""" feed_dict = { re_model.input_x: x_batch, re_model.input_y: y_batch, re_model.dropout_keep_prob: 1.0 } step, summaries, loss, accuracy = sess.run( [global_step, dev_summary_op, re_model.loss, re_model.accuracy], feed_dict) time_str = datetime.datetime.now().isoformat() print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy)) if writer: writer.add_summary(summaries, step) # Generate batches batches = dataManager.batch_iter( list(zip(x_train, y_train)), FLAGS.batch_size, FLAGS.num_epochs) num_batches_per_epoch = int(len(x_train)/FLAGS.batch_size) + 1 print("Batch data") # Training loop. For each batch... num_batch = 1 num_epoch = 1 for batch in batches: if num_batch == num_batches_per_epoch: num_epoch += 1 num_batch = 1 num_batch += 1 x_batch, y_batch = zip(*batch) train_step(x_batch, y_batch) current_step = tf.train.global_step(sess, global_step) if current_step % FLAGS.evaluate_every == 0: print("Num_batch: {}".format(num_batch))
with graph.as_default(): session_conf = tf.ConfigProto( allow_soft_placement=FLAGS.allow_soft_placement, log_device_placement=FLAGS.log_device_placement) sess = tf.Session(config=session_conf) with sess.as_default(): # Load the saved meta graph and restore variables saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file)) saver.restore(sess, checkpoint_file) # Get the placeholders from the graph by name input_x = graph.get_operation_by_name("input_x").outputs[0] # input_y = graph.get_operation_by_name("input_y").outputs[0] dropout_keep_prob = graph.get_operation_by_name("dropout_keep_prob").outputs[0] hidden_feature = graph.get_operation_by_name("hidden_feature").outputs[0] # Tensors we want to evaluate predictions = graph.get_operation_by_name("output/predictions").outputs[0] # Generate batches for one epoch batches = dataManager.batch_iter(list(x_test), FLAGS.batch_size, 1, shuffle=False) # Collect the predictions here all_predictions = [] for x_test_batch in batches: relation_feature = sess.run(hidden_feature, {input_x: x_test_batch, dropout_keep_prob: 1.0}) for f in relation_feature: np.save(output_file, f)