with tf.Session(graph=lstm_graph) as sess: tf.global_variables_initializer().run() train_score = [] validation_score = [] for epoch in range(config.max_epoch): print( "epoch ****************************************************************************", epoch) # lr_decay = config.learing_rate_decay ** max(float(epoch + 1 - config.init_epoch), 0.0) # current_lr = config.init_learning_rate * lr_decay # print("learning_rate............................", current_lr) # batch_index = 0 for batch_X, batch_Y in utils.generate_one_epoch( train_data, train_labels, config.batch_size, config.num_steps): train_data_feed = { inputs: batch_X, targets: batch_Y, learning_rate: config.learning_rate, keep_prob: config.keep_prob } train_loss, _ = sess.run([loss, optimizer], train_data_feed) # batch_index += 1 # print accuracy of train model for each 50 batch # if batch_index % 1000 == 0: # print("train_loss: ", train_loss) # output training score and validation score for each epoch
], [ validation_data, validation_labels, "validation", validation_reader ]]: data = each[0] labels = each[1] which_data = each[2] reader = each[3] final_predict = np.array([]) total_loss = 0 num_loss = 0 print( "Data.....................................................................", which_data) for batch_X, batch_Y in utils.generate_one_epoch( data, labels, config.batch_size, shuffle=False): data_feed = { inputs: batch_X, targets: batch_Y, keep_prob: 1.0 } loss_p, predict = sess.run([loss, prediction], data_feed) total_loss += loss_p num_loss += 1 # predict = predict[0] final_predict = np.append(final_predict, predict) # print "losssssssssssssssssssssssssssssssssssssssssssssssssssssssssss", which_data, total_loss/num_loss final_predict = np.array(final_predict).reshape( len(final_predict), 1) stages = [utils.get_state(x) for x in final_predict] non_eruption_stage = [