def run_epoch(session, m, vectors_data, labels_data, eval_op, verbose=False): """Runs the model on the given data.""" epoch_size = ((len(vectors_data) // m.batch_size) - 1) // m.num_steps start_time = time.time() costs = 0.0 iters = 0 state = m.initial_state.eval() true_count = 0 for step, (x, y) in enumerate(read_data.ptb_iterator(vectors_data, labels_data, m.batch_size, m.num_steps)): cost, state, out_logits, _ = session.run([m.cost, m.final_state, m.out_logits, eval_op], {m.input_data: x, m.targets: y, m.initial_state: state}) costs += cost iters += m.num_steps labels = tf.reshape(y, [-1]) correct = tf.nn.in_top_k(tf.cast(out_logits, tf.float32), tf.cast(labels, tf.int32), 1) eval_correct = tf.reduce_sum(tf.cast(correct, tf.int32)) true_count += session.run(eval_correct) if verbose and step % (epoch_size // 10) == 10: print("%.3f perplexity: %.3f speed: %.0f wps" % (step * 1.0 / epoch_size, np.exp(costs / iters), iters * m.batch_size / (time.time() - start_time))) precision = true_count/(iters*m.batch_size) print("precision:") print(precision) return np.exp(costs / iters)
def run_epoch(session, m, vectors_data, labels_data, eval_op, summary_writer, verbose=False): """Runs the model on the given data.""" epoch_size = ((len(vectors_data) // m.batch_size) - 1) // m.num_steps start_time = time.time() costs = 0.0 iters = 0 state = m.initial_state.eval() true_count = 0 for step, (x, y) in enumerate(read_data.ptb_iterator(vectors_data, labels_data, m.batch_size, m.num_steps)): cost, state, out_logits, merged1, _ = session.run([m.cost, m.final_state, m.out_logits, m.merged, eval_op], {m.input_data: x, m.targets: y, m.initial_state: state}) if (m.num_steps != 1): summary_writer.add_summary(merged1,step) costs += cost iters += m.num_steps labels = tf.reshape(y, [-1]) correct = tf.nn.in_top_k(tf.cast(out_logits, tf.float32), tf.cast(labels, tf.int32), 1, name = "correct") eval_correct = tf.reduce_sum(tf.cast(correct, tf.int32)) true_count += session.run(eval_correct) if verbose and step % (epoch_size // 10) == 10: print("%.3f perplexity: %.3f speed: %.0f wps" % (step * 1.0 / epoch_size, np.exp(costs / iters), iters * m.batch_size / (time.time() - start_time))) with tf.name_scope("perplexity") as scope: perplexity = np.exp(costs / iters) perplexity_summary = tf.scalar_summary("perplexity",perplexity) with tf.name_scope("precision") as scope: precision = true_count/(iters*m.batch_size) print("precision:") print(precision) accuracy_summary = tf.scalar_summary("accuracy", precision) merged_2 = tf.merge_summary([perplexity_summary,accuracy_summary]) merged2 = session.run(merged_2) summary_writer.add_summary(merged2) return perplexity