cost_hist_train, cost_hist_test, value_hist_train, value_hist_test, value_hist_cv, value_hist_train_ma, \ value_hist_test_ma, value_hist_cv_ma, step, step_hist, saving_score = [ ], [], [], [], [], [], [], [], 0, [], 0.05 saver = tf.train.Saver() init = tf.global_variables_initializer() sess = tf.Session() sess.run(init) # main loop while True: if step == 2000: break # train model x_train, y_train = get_data_batch( [input_train, output_train], batch_size, sequential=False) _, cost_train = sess.run([train_step, cost], feed_dict={x: x_train, y: y_train, learning_r: learning_rate, drop_out: drop_keep_prob}) # keep track of stuff step += 1 if step % 1 == 0 or step == 1: # get y_ predictions y_train_pred = sess.run( y_, feed_dict={x: input_train, drop_out: drop_keep_prob}) y_test_pred, cost_test = sess.run( [y_, cost], feed_dict={x: input_test, y: output_test, drop_out: drop_keep_prob}) y_cv_pred = sess.run( y_, feed_dict={x: input_cv, drop_out: drop_keep_prob})
value_hist_test_ma, value_hist_cv_ma, step, step_hist, saving_score = [ ], [], [], [], [], [], [], [], 0, [], 0.05 saver = tf.train.Saver() init = tf.global_variables_initializer() sess = tf.Session() sess.run(init) # train while True: if step == 30000: break # train model x_train, price_batch = get_data_batch([input_train[:-1], price_train[1:]], batch_size, sequential=False) _, cost_train = sess.run( [train_step, cost], feed_dict={ x: x_train, price_h: price_batch, learning_r: learning_rate, drop_out: drop_keep_prob }) # keep track of stuff step += 1 if step % 100 == 0 or step == 1: # get y_ predictions