probs = network.outputs
sampling_prob = tf.nn.softmax(probs)
t_actions = tf.placeholder(tf.int32, shape=[None])
t_discount_rewards = tf.placeholder(tf.float32, shape=[None])
loss = tl.rein.cross_entropy_reward_loss(probs, t_actions, t_discount_rewards)
train_op = tf.train.RMSPropOptimizer(learning_rate, decay_rate).minimize(loss)
with tf.Session() as sess:
tl.layers.initialize_global_variables(sess)
# if resume:
# load_params = tl.files.load_npz(name=model_file_name+'.npz')
# tl.files.assign_params(sess, load_params, network)
tl.files.load_and_assign_npz(sess, model_file_name + '.npz', network)
network.print_params()
network.print_layers()
start_time = time.time()
game_number = 0
while True:
if render: env.render()
cur_x = prepro(observation)
x = cur_x - prev_x if prev_x is not None else np.zeros(D)
x = x.reshape(1, D)
prev_x = cur_x
prob = sess.run(sampling_prob, feed_dict={t_states: x})
# action. 1: STOP 2: UP 3: DOWN
# action = np.random.choice([1,2,3], p=prob.flatten())
import tensorflow as tf
from tensorlayer.layers import SubpixelConv1d, SubpixelConv2d, InputLayer, Conv1d, Conv2d
## 1D
t_signal = tf.placeholder('float32', [10, 100, 4], name='x')
n = InputLayer(t_signal, name='in')
n = Conv1d(n, 32, 3, 1, padding='SAME', name='conv1d')
n = SubpixelConv1d(n, scale=2, name='subpixel')
print(n.outputs.shape)
# ... (10, 200, 2)
n.print_layers()
n.print_params(False)
shape = n.outputs.get_shape().as_list()
if shape != [10, 200, 16]:
raise Exception("shape dont match")
if len(n.all_layers) != 2:
raise Exception("layers dont match")
if len(n.all_params) != 2:
raise Exception("params dont match")
if n.count_params() != 416:
raise Exception("params dont match")
## 2D
x = tf.placeholder('float32', [10, 100, 100, 3], name='x')
n = InputLayer(x, name='in')
n = Conv2d(n, 32, (3, 2), (1, 1), padding='SAME', name='conv2d')
n = SubpixelConv2d(n, scale=2, name='subpixel2d')
