf.variable_scope('gen_scope'):
generator = Sequential('generator')
generator += FullyConnected(options['latent_dims'],
60,
tf.nn.tanh,
name='fc_1')
generator += FullyConnected(60, 60, tf.nn.tanh, name='fc_2')
generator += FullyConnected(60,
np.prod(options['img_shape']),
tf.nn.tanh,
name='fc_3')
sampler = generator(sampler_input_batch)
with tf.variable_scope('disc_scope'):
disc_model = cupboard('fixed_conv_disc')(
pickle.load(open(options['disc_params_path'], 'rb')),
options['num_feat_layers'],
name='disc_model')
disc_inputs = tf.concat(0, [real_batch, sampler])
disc_inputs = tf.reshape(
disc_inputs, [disc_inputs.get_shape()[0].value] +
options['img_shape'] + [options['input_channels']])
preds = disc_model(disc_inputs)
preds = tf.clip_by_value(preds, 0.00001, 0.99999)
# Disc Accuracy
disc_accuracy = (
1 / float(labels.get_shape()[0].value)) * tf.reduce_sum(
tf.cast(tf.equal(tf.round(preds), labels), tf.float32))
# Define Losses -------------------------------------------------------------------------
# Discrimnator Cross-Entropy
disc_CE = (1 / float(labels.get_shape()[0].value)) * tf.reduce_sum(
-tf.add(tf.mul(labels, tf.log(preds)),
tf.mul(1.0 - labels, tf.log(1.0 - preds))))
gen_loss = -tf.mul(1.0 - labels, tf.log(preds))
# Define Optimizers ---------------------------------------------------------------------
optimizer = tf.train.AdamOptimizer(learning_rate=options['lr'])
# Get Generator and Disriminator Trainable Variables
gen_train_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
'gen_scope')
disc_train_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
'disc_scope')
# Get generator gradients
grads = optimizer.compute_gradients(gen_loss, gen_train_vars)
grads = [gv for gv in grads if gv[0] != None]
clip_grads = [(tf.clip_by_norm(gv[0], 5.0,
name='gen_grad_clipping'), gv[1])
for gv in grads]
gen_backpass = optimizer.apply_gradients(clip_grads)
# Get Dsicriminator gradients
grads = optimizer.compute_gradients(disc_CE, disc_train_vars)
grads = [gv for gv in grads if gv[0] != None]
clip_grads = [(tf.clip_by_norm(gv[0], 5.0,
name='disc_grad_clipping'), gv[1])
for gv in grads]
disc_backpass = optimizer.apply_gradients(clip_grads)
log.info('Optimizer graph built')
# --------------------------------------------------------------------------------------
# Define init operation
init_op = tf.initialize_all_variables()
log.info('Variable initialization graph built')
# Define op to save and restore variables
saver = tf.train.Saver()
log.info('Save operation built')
# --------------------------------------------------------------------------
# Train loop ---------------------------------------------------------------
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
log.info('Session started')
# Initialize shared variables or restore
if options['reload_all']:
saver.restore(sess, options['reload_file'])
log.info('Shared variables restored')
else:
sess.run(init_op)
log.info('Variables initialized')
# Define last losses to compute a running average
last_losses = np.zeros((10))
last_accs = np.zeros((10))
disc_tracker = np.ones((5000))
batch_abs_idx = 0
D_to_G = options['D_to_G']
total_D2G = sum(D_to_G)
base = options['initial_G_iters'] + options['initial_D_iters']
# must_init = True
feat_params = pickle.load(open(options['disc_params_path'], 'rb'))
for epoch_idx in xrange(options['n_epochs']):
batch_rel_idx = 0
log.info('Epoch {}'.format(epoch_idx + 1))
for inputs in train_provider:
if isinstance(inputs, tuple):
inputs = inputs[0]
batch_abs_idx += 1
batch_rel_idx += 1
if batch_abs_idx < options['initial_G_iters']:
backpass = gen_backpass
log_format_string = '{},{},{},,\n'
elif options['initial_G_iters'] <= batch_abs_idx < base:
backpass = disc_backpass
log_format_string = '{},{},,,{}\n'
else:
# if np.mean(disc_tracker) < 0.95:
# disc_model._disc.layers[-2].re_init_weights(sess)
# disc_tracker = np.ones((5000))
if (batch_abs_idx - base) % total_D2G < D_to_G[0]:
# if must_init:
# # i = 0
# # for j in xrange(options['num_feat_layers']):
# # if feat_params[j]['layer_type'] == 'conv':
# # disc_model._disc.layers[i].re_init_weights(sess)
# # # print('@' * 1000)
# # # print(disc_model._disc.layers[i])
# # i += 1 # for dealing with activation function
# # elif feat_params[j]['layer_type'] == 'fc':
# # disc_model._disc.layers[i].re_init_weights(sess)
# # # print('@' * 1000)
# # # print(disc_model._disc.layers[i])
# # i += 1
# disc_model._disc.layers[-2].re_init_weights(sess)
# # print('@' * 1000)
# # print(disc_model._disc.layers[-2])
# must_init = False
backpass = disc_backpass
log_format_string = '{},{},,,{}\n'
else:
# must_init = True
backpass = gen_backpass
log_format_string = '{},{},,{},\n'
log_format_string = '{},{},{},,\n'
result = sess.run(
[disc_CE, backpass, disc_accuracy],
feed_dict={
real_batch:
inputs,
sampler_input_batch:
MVN(np.zeros(options['latent_dims']),
np.diag(np.ones(options['latent_dims'])),
size=options['batch_size'])
})
cost = result[0]
if batch_abs_idx % 10 == 0:
train_log.write(
log_format_string.format(batch_abs_idx, '2016-04-22',
np.mean(last_losses)))
train_acc.write(
log_format_string.format(batch_abs_idx, '2016-04-22',
np.mean(last_accs)))
train_log.flush()
train_acc.flush()
# Check cost
if np.isnan(cost) or np.isinf(cost):
log.info('NaN detected')
# Update last losses
last_losses = np.roll(last_losses, 1)
last_losses[0] = cost
last_accs = np.roll(last_accs, 1)
last_accs[0] = result[-1]
disc_tracker = np.roll(disc_tracker, 1)
disc_tracker[0] = result[-1]
# Display training information
if np.mod(epoch_idx, options['freq_logging']) == 0:
log.info(
'Epoch {:02}/{:02} Batch {:03} Current Loss: {:0>15.4f} Mean last losses: {:0>15.4f}'
.format(epoch_idx + 1, options['n_epochs'],
batch_abs_idx, float(cost),
np.mean(last_losses)))
log.info(
'Epoch {:02}/{:02} Batch {:03} Current Loss: {:0>15.4f} Mean last accuracies: {:0>15.4f}'
.format(epoch_idx + 1, options['n_epochs'],
batch_abs_idx, float(cost),
np.mean(last_accs)))
# Save model
if np.mod(batch_abs_idx, options['freq_saving']) == 0:
saver.save(
sess,
os.path.join(options['model_dir'],
'model_at_%d.ckpt' % batch_abs_idx))
log.info('Model saved')
# Validate model
if np.mod(batch_abs_idx, options['freq_validation']) == 0:
valid_costs = []
valid_accs = []
seen_batches = 0
for val_batch in val_provider:
if isinstance(val_batch, tuple):
val_batch = val_batch[0]
result = sess.run(
[disc_CE, disc_accuracy],
feed_dict={
real_batch:
val_batch,
sampler_input_batch:
MVN(np.zeros(options['latent_dims']),
np.diag(np.ones(options['latent_dims'])),
size=options['batch_size'])
})
valid_costs.append(result[0])
valid_accs.append(result[-1])
seen_batches += 1
if seen_batches == options['valid_batches']:
break
# Print results
log.info('Validation loss: {:0>15.4f}'.format(
float(np.mean(valid_costs))))
log.info('Validation accuracies: {:0>15.4f}'.format(
float(np.mean(valid_accs))))
val_samples = sess.run(
sampler,
feed_dict={
sampler_input_batch:
MVN(np.zeros(options['latent_dims']),
np.diag(np.ones(options['latent_dims'])),
size=options['batch_size'])
})
val_log.write(
log_format_string.format(batch_abs_idx, '2016-04-22',
np.mean(valid_costs)))
val_acc.write(
log_format_string.format(batch_abs_idx, '2016-04-22',
np.mean(valid_accs)))
val_log.flush()
val_acc.flush()
save_ae_samples(catalog,
np.ones([options['batch_size']] +
options['img_shape']),
np.reshape(inputs,
[options['batch_size']] +
options['img_shape']),
np.reshape(val_samples,
[options['batch_size']] +
options['img_shape']),
batch_abs_idx,
options['dashboard_dir'],
num_to_save=5,
save_gray=True)
log.info('End of epoch {}'.format(epoch_idx + 1))
def train(options):
# Get logger
log = utils.get_logger(os.path.join(options['model_dir'], 'log.txt'))
options_file = open(os.path.join(options['dashboard_dir'], 'options'), 'w')
options_file.write(options['description'] + '\n')
options_file.write(
'DKL Weight: {}\nLog Sigma^2 clipped to: [{}, {}]\n\n'.format(
options['DKL_weight'],
-options['sigma_clip'],
options['sigma_clip']
)
)
for optn in options:
options_file.write(optn)
options_file.write(':\t')
options_file.write(str(options[optn]))
options_file.write('\n')
options_file.close()
# Dashboard Catalog
catalog = open(os.path.join(options['dashboard_dir'], 'catalog'), 'w')
catalog.write(
"""filename,type,name
options,plain,Options
train_loss.csv,csv,Train Loss
ll.csv,csv,Neg. Log-Likelihood
dec_log_sig_sq.csv,csv,Decoder Log Simga^2
dec_std_log_sig_sq.csv,csv,STD of Decoder Log Simga^2
dec_mean.csv,csv,Decoder Mean
dkl.csv,csv,DKL
enc_log_sig_sq.csv,csv,Encoder Log Sigma^2
enc_std_log_sig_sq.csv,csv,STD of Encoder Log Sigma^2
enc_mean.csv,csv,Encoder Mean
val_loss.csv,csv,Validation Loss
"""
)
catalog.flush()
train_log = open(os.path.join(options['dashboard_dir'], 'train_loss.csv'), 'w')
val_log = open(os.path.join(options['dashboard_dir'], 'val_loss.csv'), 'w')
dkl_log = open(os.path.join(options['dashboard_dir'], 'dkl.csv'), 'w')
ll_log = open(os.path.join(options['dashboard_dir'], 'll.csv'), 'w')
dec_sig_log = open(os.path.join(options['dashboard_dir'], 'dec_log_sig_sq.csv'), 'w')
enc_sig_log = open(os.path.join(options['dashboard_dir'], 'enc_log_sig_sq.csv'), 'w')
dec_std_sig_log = open(os.path.join(options['dashboard_dir'], 'dec_std_log_sig_sq.csv'), 'w')
enc_std_sig_log = open(os.path.join(options['dashboard_dir'], 'enc_std_log_sig_sq.csv'), 'w')
dec_mean_log = open(os.path.join(options['dashboard_dir'], 'dec_mean.csv'), 'w')
enc_mean_log = open(os.path.join(options['dashboard_dir'], 'enc_mean.csv'), 'w')
# val_sig_log = open(os.path.join(options['dashboard_dir'], 'val_log_sig_sq.csv'), 'w')
train_log.write('step,time,Train Loss\n')
val_log.write('step,time,Validation Loss\n')
dkl_log.write('step,time,DKL\n')
ll_log.write('step,time,-LL\n')
dec_sig_log.write('step,time,Decoder Log Sigma^2\n')
enc_sig_log.write('step,time,Encoder Log Sigma^2\n')
dec_std_sig_log.write('step,time,STD of Decoder Log Sigma^2\n')
enc_std_sig_log.write('step,time,STD of Encoder Log Sigma^2\n')
dec_mean_log.write('step,time,Decoder Mean\n')
enc_mean_log.write('step,time,Encoder Mean\n')
# Print options
utils.print_options(options, log)
# Load dataset ----------------------------------------------------------------------
# Train provider
train_provider, val_provider, test_provider = get_providers(options, log, flat=True)
# Initialize model ------------------------------------------------------------------
with tf.device('/gpu:0'):
model = cupboard(options['model'])(
options['p_layers'],
options['q_layers'],
np.prod(options['img_shape']),
options['latent_dims'],
options['DKL_weight'],
options['sigma_clip'],
'vanilla_vae'
)
log.info('Model initialized')
# Define inputs
model_input_batch = tf.placeholder(
tf.float32,
shape = [options['batch_size'], np.prod(np.array(options['img_shape']))],
name = 'enc_inputs'
)
sampler_input_batch = tf.placeholder(
tf.float32,
shape = [options['batch_size'], options['latent_dims']],
name = 'dec_inputs'
)
log.info('Inputs defined')
# Define forward pass
cost_function = model(model_input_batch)
log.info('Forward pass graph built')
# Define sampler
sampler = model.build_sampler(sampler_input_batch)
log.info('Sampler graph built')
# Define optimizer
optimizer = tf.train.AdamOptimizer(
learning_rate=options['lr']
)
# optimizer = tf.train.GradientDescentOptimizer(learning_rate=options['lr'])
train_step = optimizer.minimize(cost_function)
# Get gradients
grads = optimizer.compute_gradients(cost_function)
grad_tensors = [gv[0] for gv in grads]
# Clip gradients
clip_grads = [(tf.clip_by_norm(gv[0], 5.0, name='grad_clipping'), gv[1]) for gv in grads]
# Update op
backpass = optimizer.apply_gradients(clip_grads)
log.info('Optimizer graph built')
# # Get gradients
# grad = optimizer.compute_gradients(cost_function)
# # Clip gradients
# clipped_grad = tf.clip_by_norm(grad, 5.0, name='grad_clipping')
# # Update op
# backpass = optimizer.apply_gradients(clipped_grad)
# Define init operation
init_op = tf.initialize_all_variables()
log.info('Variable initialization graph built')
# Define op to save and restore variables
saver = tf.train.Saver()
log.info('Save operation built')
# --------------------------------------------------------------------------
# Train loop ---------------------------------------------------------------
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
log.info('Session started')
# Initialize shared variables or restore
if options['reload']:
saver.restore(sess, options['reload_file'])
log.info('Shared variables restored')
# test_LL_and_DKL(sess, test_provider, model.DKL, model.rec_loss, options, model_input_batch)
# return
# if options['data_dir'] == 'MNIST':
# mean_img = np.zeros(np.prod(options['img_shape']))
# std_img = np.ones(np.prod(options['img_shape']))
# else:
# mean_img = np.load(os.path.join(options['data_dir'], 'mean' + options['extension']))
# std_img = np.load(os.path.join(options['data_dir'], 'std' + options['extension']))
# visualize(model.sampler_mean, sess, model.dec_mean, model.dec_log_std_sq, sampler, sampler_input_batch,
# model_input_batch, model.enc_mean, model.enc_log_std_sq,
# train_provider, val_provider, options, catalog, mean_img, std_img)
# return
else:
sess.run(init_op)
log.info('Shared variables initialized')
# Define last losses to compute a running average
last_losses = np.zeros((10))
batch_abs_idx = 0
for epoch_idx in xrange(options['n_epochs']):
batch_rel_idx = 0
log.info('Epoch {}'.format(epoch_idx + 1))
for inputs in train_provider:
if isinstance(inputs, tuple):
inputs = inputs[0]
batch_abs_idx += 1
batch_rel_idx += 1
result = sess.run(
# (cost_function, train_step, model.enc_std, model.enc_mean, model.encoder, model.dec_std, model.dec_mean, model.decoder, model.rec_loss, model.DKL),
# 0 1 2 3 4 5 6 7 8 9 10
[cost_function, backpass, model.DKL, model.rec_loss, model.dec_log_std_sq, model.enc_log_std_sq, model.enc_mean, model.dec_mean],
feed_dict = {
model_input_batch: inputs
}
)
cost = result[0]
if batch_abs_idx % 10 == 0:
train_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.mean(last_losses)))
dkl_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', -np.mean(result[2])))
ll_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', -np.mean(result[3])))
train_log.flush()
dkl_log.flush()
ll_log.flush()
dec_sig_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.mean(result[4])))
enc_sig_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.mean(result[5])))
# val_sig_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.mean(result[6])))
dec_sig_log.flush()
enc_sig_log.flush()
dec_std_sig_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.std(result[4])))
enc_std_sig_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.std(result[5])))
dec_mean_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.mean(result[7])))
enc_mean_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.mean(result[6])))
dec_std_sig_log.flush()
enc_std_sig_log.flush()
dec_mean_log.flush()
enc_mean_log.flush()
# val_sig_log.flush()
# print('\n\nENC_MEAN:')
# print(result[3])
# print('\n\nENC_STD:')
# print(result[2])
# print('\nDEC_MEAN:')
# print(result[6])
# print('\nDEC_STD:')
# print(result[5])
# print('\n\nENCODER WEIGHTS:')
# print(model._encoder.layers[0].weights['w'].eval())
# print('\n\DECODER WEIGHTS:')
# print(model._decoder.layers[0].weights['w'].eval())
# print(model._encoder.layers[0].weights['w'].eval())
# print(result[2])
# print(result[3])
# print(result[3])
# print(result[2])
# print(result[-2])
# print(result[-1])
# Check cost
if np.isnan(cost) or np.isinf(cost):
log.info('NaN detected')
for i in range(len(result)):
print("\n\nresult[%d]:" % i)
try:
print(np.any(np.isnan(result[i])))
except:
pass
print(result[i])
print(result[3].shape)
print(model._encoder.layers[0].weights['w'].eval())
print('\n\nAny:')
print(np.any(np.isnan(result[8])))
print(np.any(np.isnan(result[9])))
print(np.any(np.isnan(result[10])))
print(inputs)
return 1., 1., 1.
# Update last losses
last_losses = np.roll(last_losses, 1)
last_losses[0] = cost
# Display training information
if np.mod(epoch_idx, options['freq_logging']) == 0:
log.info('Epoch {:02}/{:02} Batch {:03} Current Loss: {:0>15.4f} Mean last losses: {:0>15.4f}'.format(
epoch_idx + 1,
options['n_epochs'],
batch_abs_idx,
float(cost),
np.mean(last_losses)
))
log.info('Batch Mean LL: {:0>15.4f}'.format(np.mean(result[3], axis=0)))
log.info('Batch Mean -DKL: {:0>15.4f}'.format(np.mean(result[2], axis=0)))
# Save model
if np.mod(batch_abs_idx, options['freq_saving']) == 0:
saver.save(sess, os.path.join(options['model_dir'], 'model_at_%d.ckpt' % batch_abs_idx))
log.info('Model saved')
save_dict = {}
# Save encoder params ------------------------------------------------------------------
for i in range(len(model._encoder.layers)):
layer_dict = {
'input_dim':model._encoder.layers[i].input_dim,
'output_dim':model._encoder.layers[i].output_dim,
'act_fn':model._encoder.layers[i].activation,
'W':model._encoder.layers[i].weights['w'].eval(),
'b':model._encoder.layers[i].weights['b'].eval()
}
save_dict['encoder'] = layer_dict
layer_dict = {
'input_dim':model._enc_mean.input_dim,
'output_dim':model._enc_mean.output_dim,
'act_fn':model._enc_mean.activation,
'W':model._enc_mean.weights['w'].eval(),
'b':model._enc_mean.weights['b'].eval()
}
save_dict['enc_mean'] = layer_dict
layer_dict = {
'input_dim':model._enc_log_std_sq.input_dim,
'output_dim':model._enc_log_std_sq.output_dim,
'act_fn':model._enc_log_std_sq.activation,
'W':model._enc_log_std_sq.weights['w'].eval(),
'b':model._enc_log_std_sq.weights['b'].eval()
}
save_dict['enc_log_std_sq'] = layer_dict
# Save decoder params ------------------------------------------------------------------
for i in range(len(model._decoder.layers)):
layer_dict = {
'input_dim':model._decoder.layers[i].input_dim,
'output_dim':model._decoder.layers[i].output_dim,
'act_fn':model._decoder.layers[i].activation,
'W':model._decoder.layers[i].weights['w'].eval(),
'b':model._decoder.layers[i].weights['b'].eval()
}
save_dict['decoder'] = layer_dict
layer_dict = {
'input_dim':model._dec_mean.input_dim,
'output_dim':model._dec_mean.output_dim,
'act_fn':model._dec_mean.activation,
'W':model._dec_mean.weights['w'].eval(),
'b':model._dec_mean.weights['b'].eval()
}
save_dict['dec_mean'] = layer_dict
layer_dict = {
'input_dim':model._dec_log_std_sq.input_dim,
'output_dim':model._dec_log_std_sq.output_dim,
'act_fn':model._dec_log_std_sq.activation,
'W':model._dec_log_std_sq.weights['w'].eval(),
'b':model._dec_log_std_sq.weights['b'].eval()
}
save_dict['dec_log_std_sq'] = layer_dict
pickle.dump(save_dict, open(os.path.join(options['model_dir'], 'vae_dict_%d' % batch_abs_idx), 'wb'))
# Validate model
if np.mod(batch_abs_idx, options['freq_validation']) == 0:
model._decoder.layers[0].weights['w'].eval()[:5,:5]
valid_costs = []
seen_batches = 0
for val_batch in val_provider:
if isinstance(val_batch, tuple):
val_batch = val_batch[0]
val_cost = sess.run(
cost_function,
feed_dict = {
model_input_batch: val_batch
}
)
valid_costs.append(val_cost)
seen_batches += 1
if seen_batches == options['valid_batches']:
break
# Print results
log.info('Validation loss: {:0>15.4f}'.format(
float(np.mean(valid_costs))
))
val_samples = sess.run(
sampler,
feed_dict = {
sampler_input_batch: MVN(
np.zeros(options['latent_dims']),
np.diag(np.ones(options['latent_dims'])),
size = options['batch_size']
)
}
)
val_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.mean(valid_costs)))
val_log.flush()
save_ae_samples(
catalog,
np.reshape(result[7], [options['batch_size']]+options['img_shape']),
np.reshape(inputs, [options['batch_size']]+options['img_shape']),
np.reshape(val_samples, [options['batch_size']]+options['img_shape']),
batch_abs_idx,
options['dashboard_dir'],
num_to_save=5,
save_gray=True
)
# save_dash_samples(
# catalog,
# val_samples,
# batch_abs_idx,
# options['dashboard_dir'],
# flat_samples=True,
# img_shape=options['img_shape'],
# num_to_save=5
# )
# save_samples(
# val_samples,
# int(batch_abs_idx/options['freq_validation']),
# os.path.join(options['model_dir'], 'valid_samples'),
# True,
# options['img_shape'],
# 5
# )
# save_samples(
# inputs,
# int(batch_abs_idx/options['freq_validation']),
# os.path.join(options['model_dir'], 'input_sanity'),
# True,
# options['img_shape'],
# num_to_save=5
# )
# save_samples(
# result[7],
# int(batch_abs_idx/options['freq_validation']),
# os.path.join(options['model_dir'], 'rec_sanity'),
# True,
# options['img_shape'],
# num_to_save=5
# )
log.info('End of epoch {}'.format(epoch_idx + 1))
def train(options):
# Get logger
log = utils.get_logger(os.path.join(options['model_dir'], 'log.txt'))
options_file = open(os.path.join(options['dashboard_dir'], 'options'), 'w')
for optn in options:
options_file.write(optn)
options_file.write(':\t')
options_file.write(str(options[optn]))
options_file.write('\n')
options_file.close()
# Dashboard Catalog
catalog = open(os.path.join(options['dashboard_dir'], 'catalog'), 'w')
catalog.write("""filename,type,name
options,plain,Options
train_loss.csv,csv,Train Loss
val_loss.csv,csv,Validation Loss
""")
catalog.flush()
train_log = open(os.path.join(options['dashboard_dir'], 'train_loss.csv'),
'w')
val_log = open(os.path.join(options['dashboard_dir'], 'val_loss.csv'), 'w')
train_log.write('step,time,Train Loss\n')
val_log.write('step,time,Validation Loss\n')
# Print options
utils.print_options(options, log)
# Load dataset ----------------------------------------------------------------------
# Train provider
train_provider, val_provider, test_provider = get_providers(options, log)
# Initialize model ------------------------------------------------------------------
# input_shape, input_channels, enc_params, dec_params, name=''
with tf.device('/gpu:0'):
if options['model'] == 'cnn_ae':
model = cupboard(options['model'])(options['img_shape'],
options['input_channels'],
options['enc_params'],
options['dec_params'], 'cnn_ae')
# Define inputs
model_clean_input_batch = tf.placeholder(
tf.float32,
shape=[options['batch_size']] + options['img_shape'] +
[options['input_channels']],
name='clean')
model_noisy_input_batch = tf.placeholder(
tf.float32,
shape=[options['batch_size']] + options['img_shape'] +
[options['input_channels']],
name='noisy')
log.info('Inputs defined')
else:
model = cupboard(options['model'])(
np.prod(options['img_shape']) * options['input_channels'],
options['enc_params'], options['dec_params'], 'ae')
# Define inputs
model_clean_input_batch = tf.placeholder(
tf.float32,
shape=[options['batch_size']] +
[np.prod(options['img_shape']) * options['input_channels']],
name='clean')
model_noisy_input_batch = tf.placeholder(
tf.float32,
shape=[options['batch_size']] +
[np.prod(options['img_shape']) * options['input_channels']],
name='noisy')
log.info('Inputs defined')
log.info('Model initialized')
# Define forward pass
print(model_clean_input_batch.get_shape())
print(model_noisy_input_batch.get_shape())
cost_function = model(model_clean_input_batch, model_noisy_input_batch)
log.info('Forward pass graph built')
log.info('Sampler graph built')
# Define optimizer
optimizer = tf.train.AdamOptimizer(learning_rate=options['lr'])
# optimizer = tf.train.GradientDescentOptimizer(learning_rate=options['lr'])
# train_step = optimizer.minimize(cost_function)
log.info('Optimizer graph built')
# Get gradients
grads = optimizer.compute_gradients(cost_function)
grads = [gv for gv in grads if gv[0] != None]
grad_tensors = [gv[0] for gv in grads]
# Clip gradients
clip_grads = [(tf.clip_by_norm(gv[0], 5.0,
name='grad_clipping'), gv[1])
for gv in grads]
# Update op
backpass = optimizer.apply_gradients(clip_grads)
# Define init operation
init_op = tf.initialize_all_variables()
log.info('Variable initialization graph built')
# Define op to save and restore variables
saver = tf.train.Saver()
log.info('Save operation built')
# --------------------------------------------------------------------------
# Train loop ---------------------------------------------------------------
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
log.info('Session started')
# Initialize shared variables or restore
if options['reload']:
saver.restore(sess, os.path.join(options['model_dir'],
'model.ckpt'))
log.info('Shared variables restored')
else:
sess.run(init_op)
log.info('Shared variables initialized')
# Define last losses to compute a running average
last_losses = np.zeros((10))
batch_abs_idx = 0
for epoch_idx in xrange(options['n_epochs']):
batch_rel_idx = 0
log.info('Epoch {}'.format(epoch_idx + 1))
for inputs, _ in train_provider:
batch_abs_idx += 1
batch_rel_idx += 1
result = sess.run(
[cost_function, backpass] + [gv[0] for gv in grads],
feed_dict = {
model_clean_input_batch: inputs,
model_noisy_input_batch: np.float32(inputs) + \
normal(
loc=0.0,
scale=np.float32(options['noise_std']),
size=inputs.shape
)
}
)
cost = result[0]
if batch_abs_idx % 10 == 0:
train_log.write('{},{},{}\n'.format(
batch_abs_idx, '2016-04-22', np.mean(last_losses)))
train_log.flush()
# Check cost
if np.isnan(cost) or np.isinf(cost):
log.info('NaN detected')
for i in range(len(result)):
print("\n\nresult[%d]:" % i)
try:
print(np.any(np.isnan(result[i])))
except:
pass
print(result[i])
print(result[3].shape)
print(model._encoder.layers[0].weights['w'].eval())
print('\n\nAny:')
print(np.any(np.isnan(result[8])))
print(np.any(np.isnan(result[9])))
print(np.any(np.isnan(result[10])))
print(inputs)
return 1., 1., 1.
# Update last losses
last_losses = np.roll(last_losses, 1)
last_losses[0] = cost
# Display training information
if np.mod(epoch_idx, options['freq_logging']) == 0:
log.info(
'Epoch {:02}/{:02} Batch {:03} Current Loss: {:0>15.4f} Mean last losses: {:0>15.4f}'
.format(epoch_idx + 1, options['n_epochs'],
batch_abs_idx, float(cost),
np.mean(last_losses)))
# Save model
if np.mod(batch_abs_idx, options['freq_saving']) == 0:
saver.save(
sess,
os.path.join(options['model_dir'],
'model_at_%d.ckpt' % batch_abs_idx))
log.info('Model saved')
# Save Encoder Params
save_dict = {
'enc_W': [],
'enc_b': [],
'enc_act_fn': [],
}
if options['model'] == 'cnn_ae':
pass
else:
for i in range(len(model._encoder.layers)):
save_dict['enc_W'].append(
model._encoder.layers[i].weights['w'].eval())
save_dict['enc_b'].append(
model._encoder.layers[i].weights['b'].eval())
save_dict['enc_act_fn'].append(
options['enc_params']['act_fn'][i])
pickle.dump(
save_dict,
open(
os.path.join(options['model_dir'],
'enc_dict_%d' % batch_abs_idx), 'wb'))
# Validate model
if np.mod(batch_abs_idx, options['freq_validation']) == 0:
model._decoder.layers[0].weights['w'].eval()[:5, :5]
valid_costs = []
seen_batches = 0
for val_batch, _ in val_provider:
noisy_val_batch = val_batch + \
normal(
loc=0.0,
scale=np.float32(options['noise_std']),
size=val_batch.shape
)
val_results = sess.run(
(cost_function, model.decoder),
feed_dict={
model_clean_input_batch: val_batch,
model_noisy_input_batch: noisy_val_batch
})
valid_costs.append(val_results[0])
seen_batches += 1
if seen_batches == options['valid_batches']:
break
# Print results
log.info('Validation loss: {:0>15.4f}'.format(
float(np.mean(valid_costs))))
val_log.write('{},{},{}\n'.format(batch_abs_idx,
'2016-04-22',
np.mean(valid_costs)))
val_log.flush()
if options['model'] == 'conv_ae':
val_recon = np.reshape(val_results[-1],
val_batch.shape)
else:
val_batch = np.reshape(
val_batch, [val_batch.shape[0]] +
options['img_shape'] + [options['input_channels']])
noisy_val_batch = np.reshape(
noisy_val_batch, [val_batch.shape[0]] +
options['img_shape'] + [options['input_channels']])
val_recon = np.reshape(
val_results[-1], [val_batch.shape[0]] +
options['img_shape'] + [options['input_channels']])
save_ae_samples(catalog,
val_batch,
noisy_val_batch,
val_recon,
batch_abs_idx,
options['dashboard_dir'],
num_to_save=5,
save_gray=True)
# save_samples(
# val_recon,
# int(batch_abs_idx/options['freq_validation']),
# os.path.join(options['model_dir'], 'valid_samples'),
# False,
# options['img_shape'],
# 5
# )
# save_samples(
# inputs,
# int(batch_abs_idx/options['freq_validation']),
# os.path.join(options['model_dir'], 'input_sanity'),
# False,
# options['img_shape'],
# num_to_save=5
# )
log.info('End of epoch {}'.format(epoch_idx + 1))
def train(options):
# Get logger
log = utils.get_logger(os.path.join(options['model_dir'], 'log.txt'))
options_file = open(os.path.join(options['dashboard_dir'], 'options'), 'w')
options_file.write(options['description'] + '\n')
options_file.write(
'DKL Weight: {}\nLog Sigma^2 clipped to: [{}, {}]\n\n'.format(
options['DKL_weight'],
-options['sigma_clip'],
options['sigma_clip']
)
)
for optn in options:
options_file.write(optn)
options_file.write(':\t')
options_file.write(str(options[optn]))
options_file.write('\n')
options_file.close()
# Dashboard Catalog
catalog = open(os.path.join(options['dashboard_dir'], 'catalog'), 'w')
catalog.write(
"""filename,type,name
options,plain,Options
train_loss.csv,csv,Train Loss
ll.csv,csv,Neg. Log-Likelihood
dec_log_sig_sq.csv,csv,Decoder Log Simga^2
dec_std_log_sig_sq.csv,csv,STD of Decoder Log Simga^2
dec_mean.csv,csv,Decoder Mean
dkl.csv,csv,DKL
enc_log_sig_sq.csv,csv,Encoder Log Sigma^2
enc_std_log_sig_sq.csv,csv,STD of Encoder Log Sigma^2
enc_mean.csv,csv,Encoder Mean
val_loss.csv,csv,Validation Loss
"""
)
catalog.flush()
train_log = open(os.path.join(options['dashboard_dir'], 'train_loss.csv'), 'w')
val_log = open(os.path.join(options['dashboard_dir'], 'val_loss.csv'), 'w')
dkl_log = open(os.path.join(options['dashboard_dir'], 'dkl.csv'), 'w')
ll_log = open(os.path.join(options['dashboard_dir'], 'll.csv'), 'w')
dec_sig_log = open(os.path.join(options['dashboard_dir'], 'dec_log_sig_sq.csv'), 'w')
enc_sig_log = open(os.path.join(options['dashboard_dir'], 'enc_log_sig_sq.csv'), 'w')
dec_std_sig_log = open(os.path.join(options['dashboard_dir'], 'dec_std_log_sig_sq.csv'), 'w')
enc_std_sig_log = open(os.path.join(options['dashboard_dir'], 'enc_std_log_sig_sq.csv'), 'w')
dec_mean_log = open(os.path.join(options['dashboard_dir'], 'dec_mean.csv'), 'w')
enc_mean_log = open(os.path.join(options['dashboard_dir'], 'enc_mean.csv'), 'w')
# val_sig_log = open(os.path.join(options['dashboard_dir'], 'val_log_sig_sq.csv'), 'w')
train_log.write('step,time,Train Loss\n')
val_log.write('step,time,Validation Loss\n')
dkl_log.write('step,time,DKL\n')
ll_log.write('step,time,-LL\n')
dec_sig_log.write('step,time,Decoder Log Sigma^2\n')
enc_sig_log.write('step,time,Encoder Log Sigma^2\n')
dec_std_sig_log.write('step,time,STD of Decoder Log Sigma^2\n')
enc_std_sig_log.write('step,time,STD of Encoder Log Sigma^2\n')
dec_mean_log.write('step,time,Decoder Mean\n')
enc_mean_log.write('step,time,Encoder Mean\n')
# Print options
utils.print_options(options, log)
# Load dataset ----------------------------------------------------------------------
# Train provider
if options['data_dir'] != 'MNIST':
num_data_points = len(
os.listdir(
os.path.join(options['data_dir'], 'train', 'patches')
)
)
num_data_points -= 2
train_provider = DataProvider(
num_data_points,
options['batch_size'],
toolbox.ImageLoader(
data_dir = os.path.join(options['data_dir'], 'train', 'patches'),
flat=True,
extension=options['file_extension']
)
)
# Valid provider
num_data_points = len(
os.listdir(
os.path.join(options['data_dir'], 'valid', 'patches')
)
)
num_data_points -= 2
val_provider = DataProvider(
num_data_points,
options['batch_size'],
toolbox.ImageLoader(
data_dir = os.path.join(options['data_dir'], 'valid', 'patches'),
flat = True,
extension=options['file_extension']
)
)
else:
train_provider = DataProvider(
55000,
options['batch_size'],
toolbox.MNISTLoader(
mode='train',
flat=True
)
)
val_provider = DataProvider(
5000,
options['batch_size'],
toolbox.MNISTLoader(
mode='validation',
flat = True
)
)
log.info('Data providers initialized.')
# Initialize model ------------------------------------------------------------------
with tf.device('/gpu:0'):
# Feature Extractor -----------------------------------------------------
# feat_params = pickle.load(open(options['feat_params_path'], 'rb'))
# _classifier = Sequential('CNN_Classifier')
# _classifier += ConvLayer(
# options['input_channels'],
# feat_params[0]['n_filters_out'],
# feat_params[0]['input_dim'],
# feat_params[0]['filter_dim'],
# feat_params[0]['strides'],
# name='classifier_conv_0'
# )
# _classifier += feat_params[0]['act_fn']
# _classifier.layers[-2].weights['W'] = tf.constant(feat_params[0]['W'])
# _classifier.layers[-2].weights['b'] = tf.constant(feat_params[0]['b'])
# print("1 conv layer")
# i = 1
# while i < options['num_feat_layers']:
# if 'filter_dim' in feat_params[i]:
# _classifier += ConvLayer(
# feat_params[i]['n_filters_in'],
# feat_params[i]['n_filters_out'],
# feat_params[i]['input_dim'],
# feat_params[i]['filter_dim'],
# feat_params[i]['strides'],
# name='classifier_conv_0'
# )
# _classifier += feat_params[i]['act_fn']
# _classifier.layers[-2].weights['W'] = tf.constant(feat_params[i]['W'])
# _classifier.layers[-2].weights['b'] = tf.constant(feat_params[i]['b'])
# print("1 conv layer")
# else:
# _classifier += ConstFC(
# feat_params[i]['W'],
# feat_params[i]['b'],
# activation=feat_params[i]['act_fn'],
# name='classifier_fc_0'
# )
# print("1 fc layer")
# i += 1
if options['feat_type'] == 'fc':
feat_model = Sequential('feat_extractor')
feat_params = pickle.load(open(options['feat_params_path'], 'rb'))
for i in range(options['num_feat_layers']):
feat_model += ConstFC(
feat_params['enc_W'][i],
feat_params['enc_b'][i],
activation=feat_params['enc_act_fn'][i],
name='feat_layer_%d'%i
)
else:
pass
# VAE -------------------------------------------------------------------
# VAE model
vae_model = cupboard('vanilla_vae')(
options['p_layers'],
options['q_layers'],
np.prod(options['img_shape']),
options['latent_dims'],
options['DKL_weight'],
options['sigma_clip'],
'vanilla_vae'
)
# -----------------------------------------------------------------------
feat_vae = cupboard('feat_vae')(
vae_model,
feat_model,
options['DKL_weight'],
0.0,
img_shape=options['img_shape'],
input_channels=options['input_channels'],
flat=True,
name='feat_vae_model'
)
log.info('Model initialized')
# Define inputs
model_input_batch = tf.placeholder(
tf.float32,
shape = [options['batch_size'], np.prod(np.array(options['img_shape']))],
name = 'enc_inputs'
)
sampler_input_batch = tf.placeholder(
tf.float32,
shape = [options['batch_size'], options['latent_dims']],
name = 'dec_inputs'
)
log.info('Inputs defined')
# Define forward pass
cost_function = feat_vae(model_input_batch)
log.info('Forward pass graph built')
# Define sampler
sampler = feat_vae.build_sampler(sampler_input_batch)
log.info('Sampler graph built')
# Define optimizer
optimizer = tf.train.AdamOptimizer(
learning_rate=options['lr']
)
# optimizer = tf.train.GradientDescentOptimizer(learning_rate=options['lr'])
# train_step = optimizer.minimize(cost_function)
log.info('Optimizer graph built')
# Get gradients
grads = optimizer.compute_gradients(cost_function)
grads = [gv for gv in grads if gv[0] != None]
grad_tensors = [gv[0] for gv in grads]
# Clip gradients
clip_grads = [(tf.clip_by_norm(gv[0], 5.0, name='grad_clipping'), gv[1]) for gv in grads]
# Update op
backpass = optimizer.apply_gradients(clip_grads)
# Define init operation
init_op = tf.initialize_all_variables()
log.info('Variable initialization graph built')
# Define op to save and restore variables
saver = tf.train.Saver()
log.info('Save operation built')
# --------------------------------------------------------------------------
# Train loop ---------------------------------------------------------------
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
log.info('Session started')
# Initialize shared variables or restore
if options['reload']:
saver.restore(sess, options['reload_file'])
log.info('Shared variables restored')
if options['data_dir'] == 'MNIST':
mean_img = np.zeros(np.prod(options['img_shape']))
std_img = np.ones(np.prod(options['img_shape']))
else:
mean_img = np.load(os.path.join(options['data_dir'], 'mean' + options['extension']))
std_img = np.load(os.path.join(options['data_dir'], 'std' + options['extension']))
visualize(feat_vae.vae.sampler_mean, sess, feat_vae.vae.dec_mean, feat_vae.vae.dec_log_std_sq, sampler, sampler_input_batch,
model_input_batch, feat_vae.vae.enc_mean, feat_vae.vae.enc_log_std_sq,
train_provider, val_provider, options, catalog, mean_img, std_img)
return
else:
sess.run(init_op)
log.info('Shared variables initialized')
# Define last losses to compute a running average
last_losses = np.zeros((10))
batch_abs_idx = 0
for epoch_idx in xrange(options['n_epochs']):
batch_rel_idx = 0
log.info('Epoch {}'.format(epoch_idx + 1))
for inputs in train_provider:
batch_abs_idx += 1
batch_rel_idx += 1
result = sess.run(
# (cost_function, train_step, model.enc_std, model.enc_mean, model.encoder, model.dec_std, model.dec_mean, model.decoder, model.rec_loss, model.DKL),
# 0 1 2 3 4 5 6 7 8 9 10
[cost_function, backpass, feat_vae.vae.DKL, feat_vae.vae.rec_loss, feat_vae.vae.dec_log_std_sq, feat_vae.vae.enc_log_std_sq, feat_vae.vae.enc_mean, feat_vae.vae.dec_mean] + [gv[0] for gv in grads],
feed_dict = {
model_input_batch: inputs
}
)
cost = result[0]
if batch_abs_idx % 10 == 0:
train_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.mean(last_losses)))
dkl_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', -np.mean(result[2])))
ll_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', -np.mean(result[3])))
train_log.flush()
dkl_log.flush()
ll_log.flush()
dec_sig_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.mean(result[4])))
enc_sig_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.mean(result[5])))
# val_sig_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.mean(result[6])))
dec_sig_log.flush()
enc_sig_log.flush()
dec_std_sig_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.std(result[4])))
enc_std_sig_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.std(result[5])))
dec_mean_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.mean(result[7])))
enc_mean_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.mean(result[6])))
dec_std_sig_log.flush()
enc_std_sig_log.flush()
dec_mean_log.flush()
enc_mean_log.flush()
# val_sig_log.flush()
# Check cost
if np.isnan(cost) or np.isinf(cost):
log.info('NaN detected')
for i in range(len(result)):
print("\n\nresult[%d]:" % i)
try:
print(np.any(np.isnan(result[i])))
except:
pass
print(result[i])
print(result[3].shape)
print(model._encoder.layers[0].weights['w'].eval())
print('\n\nAny:')
print(np.any(np.isnan(result[8])))
print(np.any(np.isnan(result[9])))
print(np.any(np.isnan(result[10])))
print(inputs)
return 1., 1., 1.
# Update last losses
last_losses = np.roll(last_losses, 1)
last_losses[0] = cost
# Display training information
if np.mod(epoch_idx, options['freq_logging']) == 0:
log.info('Epoch {:02}/{:02} Batch {:03} Current Loss: {:0>15.4f} Mean last losses: {:0>15.4f}'.format(
epoch_idx + 1,
options['n_epochs'],
batch_abs_idx,
float(cost),
np.mean(last_losses)
))
log.info('Batch Mean LL: {:0>15.4f}'.format(np.mean(result[3], axis=0)))
log.info('Batch Mean -DKL: {:0>15.4f}'.format(np.mean(result[2], axis=0)))
# Save model
if np.mod(batch_abs_idx, options['freq_saving']) == 0:
saver.save(sess, os.path.join(options['model_dir'], 'model_at_%d.ckpt' % batch_abs_idx))
log.info('Model saved')
# Validate model
if np.mod(batch_abs_idx, options['freq_validation']) == 0:
valid_costs = []
seen_batches = 0
for val_batch in val_provider:
val_cost = sess.run(
cost_function,
feed_dict = {
model_input_batch: val_batch
}
)
valid_costs.append(val_cost)
seen_batches += 1
if seen_batches == options['valid_batches']:
break
# Print results
log.info('Validation loss: {:0>15.4f}'.format(
float(np.mean(valid_costs))
))
val_samples = sess.run(
sampler,
feed_dict = {
sampler_input_batch: MVN(
np.zeros(options['latent_dims']),
np.diag(np.ones(options['latent_dims'])),
size = options['batch_size']
)
}
)
val_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.mean(valid_costs)))
val_log.flush()
save_ae_samples(
catalog,
np.reshape(result[7], [options['batch_size']]+options['img_shape']),
np.reshape(inputs, [options['batch_size']]+options['img_shape']),
np.reshape(val_samples, [options['batch_size']]+options['img_shape']),
batch_abs_idx,
options['dashboard_dir'],
num_to_save=5,
save_gray=True
)
# save_dash_samples(
# catalog,
# val_samples,
# batch_abs_idx,
# options['dashboard_dir'],
# flat_samples=True,
# img_shape=options['img_shape'],
# num_to_save=5
# )
save_samples(
val_samples,
int(batch_abs_idx/options['freq_validation']),
os.path.join(options['model_dir'], 'valid_samples'),
True,
options['img_shape'],
5
)
save_samples(
inputs,
int(batch_abs_idx/options['freq_validation']),
os.path.join(options['model_dir'], 'input_sanity'),
True,
options['img_shape'],
num_to_save=5
)
save_samples(
result[7],
int(batch_abs_idx/options['freq_validation']),
os.path.join(options['model_dir'], 'rec_sanity'),
True,
options['img_shape'],
num_to_save=5
)
log.info('End of epoch {}'.format(epoch_idx + 1))
def train(options):
# Get logger
log = utils.get_logger(os.path.join(options['model_dir'], 'log.txt'))
options_file = open(os.path.join(options['dashboard_dir'], 'options'), 'w')
options_file.write(options['description'] + '\n')
options_file.write(
'DKL Weight: {}\nLog Sigma^2 clipped to: [{}, {}]\n\n'.format(
options['DKL_weight'],
-options['sigma_clip'],
options['sigma_clip']
)
)
for optn in options:
options_file.write(optn)
options_file.write(':\t')
options_file.write(str(options[optn]))
options_file.write('\n')
options_file.close()
# Dashboard Catalog
catalog = open(os.path.join(options['dashboard_dir'], 'catalog'), 'w')
catalog.write(
"""filename,type,name
options,plain,Options
train_loss.csv,csv,Train Loss
ll.csv,csv,Neg. Log-Likelihood
dec_log_sig_sq.csv,csv,Decoder Log Simga^2
dec_std_log_sig_sq.csv,csv,STD of Decoder Log Simga^2
dec_mean.csv,csv,Decoder Mean
dkl.csv,csv,DKL
enc_log_sig_sq.csv,csv,Encoder Log Sigma^2
enc_std_log_sig_sq.csv,csv,STD of Encoder Log Sigma^2
enc_mean.csv,csv,Encoder Mean
val_loss.csv,csv,Validation Loss
"""
)
catalog.flush()
train_log = open(os.path.join(options['dashboard_dir'], 'train_loss.csv'), 'w')
val_log = open(os.path.join(options['dashboard_dir'], 'val_loss.csv'), 'w')
dkl_log = open(os.path.join(options['dashboard_dir'], 'dkl.csv'), 'w')
ll_log = open(os.path.join(options['dashboard_dir'], 'll.csv'), 'w')
dec_sig_log = open(os.path.join(options['dashboard_dir'], 'dec_log_sig_sq.csv'), 'w')
enc_sig_log = open(os.path.join(options['dashboard_dir'], 'enc_log_sig_sq.csv'), 'w')
dec_std_sig_log = open(os.path.join(options['dashboard_dir'], 'dec_std_log_sig_sq.csv'), 'w')
enc_std_sig_log = open(os.path.join(options['dashboard_dir'], 'enc_std_log_sig_sq.csv'), 'w')
dec_mean_log = open(os.path.join(options['dashboard_dir'], 'dec_mean.csv'), 'w')
enc_mean_log = open(os.path.join(options['dashboard_dir'], 'enc_mean.csv'), 'w')
# val_sig_log = open(os.path.join(options['dashboard_dir'], 'val_log_sig_sq.csv'), 'w')
train_log.write('step,time,Train Loss\n')
val_log.write('step,time,Validation Loss\n')
dkl_log.write('step,time,DKL\n')
ll_log.write('step,time,-LL\n')
dec_sig_log.write('step,time,Decoder Log Sigma^2\n')
enc_sig_log.write('step,time,Encoder Log Sigma^2\n')
dec_std_sig_log.write('step,time,STD of Decoder Log Sigma^2\n')
enc_std_sig_log.write('step,time,STD of Encoder Log Sigma^2\n')
dec_mean_log.write('step,time,Decoder Mean\n')
enc_mean_log.write('step,time,Encoder Mean\n')
# Print options
utils.print_options(options, log)
# Load dataset ----------------------------------------------------------------------
# Train provider
train_provider, val_provider, test_provider = get_providers(options, log, flat=True)
# Initialize model ------------------------------------------------------------------
with tf.device('/gpu:0'):
# Define inputs ----------------------------------------------------------
model_input_batch = tf.placeholder(
tf.float32,
shape = [options['batch_size'], np.prod(np.array(options['img_shape']))],
name = 'enc_inputs'
)
sampler_input_batch = tf.placeholder(
tf.float32,
shape = [options['batch_size'], options['latent_dims']],
name = 'dec_inputs'
)
log.info('Inputs defined')
# Feature Extractor -----------------------------------------------------
feat_layers = []
feat_params = pickle.load(open(options['feat_params_path'], 'rb'))
_classifier = Sequential('CNN_Classifier')
conv_count, pool_count, fc_count = 0, 0, 0
for lay in feat_params:
print(lay['layer_type'])
for i in xrange(options['num_feat_layers']):
if feat_params[i]['layer_type'] == 'conv':
_classifier += ConvLayer(
feat_params[i]['n_filters_in'],
feat_params[i]['n_filters_out'],
feat_params[i]['input_dim'],
feat_params[i]['filter_dim'],
feat_params[i]['strides'],
name='classifier_conv_%d' % conv_count
)
_classifier.layers[-1].weights['W'] = tf.constant(feat_params[i]['W'])
_classifier.layers[-1].weights['b'] = tf.constant(feat_params[i]['b'])
_classifier += feat_params[i]['act_fn']
conv_count += 1
elif feat_params[i]['layer_type'] == 'pool':
_classifier += PoolLayer(
feat_params[i]['input_dim'],
feat_params[i]['filter_dim'],
feat_params[i]['strides'],
name='classifier_pool_%d' % i
)
pool_count += 1
feat_layers.append(i)
elif feat_params[i]['layer_type'] == 'fc':
_classifier += ConstFC(
feat_params[i]['W'],
feat_params[i]['b'],
activation=feat_params[i]['act_fn'],
name='classifier_fc_%d' % fc_count
)
fc_count += 1
feat_layers.append(i)
# if options['feat_type'] == 'fc':
# feat_model = Sequential('feat_extractor')
# feat_params = pickle.load(open(options['feat_params_path'], 'rb'))
# for i in range(options['num_feat_layers']):
# feat_model += ConstFC(
# feat_params['enc_W'][i],
# feat_params['enc_b'][i],
# activation=feat_params['enc_act_fn'][i],
# name='feat_layer_%d'%i
# )
# else:
# pass
# VAE -------------------------------------------------------------------
# VAE model
vae_model = cupboard('vanilla_vae')(
options['p_layers'],
options['q_layers'],
np.prod(options['img_shape']),
options['latent_dims'],
options['DKL_weight'],
options['sigma_clip'],
'vanilla_vae'
)
# -----------------------------------------------------------------------
feat_vae = cupboard('feat_vae')(
vae_model,
_classifier,
feat_layers,
options['DKL_weight'],
options['vae_rec_loss_weight'],
img_shape=options['img_shape'],
input_channels=options['input_channels'],
flat=False,
name='feat_vae_model'
)
log.info('Model initialized')
# Define forward pass
cost_function = feat_vae(model_input_batch)
log.info('Forward pass graph built')
# Define sampler
sampler = feat_vae.build_sampler(sampler_input_batch)
log.info('Sampler graph built')
# Define optimizer
optimizer = tf.train.AdamOptimizer(
learning_rate=options['lr']
)
# optimizer = tf.train.GradientDescentOptimizer(learning_rate=options['lr'])
# train_step = optimizer.minimize(cost_function)
log.info('Optimizer graph built')
# Get gradients
grads = optimizer.compute_gradients(cost_function)
grads = [gv for gv in grads if gv[0] != None]
grad_tensors = [gv[0] for gv in grads]
# Clip gradients
clip_grads = [(tf.clip_by_norm(gv[0], 5.0, name='grad_clipping'), gv[1]) for gv in grads]
# Update op
backpass = optimizer.apply_gradients(clip_grads)
# Define init operation
init_op = tf.initialize_all_variables()
log.info('Variable initialization graph built')
# Define op to save and restore variables
saver = tf.train.Saver()
log.info('Save operation built')
# --------------------------------------------------------------------------
# Train loop ---------------------------------------------------------------
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
log.info('Session started')
# Initialize shared variables or restore
if options['reload']:
saver.restore(sess, options['reload_file'])
log.info('Shared variables restored')
test_LL_and_DKL(sess, test_provider, feat_vae.vae.DKL, feat_vae.vae.rec_loss, options, model_input_batch)
return
mean_img = np.load(os.path.join(options['data_dir'], 'mean' + options['extension']))
std_img = np.load(os.path.join(options['data_dir'], 'std' + options['extension']))
visualize(sess, feat_vae.vae.dec_mean, feat_vae.vae.dec_log_std_sq, sampler, sampler_input_batch,
model_input_batch, feat_vae.vae.enc_mean, feat_vae.vae.enc_log_std_sq,
train_provider, val_provider, options, catalog, mean_img, std_img)
return
else:
sess.run(init_op)
log.info('Shared variables initialized')
# Define last losses to compute a running average
last_losses = np.zeros((10))
batch_abs_idx = 0
for epoch_idx in xrange(options['n_epochs']):
batch_rel_idx = 0
log.info('Epoch {}'.format(epoch_idx + 1))
for inputs,_ in train_provider:
batch_abs_idx += 1
batch_rel_idx += 1
result = sess.run(
# (cost_function, train_step, model.enc_std, model.enc_mean, model.encoder, model.dec_std, model.dec_mean, model.decoder, model.rec_loss, model.DKL),
# 0 1 2 3 4 5 6 7 8 9 10
[cost_function, backpass, feat_vae.vae.DKL, feat_vae.vae.rec_loss, feat_vae.vae.dec_log_std_sq, feat_vae.vae.enc_log_std_sq, feat_vae.vae.enc_mean, feat_vae.vae.dec_mean] + [gv[0] for gv in grads],
feed_dict = {
model_input_batch: inputs
}
)
cost = result[0]
if batch_abs_idx % 10 == 0:
train_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.mean(last_losses)))
dkl_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', -np.mean(result[2])))
ll_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', -np.mean(result[3])))
train_log.flush()
dkl_log.flush()
ll_log.flush()
dec_sig_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.mean(result[4])))
enc_sig_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.mean(result[5])))
# val_sig_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.mean(result[6])))
dec_sig_log.flush()
enc_sig_log.flush()
dec_std_sig_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.std(result[4])))
enc_std_sig_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.std(result[5])))
dec_mean_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.mean(result[7])))
enc_mean_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.mean(result[6])))
dec_std_sig_log.flush()
enc_std_sig_log.flush()
dec_mean_log.flush()
enc_mean_log.flush()
# val_sig_log.flush()
# Check cost
if np.isnan(cost) or np.isinf(cost):
log.info('NaN detected')
for i in range(len(result)):
print("\n\nresult[%d]:" % i)
try:
print(np.any(np.isnan(result[i])))
except:
pass
print(result[i])
print(result[3].shape)
print(model._encoder.layers[0].weights['w'].eval())
print('\n\nAny:')
print(np.any(np.isnan(result[8])))
print(np.any(np.isnan(result[9])))
print(np.any(np.isnan(result[10])))
print(inputs)
return 1., 1., 1.
# Update last losses
last_losses = np.roll(last_losses, 1)
last_losses[0] = cost
# Display training information
if np.mod(epoch_idx, options['freq_logging']) == 0:
log.info('Epoch {:02}/{:02} Batch {:03} Current Loss: {:0>15.4f} Mean last losses: {:0>15.4f}'.format(
epoch_idx + 1,
options['n_epochs'],
batch_abs_idx,
float(cost),
np.mean(last_losses)
))
log.info('Batch Mean LL: {:0>15.4f}'.format(np.mean(result[3], axis=0)))
log.info('Batch Mean -DKL: {:0>15.4f}'.format(np.mean(result[2], axis=0)))
# Save model
if np.mod(batch_abs_idx, options['freq_saving']) == 0:
saver.save(sess, os.path.join(options['model_dir'], 'model_at_%d.ckpt' % batch_abs_idx))
log.info('Model saved')
# Validate model
if np.mod(batch_abs_idx, options['freq_validation']) == 0:
valid_costs = []
seen_batches = 0
for val_batch,_ in val_provider:
val_cost = sess.run(
cost_function,
feed_dict = {
model_input_batch: val_batch
}
)
valid_costs.append(val_cost)
seen_batches += 1
if seen_batches == options['valid_batches']:
break
# Print results
log.info('Validation loss: {:0>15.4f}'.format(
float(np.mean(valid_costs))
))
val_samples = sess.run(
sampler,
feed_dict = {
sampler_input_batch: MVN(
np.zeros(options['latent_dims']),
np.diag(np.ones(options['latent_dims'])),
size = options['batch_size']
)
}
)
val_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.mean(valid_costs)))
val_log.flush()
save_ae_samples(
catalog,
np.reshape(result[7], [options['batch_size']]+options['img_shape']),
np.reshape(inputs, [options['batch_size']]+options['img_shape']),
np.reshape(val_samples, [options['batch_size']]+options['img_shape']),
batch_abs_idx,
options['dashboard_dir'],
num_to_save=5,
save_gray=True
)
# save_dash_samples(
# catalog,
# val_samples,
# batch_abs_idx,
# options['dashboard_dir'],
# flat_samples=True,
# img_shape=options['img_shape'],
# num_to_save=5
# )
save_samples(
val_samples,
int(batch_abs_idx/options['freq_validation']),
os.path.join(options['model_dir'], 'valid_samples'),
True,
options['img_shape'],
5
)
save_samples(
inputs,
int(batch_abs_idx/options['freq_validation']),
os.path.join(options['model_dir'], 'input_sanity'),
True,
options['img_shape'],
num_to_save=5
)
save_samples(
result[7],
int(batch_abs_idx/options['freq_validation']),
os.path.join(options['model_dir'], 'rec_sanity'),
True,
options['img_shape'],
num_to_save=5
)
log.info('End of epoch {}'.format(epoch_idx + 1))
# Test Model --------------------------------------------------------------------------
test_results = []
for inputs in test_provider:
if isinstance(inputs, tuple):
inputs = inputs[0]
batch_results = sess.run(
[
feat_vae.vae.DKL, feat_vae.vae.rec_loss,
feat_vae.vae.dec_log_std_sq, feat_vae.vae.enc_log_std_sq,
feat_vae.vae.dec_mean, feat_vae.vae.enc_mean
],
feed_dict = {
model_input_batch: inputs
}
)
test_results.append(map(lambda p: np.mean(p, axis=1) if len(p.shape) > 1 else np.mean(p), batch_results))
test_results = map(list, zip(*test_results))
# Print results
log.info('Test Mean Rec. Loss: {:0>15.4f}'.format(
float(np.mean(test_results[1]))
))
log.info('Test DKL: {:0>15.4f}'.format(
float(np.mean(test_results[0]))
))
log.info('Test Dec. Mean Log Std Sq: {:0>15.4f}'.format(
float(np.mean(test_results[2]))
))
log.info('Test Enc. Mean Log Std Sq: {:0>15.4f}'.format(
float(np.mean(test_results[3]))
))
log.info('Test Dec. Mean Mean: {:0>15.4f}'.format(
float(np.mean(test_results[4]))
))
log.info('Test Enc. Mean Mean: {:0>15.4f}'.format(
float(np.mean(test_results[5]))
))
def train(options):
# Get logger
log = utils.get_logger(os.path.join(options['model_dir'], 'log.txt'))
options_file = open(os.path.join(options['dashboard_dir'], 'options'), 'w')
options_file.write(options['description'] + '\n')
options_file.write(
'DKL Weight: {}\nLog Sigma^2 clipped to: [{}, {}]\n\n'.format(
options['DKL_weight'], -options['sigma_clip'],
options['sigma_clip']))
for optn in options:
options_file.write(optn)
options_file.write(':\t')
options_file.write(str(options[optn]))
options_file.write('\n')
options_file.close()
# Dashboard Catalog
catalog = open(os.path.join(options['dashboard_dir'], 'catalog'), 'w')
catalog.write("""filename,type,name
options,plain,Options
train_loss.csv,csv,Train Loss
val_loss.csv,csv,Validation Loss
""")
catalog.flush()
train_log = open(os.path.join(options['dashboard_dir'], 'train_loss.csv'),
'w')
val_log = open(os.path.join(options['dashboard_dir'], 'val_loss.csv'), 'w')
train_log.write('step,time,Train Loss\n')
val_log.write('step,time,Validation Loss\n')
# Print options
utils.print_options(options, log)
# Load dataset ----------------------------------------------------------------------
# Train provider
train_provider, val_provider, test_provider = get_providers(options,
log,
flat=True)
# Initialize model ------------------------------------------------------------------
with tf.device('/gpu:0'):
model = cupboard(options['model'])(
options['p_layers'], options['q_layers'],
np.prod(options['img_shape']), options['latent_dims'],
options['DKL_weight'], options['sigma_clip'], 'vanilla_vae')
log.info('Model initialized')
# Define inputs
model_input_batch = tf.placeholder(
tf.float32,
shape=[
options['batch_size'],
np.prod(np.array(options['img_shape']))
],
name='enc_inputs')
model_label_batch = tf.placeholder(
tf.float32,
shape=[options['batch_size'], options['num_classes']],
name='labels')
log.info('Inputs defined')
# Load VAE
model(model_input_batch)
feat_params = pickle.load(open(options['feat_params_path'], 'rb'))
for i in range(len(model._encoder.layers)):
model._encoder.layers[i].weights['w'] = tf.constant(
feat_params[i]['W'])
model._encoder.layers[i].weights['b'] = tf.constant(
feat_params[i]['b'])
model._enc_mean.weights['w'] = tf.constant(feat_params[-2]['W'])
model._enc_mean.weights['b'] = tf.constant(feat_params[-2]['b'])
model._enc_log_std_sq.weights['w'] = tf.constant(feat_params[-1]['W'])
model._enc_log_std_sq.weights['b'] = tf.constant(feat_params[-1]['b'])
enc_std = tf.exp(tf.mul(0.5, model.enc_log_std_sq))
classifier = FC(
model.latent_dims,
options['num_classes'],
activation=None,
scale=0.01,
name='classifier_fc')(tf.add(
tf.mul(tf.random_normal([model.n_samples, model.latent_dims]),
enc_std), model.enc_mean))
classifier = tf.nn.softmax(classifier)
cost_function = -tf.mul(model_label_batch, tf.log(classifier))
cost_function = tf.reduce_sum(cost_function)
cost_function *= 1 / float(options['batch_size'])
log.info('Forward pass graph built')
# Define optimizer
optimizer = tf.train.AdamOptimizer(learning_rate=options['lr'])
# optimizer = tf.train.GradientDescentOptimizer(learning_rate=options['lr'])
# train_step = optimizer.minimize(cost_function)
# Get gradients
grads = optimizer.compute_gradients(cost_function)
grads = [gv for gv in grads if gv[0] != None]
grad_tensors = [gv[0] for gv in grads]
# Clip gradients
clip_grads = [(tf.clip_by_norm(gv[0], 5.0,
name='grad_clipping'), gv[1])
for gv in grads]
# Update op
backpass = optimizer.apply_gradients(clip_grads)
log.info('Optimizer graph built')
# # Get gradients
# grad = optimizer.compute_gradients(cost_function)
# # Clip gradients
# clipped_grad = tf.clip_by_norm(grad, 5.0, name='grad_clipping')
# # Update op
# backpass = optimizer.apply_gradients(clipped_grad)
# Define init operation
init_op = tf.initialize_all_variables()
log.info('Variable initialization graph built')
# Define op to save and restore variables
saver = tf.train.Saver()
log.info('Save operation built')
# --------------------------------------------------------------------------
# Train loop ---------------------------------------------------------------
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
log.info('Session started')
# Initialize shared variables or restore
if options['reload']:
saver.restore(sess, os.path.join(options['model_dir'],
'model.ckpt'))
log.info('Shared variables restored')
else:
sess.run(init_op)
log.info('Shared variables initialized')
# Define last losses to compute a running average
last_losses = np.zeros((10))
last_accs = np.zeros((10))
batch_abs_idx = 0
for epoch_idx in xrange(options['n_epochs']):
batch_rel_idx = 0
log.info('Epoch {}'.format(epoch_idx + 1))
for inputs, labels in train_provider:
batch_abs_idx += 1
batch_rel_idx += 1
result = sess.run(
# (cost_function, train_step, model.enc_std, model.enc_mean, model.encoder, model.dec_std, model.dec_mean, model.decoder, model.rec_loss, model.DKL),
# 0 1 2 3 4 5 6 7 8 9 10
[cost_function, backpass, classifier] +
[gv[0] for gv in grads],
feed_dict={
model_input_batch: inputs,
model_label_batch: labels
})
cost = result[0]
if batch_abs_idx % 10 == 0:
train_log.write('{},{},{}\n'.format(
batch_abs_idx, '2016-04-22', np.mean(last_losses)))
train_log.flush()
# Check cost
if np.isnan(cost) or np.isinf(cost):
log.info('NaN detected')
for i in range(len(result)):
print("\n\nresult[%d]:" % i)
try:
print(np.any(np.isnan(result[i])))
except:
pass
print(result[i])
print(result[3].shape)
print(model._encoder.layers[0].weights['w'].eval())
print('\n\nAny:')
print(np.any(np.isnan(result[8])))
print(np.any(np.isnan(result[9])))
print(np.any(np.isnan(result[10])))
print(inputs)
return 1., 1., 1.
# Update last losses
last_losses = np.roll(last_losses, 1)
last_losses[0] = cost
last_accs = np.roll(last_accs, 1)
last_accs[0] = np.mean(
np.argmax(labels, axis=1) == np.argmax(result[2], axis=1))
# Display training information
if np.mod(epoch_idx, options['freq_logging']) == 0:
log.info(
'Epoch {:02}/{:02} Batch {:03} Current Acc.: {:0>15.4f} Mean last accs: {:0>15.4f}'
.format(epoch_idx + 1, options['n_epochs'],
batch_abs_idx, last_accs[0],
np.mean(last_accs)))
log.info('Batch Mean Loss: {:0>15.4f}'.format(
np.mean(last_losses)))
# Save model
if np.mod(batch_abs_idx, options['freq_saving']) == 0:
saver.save(
sess,
os.path.join(options['model_dir'],
'model_at_%d.ckpt' % batch_abs_idx))
log.info('Model saved')
# Validate model
if np.mod(batch_abs_idx, options['freq_validation']) == 0:
valid_costs = []
seen_batches = 0
for val_batch, labels in val_provider:
val_result = sess.run(
[cost_function, classifier],
feed_dict={
model_input_batch: val_batch,
model_label_batch: labels
})
val_cost = np.mean(
np.argmax(labels, axis=1) == np.argmax(
val_result[1], axis=1))
valid_costs.append(val_cost)
seen_batches += 1
if seen_batches == options['valid_batches']:
break
# Print results
log.info('Validation acc.: {:0>15.4f}'.format(
float(np.mean(valid_costs))))
val_log.write('{},{},{}\n'.format(batch_abs_idx,
'2016-04-22',
np.mean(valid_costs)))
val_log.flush()
log.info('End of epoch {}'.format(epoch_idx + 1))
# --------------------------------------------------------------------------
test_results = []
for inputs, labels in test_provider:
if isinstance(inputs, tuple):
inputs = inputs[0]
batch_results = sess.run([cost_function, classifier],
feed_dict={
model_input_batch: inputs,
model_label_batch: labels
})
test_results.append(
np.mean(
np.argmax(labels, axis=1) == np.argmax(batch_results[1],
axis=1)))
# Print results
log.info('Test Accuracy: {:0>15.4f}'.format(np.mean(test_results)))
def train(options):
# Get logger
log = utils.get_logger(os.path.join(options['model_dir'], 'log.txt'))
options_file = open(os.path.join(options['dashboard_dir'], 'options'), 'w')
options_file.write(options['description'] + '\n')
for optn in options:
options_file.write(optn)
options_file.write(':\t')
options_file.write(str(options[optn]))
options_file.write('\n')
options_file.close()
# Dashboard Catalog
catalog = open(os.path.join(options['dashboard_dir'], 'catalog'), 'w')
catalog.write("""filename,type,name
options,plain,Options
train_loss.csv,csv,Train Loss
train_acc.csv,csv,Train Accuracy
val_loss.csv,csv,Validation Loss
val_acc.csv,csv,Validation Accuracy
""")
catalog.flush()
train_log = open(os.path.join(options['dashboard_dir'], 'train_loss.csv'),
'w')
train_acc_log = open(
os.path.join(options['dashboard_dir'], 'train_acc.csv'), 'w')
val_log = open(os.path.join(options['dashboard_dir'], 'val_loss.csv'), 'w')
val_acc_log = open(os.path.join(options['dashboard_dir'], 'val_acc.csv'),
'w')
train_log.write('step,time,Train Loss\n')
val_log.write('step,time,Validation Loss\n')
train_acc_log.write('step,time,Train Accuracy\n')
val_acc_log.write('step,time,Validation Accuracy\n')
# Print options
utils.print_options(options, log)
# Load dataset ----------------------------------------------------------------------
# Train provider
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
train_data = mnist.train.images
train_labels = mnist.train.labels
validation_data = mnist.validation.images
validation_labels = mnist.validation.labels
test_data = mnist.test.images
test_labels = mnist.test.labels
data_percentage = options['data_percentage']
print(train_data.shape)
log.info('Data providers initialized.')
# Initialize model ------------------------------------------------------------------
with tf.device('/gpu:0'):
model = cupboard(options['model'])(
options['img_shape'], options['input_channels'],
options['num_classes'], options['conv_params'],
options['pool_params'], options['fc_params'], 'MNIST_classifier')
log.info('Model initialized')
# Define inputs
input_batch = tf.placeholder(tf.float32,
shape=[options['batch_size']] +
options['img_shape'] +
[options['input_channels']],
name='inputs')
label_batch = tf.placeholder(
tf.float32,
shape=[options['batch_size'], options['num_classes']],
name='labels')
keep_prob = tf.placeholder(tf.float32, name='keep_prob')
log.info('Inputs defined')
# Define forward pass
cost_function, classifier = model(input_batch, label_batch, keep_prob)
log.info('Forward pass graph built')
# Define optimizer
optimizer = tf.train.AdamOptimizer(learning_rate=options['lr'])
# train_step = optimizer.minimize(cost_function)
log.info('Optimizer graph built')
# Get gradients
grads = optimizer.compute_gradients(cost_function)
grads = [gv for gv in grads if gv[0] != None]
grad_tensors = [gv[0] for gv in grads]
# Clip gradients
clip_grads = [(tf.clip_by_norm(gv[0], 5.0,
name='grad_clipping'), gv[1])
for gv in grads]
# Update op
backpass = optimizer.apply_gradients(clip_grads)
# Define init operation
init_op = tf.initialize_all_variables()
log.info('Variable initialization graph built')
# Define op to save and restore variables
saver = tf.train.Saver()
log.info('Save operation built')
# --------------------------------------------------------------------------
# Train loop ---------------------------------------------------------------
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
log.info('Session started')
# Initialize shared variables or restore
if options['reload']:
saver.restore(sess, os.path.join(options['model_dir'],
'model.ckpt'))
log.info('Shared variables restored')
else:
sess.run(init_op)
log.info('Shared variables initialized')
# Define last losses to compute a running average
last_losses = np.zeros((10))
last_accs = np.zeros((10))
batch_abs_idx = 0
for epoch_idx in xrange(options['n_epochs']):
batch_rel_idx = 0
log.info('Epoch {}'.format(epoch_idx + 1))
for i in xrange(
int((data_percentage * train_data.shape[0]) /
options['batch_size'])):
inputs = np.reshape(train_data[i:i + options['batch_size'], :],
[options['batch_size'], 28, 28, 1])
labels = train_labels[i:i + options['batch_size'], :]
batch_abs_idx += 1
batch_rel_idx += 1
results = sess.run([cost_function, classifier, backpass] +
[gv[0] for gv in grads],
feed_dict={
input_batch: inputs,
label_batch: labels,
keep_prob: 0.5
})
cost = results[0]
# Check cost
if np.isnan(cost) or np.isinf(cost):
log.info('NaN detected')
return 1., 1., 1.
accuracy = np.mean(
np.argmax(results[1], axis=1) == np.argmax(labels, axis=1))
# Update last losses
last_losses = np.roll(last_losses, 1)
last_losses[0] = cost
last_accs = np.roll(last_accs, 1)
last_accs[0] = accuracy
if batch_abs_idx % 10 == 0:
train_log.write('{},{},{}\n'.format(
batch_abs_idx, '2016-04-22', np.mean(last_losses)))
train_acc_log.write('{},{},{}\n'.format(
batch_abs_idx, '2016-04-22', np.mean(last_accs)))
train_log.flush()
train_acc_log.flush()
# Display training information
if np.mod(epoch_idx, options['freq_logging']) == 0:
log.info(
'Epoch {:02}/{:02} Batch {:03} Current Loss: {:0>15.4f} Mean last losses: {:0>15.4f}'
.format(epoch_idx + 1, options['n_epochs'],
batch_abs_idx, float(cost),
np.mean(last_losses)))
log.info(
'Epoch {:02}/{:02} Batch {:03} Current Accuracy: {:0>15.4f}'
.format(epoch_idx + 1, options['n_epochs'],
batch_abs_idx, np.mean(last_accs)))
# Save model
if np.mod(batch_abs_idx, options['freq_saving']) == 0:
saver.save(
sess,
os.path.join(options['model_dir'],
'model_at_%d.ckpt' % batch_abs_idx))
save_dict = []
for c_ind in xrange(0, len(model._classifier_conv.layers)):
if isinstance(model._classifier_conv.layers[c_ind],
ConvLayer):
layer_dict = {
'n_filters_in':
model._classifier_conv.layers[c_ind].
n_filters_in,
'n_filters_out':
model._classifier_conv.layers[c_ind].
n_filters_out,
'input_dim':
model._classifier_conv.layers[c_ind].input_dim,
'filter_dim':
model._classifier_conv.layers[c_ind].
filter_dim,
'strides':
model._classifier_conv.layers[c_ind].strides,
'padding':
model._classifier_conv.layers[c_ind].padding,
'act_fn':
model._classifier_conv.layers[c_ind + 1],
'W':
model._classifier_conv.layers[c_ind].
weights['W'].eval(),
'b':
model._classifier_conv.layers[c_ind].
weights['b'].eval(),
'layer_type':
'conv'
}
save_dict.append(layer_dict)
elif isinstance(model._classifier_conv.layers[c_ind],
PoolLayer):
layer_dict = {
'input_dim':
model._classifier_conv.layers[c_ind].input_dim,
'filter_dim':
model._classifier_conv.layers[c_ind].
filter_dim,
'strides':
model._classifier_conv.layers[c_ind].strides,
'layer_type':
'pool'
}
save_dict.append(layer_dict)
for c_ind in xrange(0,
len(model._classifier_fc.layers) - 2,
2):
layer_dict = {
'input_dim':
model._classifier_fc.layers[c_ind].input_dim,
'output_dim':
model._classifier_fc.layers[c_ind].output_dim,
'act_fn':
model.fc_params['act_fn'][c_ind],
'W':
model._classifier_fc.layers[c_ind].weights['w'].
eval(),
'b':
model._classifier_fc.layers[c_ind].weights['b'].
eval(),
'layer_type':
'fc'
}
save_dict.append(layer_dict)
pickle.dump(
save_dict,
open(
os.path.join(options['model_dir'],
'class_dict_%d' % batch_abs_idx),
'wb'))
log.info('Model saved')
# Save params for feature vae training later
# conv_feat = deepcopy(model._classifier_conv)
# for lay_ind in range(0,len(conv_feat.layers),2):
# conv_feat[lay_ind].weights['W'] = tf.constant(conv_feat[lay_ind].weights['W'].eval())
# conv_feat[lay_ind].weights['b'] = tf.constant(conv_feat[lay_ind].weights['b'].eval())
# pickle(conv_feat, open(os.path.join(options['model_dir'], 'classifier_conv_feat_%d' % batch_abs_idx), 'wb'))
# Validate model
if np.mod(batch_abs_idx, options['freq_validation']) == 0:
valid_costs = []
val_accuracies = []
seen_batches = 0
for j in xrange(
int((data_percentage * validation_data.shape[0]) /
options['batch_size'])):
val_batch = np.reshape(
validation_data[j:j + options['batch_size'], :],
[options['batch_size'], 28, 28, 1])
val_label = validation_labels[j:j +
options['batch_size'], :]
# Break if 10 batches seen for now
if seen_batches == options['valid_batches']:
break
val_results = sess.run(
[cost_function, classifier],
feed_dict={
input_batch: val_batch,
label_batch: val_label,
keep_prob: 1.0
})
val_cost = val_results[0]
valid_costs.append(val_cost)
seen_batches += 1
val_accuracies.append(
np.mean(
np.argmax(val_results[1], axis=1) == np.argmax(
val_label, axis=1)))
# Print results
log.info('Mean Validation loss: {:0>15.4f}'.format(
float(np.mean(valid_costs))))
log.info('Mean Validation Accuracy: {:0>15.4f}'.format(
np.mean(val_accuracies)))
val_log.write('{},{},{}\n'.format(
batch_abs_idx, '2016-04-22',
float(np.mean(valid_costs))))
val_acc_log.write('{},{},{}\n'.format(
batch_abs_idx, '2016-04-22', np.mean(val_accuracies)))
val_log.flush()
val_acc_log.flush()
log.info('End of epoch {}'.format(epoch_idx + 1))
# --------------------------------------------------------------------------
test_costs = []
test_accuracies = []
for j in xrange(test_data.shape[0] / options['batch_size']):
test_batch = np.reshape(test_data[j:j + options['batch_size'], :],
[options['batch_size'], 28, 28, 1])
test_label = test_labels[j:j + options['batch_size'], :]
test_results = sess.run([cost_function, classifier],
feed_dict={
input_batch: test_batch,
label_batch: test_label,
keep_prob: 1.0
})
test_cost = test_results[0]
test_costs.append(test_cost)
test_accuracies.append(
np.mean(
np.argmax(test_results[1], axis=1) == np.argmax(test_label,
axis=1)))
# Print results
log.info('Test loss: {:0>15.4f}'.format(float(np.mean(test_costs))))
log.info('Test Accuracy: {:0>15.4f}'.format(np.mean(test_accuracies)))
def train(options):
# Get logger
log = utils.get_logger(os.path.join(options['model_dir'], 'log.txt'))
options_file = open(os.path.join(options['dashboard_dir'], 'options'), 'w')
options_file.write(options['description'] + '\n')
options_file.write(
'DKL Weight: {}\nLog Sigma^2 clipped to: [{}, {}]\n\n'.format(
options['DKL_weight'],
-options['sigma_clip'],
options['sigma_clip']
)
)
for optn in options:
options_file.write(optn)
options_file.write(':\t')
options_file.write(str(options[optn]))
options_file.write('\n')
options_file.close()
# Dashboard Catalog
catalog = open(os.path.join(options['dashboard_dir'], 'catalog'), 'w')
catalog.write(
"""filename,type,name
options,plain,Options
train_loss.csv,csv,Discriminator Cross-Entropy
ll.csv,csv,Neg. Log-Likelihood
dec_log_sig_sq.csv,csv,Decoder Log Simga^2
dec_std_log_sig_sq.csv,csv,STD of Decoder Log Simga^2
dec_mean.csv,csv,Decoder Mean
dkl.csv,csv,DKL
enc_log_sig_sq.csv,csv,Encoder Log Sigma^2
enc_std_log_sig_sq.csv,csv,STD of Encoder Log Sigma^2
enc_mean.csv,csv,Encoder Mean
val_loss.csv,csv,Validation Loss
"""
)
catalog.flush()
train_log = open(os.path.join(options['dashboard_dir'], 'train_loss.csv'), 'w')
val_log = open(os.path.join(options['dashboard_dir'], 'val_loss.csv'), 'w')
dkl_log = open(os.path.join(options['dashboard_dir'], 'dkl.csv'), 'w')
ll_log = open(os.path.join(options['dashboard_dir'], 'll.csv'), 'w')
dec_sig_log = open(os.path.join(options['dashboard_dir'], 'dec_log_sig_sq.csv'), 'w')
enc_sig_log = open(os.path.join(options['dashboard_dir'], 'enc_log_sig_sq.csv'), 'w')
dec_std_sig_log = open(os.path.join(options['dashboard_dir'], 'dec_std_log_sig_sq.csv'), 'w')
enc_std_sig_log = open(os.path.join(options['dashboard_dir'], 'enc_std_log_sig_sq.csv'), 'w')
dec_mean_log = open(os.path.join(options['dashboard_dir'], 'dec_mean.csv'), 'w')
enc_mean_log = open(os.path.join(options['dashboard_dir'], 'enc_mean.csv'), 'w')
# val_sig_log = open(os.path.join(options['dashboard_dir'], 'val_log_sig_sq.csv'), 'w')
train_log.write('step,time,Train CE (Training Vanilla),Train CE (Training Gen.),Train CE (Training Disc.)\n')
val_log.write('step,time,Validation CE (Training Vanilla),Validation CE (Training Gen.),Validation CE (Training Disc.)\n')
dkl_log.write('step,time,DKL (Training Vanilla),DKL (Training Gen.),DKL (Training Disc.)\n')
ll_log.write('step,time,-LL (Training Vanilla),-LL (Training Gen.),-LL (Training Disc.)\n')
dec_sig_log.write('step,time,Decoder Log Sigma^2 (Training Vanilla),Decoder Log Sigma^2 (Training Gen.),Decoder Log Sigma^2 (Training Disc.)\n')
enc_sig_log.write('step,time,Encoder Log Sigma^2 (Training Vanilla),Encoder Log Sigma^2 (Training Gen.),Encoder Log Sigma^2 (Training Disc.)\n')
dec_std_sig_log.write('step,time,STD of Decoder Log Sigma^2 (Training Vanilla),STD of Decoder Log Sigma^2 (Training Gen.),STD of Decoder Log Sigma^2 (Training Disc.)\n')
enc_std_sig_log.write('step,time,STD of Encoder Log Sigma^2 (Training Vanilla),STD of Encoder Log Sigma^2 (Training Gen.),STD of Encoder Log Sigma^2 (Training Disc.)\n')
dec_mean_log.write('step,time,Decoder Mean (Training Vanilla),Decoder Mean (Training Gen.),Decoder Mean (Training Disc.)\n')
enc_mean_log.write('step,time,Encoder Mean (Training Vanilla),Encoder Mean (Training Gen.),Encoder Mean (Training Disc.)\n')
# Print options
utils.print_options(options, log)
# Load dataset ----------------------------------------------------------------------
# Train provider
train_provider, val_provider, test_provider = get_providers(options, log, flat=True)
# Initialize model ------------------------------------------------------------------
with tf.device('/gpu:0'):
# Define inputs
model_input_batch = tf.placeholder(
tf.float32,
shape = [options['batch_size'], np.prod(np.array(options['img_shape']))],
name = 'enc_inputs'
)
sampler_input_batch = tf.placeholder(
tf.float32,
shape = [options['batch_size'], options['latent_dims']],
name = 'dec_inputs'
)
log.info('Inputs defined')
# Define model
with tf.variable_scope('vae_scope'):
vae_model = cupboard('vanilla_vae')(
options['p_layers'],
options['q_layers'],
np.prod(options['img_shape']),
options['latent_dims'],
options['DKL_weight'],
options['sigma_clip'],
'vae_model'
)
with tf.variable_scope('disc_scope'):
disc_model = cupboard('fixed_conv_disc')(
pickle.load(open(options['disc_params_path'], 'rb')),
options['num_feat_layers'],
name='disc_model'
)
vae_gan = cupboard('vae_gan')(
vae_model,
disc_model,
options['disc_weight'],
options['img_shape'],
options['input_channels'],
'vae_scope',
'disc_scope',
name='vae_gan_model'
)
# Define Optimizers ---------------------------------------------------------------------
optimizer = tf.train.AdamOptimizer(
learning_rate=options['lr']
)
vae_backpass, disc_backpass, vanilla_backpass = vae_gan(model_input_batch, sampler_input_batch, optimizer)
log.info('Optimizer graph built')
# --------------------------------------------------------------------------------------
# Define init operation
init_op = tf.initialize_all_variables()
log.info('Variable initialization graph built')
# Define op to save and restore variables
saver = tf.train.Saver()
log.info('Save operation built')
# --------------------------------------------------------------------------
# Train loop ---------------------------------------------------------------
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
log.info('Session started')
# Initialize shared variables or restore
if options['reload_all']:
saver.restore(sess, options['reload_file'])
log.info('Shared variables restored')
else:
sess.run(init_op)
log.info('Variables initialized')
if options['reload_vae']:
vae_model.reload_vae(options['vae_params_path'])
# Define last losses to compute a running average
last_losses = np.zeros((10))
batch_abs_idx = 0
D_to_G = options['D_to_G']
total_D2G = sum(D_to_G)
base = options['initial_G_iters'] + options['initial_D_iters']
for epoch_idx in xrange(options['n_epochs']):
batch_rel_idx = 0
log.info('Epoch {}'.format(epoch_idx + 1))
for inputs in train_provider:
if isinstance(inputs, tuple):
inputs = inputs[0]
batch_abs_idx += 1
batch_rel_idx += 1
if batch_abs_idx < options['initial_G_iters']:
backpass = vanilla_backpass
log_format_string = '{},{},{},,\n'
elif options['initial_G_iters'] <= batch_abs_idx < base:
backpass = disc_backpass
log_format_string = '{},{},,,{}\n'
else:
if (batch_abs_idx - base) % total_D2G < D_to_G[0]:
backpass = disc_backpass
log_format_string = '{},{},,,{}\n'
else:
backpass = vae_backpass
log_format_string = '{},{},,{},\n'
result = sess.run(
[
vae_gan.disc_CE,
backpass,
vae_gan._vae.DKL,
vae_gan._vae.rec_loss,
vae_gan._vae.dec_log_std_sq,
vae_gan._vae.enc_log_std_sq,
vae_gan._vae.enc_mean,
vae_gan._vae.dec_mean
],
feed_dict = {
model_input_batch: inputs,
sampler_input_batch: MVN(
np.zeros(options['latent_dims']),
np.diag(np.ones(options['latent_dims'])),
size = options['batch_size']
)
}
)
cost = result[0]
if batch_abs_idx % 10 == 0:
train_log.write(log_format_string.format(batch_abs_idx, '2016-04-22', np.mean(last_losses)))
dkl_log.write(log_format_string.format(batch_abs_idx, '2016-04-22', -np.mean(result[2])))
ll_log.write(log_format_string.format(batch_abs_idx, '2016-04-22', -np.mean(result[3])))
train_log.flush()
dkl_log.flush()
ll_log.flush()
dec_sig_log.write(log_format_string.format(batch_abs_idx, '2016-04-22', np.mean(result[4])))
enc_sig_log.write(log_format_string.format(batch_abs_idx, '2016-04-22', np.mean(result[5])))
# val_sig_log.write('{},{},{}\n'.format(batch_abs_idx, '2016-04-22', np.mean(result[6])))
dec_sig_log.flush()
enc_sig_log.flush()
dec_std_sig_log.write(log_format_string.format(batch_abs_idx, '2016-04-22', np.std(result[4])))
enc_std_sig_log.write(log_format_string.format(batch_abs_idx, '2016-04-22', np.std(result[5])))
dec_mean_log.write(log_format_string.format(batch_abs_idx, '2016-04-22', np.mean(result[7])))
enc_mean_log.write(log_format_string.format(batch_abs_idx, '2016-04-22', np.mean(result[6])))
dec_std_sig_log.flush()
enc_std_sig_log.flush()
dec_mean_log.flush()
enc_mean_log.flush()
# Check cost
if np.isnan(cost) or np.isinf(cost):
log.info('NaN detected')
for i in range(len(result)):
print("\n\nresult[%d]:" % i)
try:
print(np.any(np.isnan(result[i])))
except:
pass
print(result[i])
print(result[3].shape)
print(vae_gan._vae._encoder.layers[0].weights['w'].eval())
print('\n\nAny:')
print(np.any(np.isnan(result[8])))
print(np.any(np.isnan(result[9])))
print(np.any(np.isnan(result[10])))
print(inputs)
return 1., 1., 1.
# Update last losses
last_losses = np.roll(last_losses, 1)
last_losses[0] = cost
# Display training information
if np.mod(epoch_idx, options['freq_logging']) == 0:
log.info('Epoch {:02}/{:02} Batch {:03} Current Loss: {:0>15.4f} Mean last losses: {:0>15.4f}'.format(
epoch_idx + 1,
options['n_epochs'],
batch_abs_idx,
float(cost),
np.mean(last_losses)
))
log.info('Batch Mean LL: {:0>15.4f}'.format(np.mean(result[3], axis=0)))
log.info('Batch Mean -DKL: {:0>15.4f}'.format(np.mean(result[2], axis=0)))
# Save model
if np.mod(batch_abs_idx, options['freq_saving']) == 0:
saver.save(sess, os.path.join(options['model_dir'], 'model_at_%d.ckpt' % batch_abs_idx))
log.info('Model saved')
save_dict = {}
# Save encoder params ------------------------------------------------------------------
for i in range(len(vae_gan._vae._encoder.layers)):
layer_dict = {
'input_dim':vae_gan._vae._encoder.layers[i].input_dim,
'output_dim':vae_gan._vae._encoder.layers[i].output_dim,
'act_fn':vae_gan._vae._encoder.layers[i].activation,
'W':vae_gan._vae._encoder.layers[i].weights['w'].eval(),
'b':vae_gan._vae._encoder.layers[i].weights['b'].eval()
}
save_dict['encoder'] = layer_dict
layer_dict = {
'input_dim':vae_gan._vae._enc_mean.input_dim,
'output_dim':vae_gan._vae._enc_mean.output_dim,
'act_fn':vae_gan._vae._enc_mean.activation,
'W':vae_gan._vae._enc_mean.weights['w'].eval(),
'b':vae_gan._vae._enc_mean.weights['b'].eval()
}
save_dict['enc_mean'] = layer_dict
layer_dict = {
'input_dim':vae_gan._vae._enc_log_std_sq.input_dim,
'output_dim':vae_gan._vae._enc_log_std_sq.output_dim,
'act_fn':vae_gan._vae._enc_log_std_sq.activation,
'W':vae_gan._vae._enc_log_std_sq.weights['w'].eval(),
'b':vae_gan._vae._enc_log_std_sq.weights['b'].eval()
}
save_dict['enc_log_std_sq'] = layer_dict
# Save decoder params ------------------------------------------------------------------
for i in range(len(vae_gan._vae._decoder.layers)):
layer_dict = {
'input_dim':vae_gan._vae._decoder.layers[i].input_dim,
'output_dim':vae_gan._vae._decoder.layers[i].output_dim,
'act_fn':vae_gan._vae._decoder.layers[i].activation,
'W':vae_gan._vae._decoder.layers[i].weights['w'].eval(),
'b':vae_gan._vae._decoder.layers[i].weights['b'].eval()
}
save_dict['decoder'] = layer_dict
layer_dict = {
'input_dim':vae_gan._vae._dec_mean.input_dim,
'output_dim':vae_gan._vae._dec_mean.output_dim,
'act_fn':vae_gan._vae._dec_mean.activation,
'W':vae_gan._vae._dec_mean.weights['w'].eval(),
'b':vae_gan._vae._dec_mean.weights['b'].eval()
}
save_dict['dec_mean'] = layer_dict
layer_dict = {
'input_dim':vae_gan._vae._dec_log_std_sq.input_dim,
'output_dim':vae_gan._vae._dec_log_std_sq.output_dim,
'act_fn':vae_gan._vae._dec_log_std_sq.activation,
'W':vae_gan._vae._dec_log_std_sq.weights['w'].eval(),
'b':vae_gan._vae._dec_log_std_sq.weights['b'].eval()
}
save_dict['dec_log_std_sq'] = layer_dict
pickle.dump(save_dict, open(os.path.join(options['model_dir'], 'vae_dict_%d' % batch_abs_idx), 'wb'))
# Validate model
if np.mod(batch_abs_idx, options['freq_validation']) == 0:
vae_gan._vae._decoder.layers[0].weights['w'].eval()[:5,:5]
valid_costs = []
seen_batches = 0
for val_batch in val_provider:
if isinstance(val_batch, tuple):
val_batch = val_batch[0]
val_cost = sess.run(
vae_gan.disc_CE,
feed_dict = {
model_input_batch: val_batch,
sampler_input_batch: MVN(
np.zeros(options['latent_dims']),
np.diag(np.ones(options['latent_dims'])),
size = options['batch_size']
)
}
)
valid_costs.append(val_cost)
seen_batches += 1
if seen_batches == options['valid_batches']:
break
# Print results
log.info('Validation loss: {:0>15.4f}'.format(
float(np.mean(valid_costs))
))
val_samples = sess.run(
vae_gan.sampler,
feed_dict = {
sampler_input_batch: MVN(
np.zeros(options['latent_dims']),
np.diag(np.ones(options['latent_dims'])),
size = options['batch_size']
)
}
)
val_log.write(log_format_string.format(batch_abs_idx, '2016-04-22', np.mean(valid_costs)))
val_log.flush()
save_ae_samples(
catalog,
np.reshape(result[7], [options['batch_size']]+options['img_shape']),
np.reshape(inputs, [options['batch_size']]+options['img_shape']),
np.reshape(val_samples, [options['batch_size']]+options['img_shape']),
batch_abs_idx,
options['dashboard_dir'],
num_to_save=5,
save_gray=True
)
log.info('End of epoch {}'.format(epoch_idx + 1))
