def add_latents_to_dataset_using_tensors(args, sess, tensors, data):
""" Get latent representations from model.
Args:
args: Arguments from parser in train_grocerystore.py.
sess: Tensorflow session.
tensors: Tensors used for extracting latent representations.
data: Data used during epoch.
Returns:
Data dictionary filled with latent representations.
"""
latents = sess.run(tensors['latents'],
feed_dict={tensors['x']: data['features']})
data['latents'] = latents
if args.use_private:
latents_ux = sess.run(tensors['latents_ux'],
feed_dict={tensors['x']: data['features']})
data['latents_ux'] = latents_ux
if args.use_text:
all_captions = load_captions(data['captions'], data['labels'])
latents_uw = sess.run(
tensors['latents_uw'],
feed_dict={tensors['captions']: all_captions})
data['latents_uw'] = latents_uw
if args.use_iconic:
batch_size = args.batch_size
n_examples = len(data['iconic_image_paths'])
n_batches = int(np.ceil(n_examples / batch_size))
latents_ui = np.zeros([n_examples, args.z_dim])
for i in range(n_batches):
start = i * batch_size
end = start + batch_size
if end > n_examples:
end = n_examples
iconic_images = load_iconic_images(
data['iconic_image_paths'][start:end])
latents_ui[start:end] = sess.run(
tensors['latents_ui'],
feed_dict={tensors['iconic_images']: iconic_images})
data['latents_ui'] = latents_ui
return data
def run_training_epoch(args, data, model, hyperparams, session, train_op=None, shuffle=False, mode='train'):
""" Execute training epoch for Autoencoder.
Args:
args: Arguments from parser in train_grocerystore.py.
data: Data used during epoch.
model: Model used during epoch.
hyperparams: Hyperparameters for training.
session: Tensorflow session.
train_op: Op for computing gradients and updating parameters in model.
shuffle: For shuffling data before epoch.
mode: Training/validation mode.
Returns:
Metrics in python dictionary.
"""
# Hyperparameters
batch_size = hyperparams['batch_size']
dropout_rate = hyperparams['dropout_rate']
kl_weight = hyperparams['kl_weight']
is_training = hyperparams['is_training']
# Data
features = data['features']
labels = data['labels']
captions = data['captions']
iconic_image_path = data['iconic_image_paths']
n_classes = data['n_classes']
n_examples = len(features)
n_batches = int(np.ceil(n_examples/batch_size))
if shuffle:
perm = np.random.permutation(n_examples)
features = features[perm]
iconic_image_path = iconic_image_path[perm]
labels = labels[perm]
total_loss = 0.
x_loss = 0.
i_loss = 0.
w_loss = 0.
clf_loss = 0.
accuracy = 0.
for i in range(n_batches):
start = i * batch_size
end = start + batch_size
if end > n_examples:
end = n_examples
# Prepare batch and hyperparameters
x_batch = features[start:end]
i_batch = load_iconic_images(iconic_image_path[start:end])
captions_batch = load_captions(captions, labels[start:end])
labels_batch = onehot_encode(labels[start:end], n_classes)
feed_dict={model.x: x_batch, model.iconic_images: i_batch, model.captions: captions_batch, model.labels: labels_batch,
model.dropout_rate: dropout_rate, model.is_training: is_training}
if mode == 'train':
# Training step
train_step_results = session.run([train_op] + model.log_var, feed_dict=feed_dict)
total_loss += train_step_results[1]
x_loss += np.sum(train_step_results[2])
i_loss += np.sum(train_step_results[3])
w_loss += np.sum(train_step_results[4])
clf_loss += np.sum(train_step_results[5])
accuracy += np.sum(train_step_results[6])
elif mode == 'val':
# Validation step
val_step_results = session.run(model.val_log_var, feed_dict=feed_dict)
total_loss += val_step_results[0]
x_loss += np.sum(val_step_results[1])
i_loss += np.sum(val_step_results[2])
w_loss += np.sum(val_step_results[3])
clf_loss += np.sum(val_step_results[4])
accuracy += np.sum(val_step_results[5])
else:
raise ValueError("Argument \'mode\' %s doesn't exist!" %mode)
# Epoch finished, return results. clf_loss and accuracy are zero if args.classifier_head is False
results = {'total_loss': total_loss / n_batches, 'x_loss': x_loss / n_examples,
'i_loss': i_loss / n_examples, 'w_loss': w_loss / n_examples,
'clf_loss': clf_loss / n_examples, 'accuracy': accuracy / n_batches}
return results