Exemplos de get_predictions em Python

Exemplo n.º 1

# Create embedding lookup
identity_mat = tf.diag(tf.ones(shape=[vocab_length]))
address1_embed = tf.nn.embedding_lookup(identity_mat, address1_ph)
address2_embed = tf.nn.embedding_lookup(identity_mat, address2_ph)

# Define Model
text_snn = model.snn(address1_embed, address2_embed, dropout_keep_prob_ph,
                     vocab_length, num_features, max_address_len)

# Define Accuracy
batch_accuracy = model.accuracy(text_snn, y_target_ph)
# Define Loss
batch_loss = model.loss(text_snn, y_target_ph, margin)
# Define Predictions
predictions = model.get_predictions(text_snn)

# Declare optimizer
optimizer = tf.train.AdamOptimizer(0.01)
# Apply gradients
train_op = optimizer.minimize(batch_loss)

# Initialize Variables
init = tf.global_variables_initializer()
sess.run(init)

# Train loop
train_loss_vec = []
train_acc_vec = []
for b in range(n_batches):
    # Get a batch of data

Exemplo n.º 2

Arquivo: 06_siamese_similarity_driver.py Projeto: Bluebear171/tensorflow_cookbook

# Create embedding lookup
identity_mat = tf.diag(tf.ones(shape=[vocab_length]))
address1_embed = tf.nn.embedding_lookup(identity_mat, address1_ph)
address2_embed = tf.nn.embedding_lookup(identity_mat, address2_ph)

# Define Model
text_snn = model.snn(address1_embed, address2_embed, dropout_keep_prob_ph,
                     vocab_length, num_features, max_address_len)

# Define Accuracy
batch_accuracy = model.accuracy(text_snn, y_target_ph)
# Define Loss
batch_loss = model.loss(text_snn, y_target_ph, margin)
# Define Predictions
predictions = model.get_predictions(text_snn)

# Declare optimizer
optimizer = tf.train.AdamOptimizer(0.01)
# Apply gradients
train_op = optimizer.minimize(batch_loss)

# Initialize Variables
init = tf.global_variables_initializer()
sess.run(init)

# Train loop
train_loss_vec = []
train_acc_vec = []
for b in range(n_batches):
    # Get a batch of data