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
