network = DenseLayer(network, n_units=3, name='output')
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
from tensorlayer.layers import InputLayer, TimeDistributedLayer, DenseLayer
sess = tf.InteractiveSession()
batch_size = 32
timestep = 20
input_dim = 100
## no reuse
x = tf.placeholder(dtype=tf.float32, shape=[batch_size, timestep, input_dim], name="encode_seqs")
net = InputLayer(x, name='input')
net = TimeDistributedLayer(net, layer_class=DenseLayer, args={'n_units': 50, 'name': 'dense'}, name='time_dense')
if net.outputs.get_shape().as_list() != [32, 20, 50]:
raise Exception("shape do not match")
# ... (32, 20, 50)
net.print_params(False)
if net.count_params() != 5050:
raise Exception("params do not match")
## reuse
def model(x, is_train=True, reuse=False):
with tf.variable_scope("model", reuse=reuse):
net = InputLayer(x, name='input')
net = TimeDistributedLayer(net, layer_class=DenseLayer, args={'n_units': 50, 'name': 'dense'}, name='time_dense')
return net
net_train = model(x, is_train=True, reuse=False)
net_test = model(x, is_train=False, reuse=True)
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')
from tensorlayer.layers import InputLayer, TimeDistributedLayer, DenseLayer
sess = tf.InteractiveSession()
batch_size = 32
timestep = 20
input_dim = 100
## no reuse
x = tf.placeholder(dtype=tf.float32, shape=[batch_size, timestep, input_dim], name="encode_seqs")
net = InputLayer(x, name='input')
net = TimeDistributedLayer(net, layer_class=DenseLayer, args={'n_units': 50, 'name': 'dense'}, name='time_dense')
if net.outputs.get_shape().as_list() != [32, 20, 50]:
raise Exception("shape dont match")
# ... (32, 20, 50)
net.print_params(False)
if net.count_params() != 5050:
raise Exception("params dont match")
## reuse
def model(x, is_train=True, reuse=False):
with tf.variable_scope("model", reuse=reuse):
net = InputLayer(x, name='input')
net = TimeDistributedLayer(net, layer_class=DenseLayer, args={'n_units': 50, 'name': 'dense'}, name='time_dense')
return net
net_train = model(x, is_train=True, reuse=False)
net_test = model(x, is_train=False, reuse=True)
