tf.initialize_all_variables().run()
mean_time_per_epoch = 0
start_time = time.time()
print('\n' * 2)
for epoch in range(conf.num_epochs):
# additional variables to track the model
losses = 0
batch_index = 0
# Model training
current_learning_rate = conf.learning_rate * (conf.decay_rate**epoch)
sess.run(tf.assign(train_model.learning_rate, current_learning_rate))
state = train_model.initial_state.eval()
for batch_x, batch_y in reader.generateXYPairs():
# run_metadata = tf.RunMetadata()
feed_dict = {
train_model.input_x: batch_x,
train_model.input_y: batch_y,
train_model.initial_state: state,
}
summary, perplexity, cost_res, loss_res, state, _ = sess.run(
[
train_model.merged, train_model.perplexity,
train_model.cost, train_model.loss,
train_model.final_state, train_model.train_operation
],
feed_dict=feed_dict)
losses += loss_res
batch_index += 1
