e-lin

optimizer = tf.train.AdamOptimizer(learning_rate)

with tf.name_scope("phase1"):
    phase1_outputs = tf.matmul(hidden1, weights4) + biases4  # bypass hidden2 and hidden3
    phase1_reconstruction_loss = tf.reduce_mean(tf.square(phase1_outputs - X))
    phase1_reg_loss = regularizer(weights1) + regularizer(weights4)
    phase1_loss = phase1_reconstruction_loss + phase1_reg_loss
    phase1_training_op = optimizer.minimize(phase1_loss)

with tf.name_scope("phase2"):

e-lin

training_phrases = {

    'when_is_open' : ' '.join([
        'when is the restaurant open?',
        'when to start open?'
    ]),

    'where_is_the_restaurant' : ' '.join([
        'where is the restaurant',
        'what is the location of the restaurant?',