axis=1) # zero pad the end but we don't actually use it
z_mask = 1.0 - raw_d
z_targ = tf.concat([z_targ, z_mask],
axis=2) # use a signal to not pass grad
if args.env_name == 'CarRacing-v0':
outputs = z_targ
else:
d_mask = tf.concat([
tf.ones([args.rnn_batch_size, 1, 1], dtype=tf.float16),
1.0 - raw_d[:, :-1, :]
],
axis=1)
d_targ = tf.concat([raw_d, d_mask], axis=2)
outputs = [z_targ, d_targ]
loss = rnn.train_on_batch(x=inputs, y=outputs)
if (step % 20 == 0 and step > 0):
end = time.time()
time_taken = end - start
start = time.time()
if args.env_name == 'CarRacing-v0':
output_log = "step: %d, lr: %.6f, loss: %.4f, train_time_taken: %.4f" % (
step, curr_learning_rate, loss, time_taken)
else:
output_log = "step: %d, lr: %.6f, loss: %.4f, z_loss: %.4f, d_loss: %.4f, train_time_taken: %.4f" % (
step, curr_learning_rate, loss[0], loss[1], loss[2],
time_taken)
print(output_log)
tf.keras.models.save_model(rnn,
rnn._set_inputs(inputs)
dummy_zero = tf.zeros([raw_z.shape[0], 1, raw_z.shape[2]], dtype=tf.float16)
z_targ = tf.concat([raw_z[:, 1:, :], dummy_zero], axis=1) # zero pad the end but we don't actually use it
z_mask = 1.0 - raw_d
z_targ = tf.concat([z_targ, z_mask], axis=2) # use a signal to not pass grad
outputs = {'MDN': z_targ}
if args.rnn_r_pred == 1:
r_mask = tf.concat([tf.ones([args.rnn_batch_size, 1, 1], dtype=tf.float16), 1.0 - raw_d[:, :-1, :]], axis=1)
r_targ = tf.concat([raw_r, r_mask], axis=2)
outputs['r'] = r_targ
if args.rnn_d_pred == 1:
d_mask = tf.concat([tf.ones([args.rnn_batch_size, 1, 1], dtype=tf.float16), 1.0 - raw_d[:, :-1, :]], axis=1)
d_targ = tf.concat([raw_d, d_mask], axis=2)
outputs['d'] = d_targ
loss = rnn.train_on_batch(x=inputs, y=outputs, return_dict=True)
[tf.summary.scalar(loss_key, loss_val, step=step) for loss_key, loss_val in loss.items()]
if (step%20==0 and step > 0):
end = time.time()
time_taken = end-start
start = time.time()
output_log = "step: %d, train_time_taken: %.4f, lr: %.6f" % (step, time_taken, curr_learning_rate)
for loss_key, loss_val in loss.items():
output_log += ', {}: {:.4f}'.format(loss_key, loss_val)
print(output_log)
if (step%1000==0 and step > 0):
tf.keras.models.save_model(rnn, model_save_path, include_optimizer=True, save_format='tf')
step += 1
