def __init__(self,
clean_encoder_layer,
corrupted_encoder_layer,
previous_decoder_layer=None,
is_training_phase=True):
with tf.name_scope("decoder_layer") as scope:
is_first_decoder_layer = previous_decoder_layer is None
if is_first_decoder_layer:
pre_1st_normalization = corrupted_encoder_layer.post_activation
else:
input_size = _layer_size(previous_decoder_layer.post_denoising)
output_size = _layer_size(clean_encoder_layer.post_activation)
weights = _weight_variable([input_size, output_size], name='V')
pre_1st_normalization = tf.matmul(
previous_decoder_layer.post_denoising, weights)
pre_denoising, _, _ = batch_norm(
pre_1st_normalization, is_training_phase=is_training_phase)
post_denoising = self._denoise(
corrupted_encoder_layer.pre_activation, pre_denoising)
post_2nd_normalization = \
(post_denoising - clean_encoder_layer.batch_mean) / clean_encoder_layer.batch_std
self.post_denoising = post_denoising
self.post_2nd_normalization = post_2nd_normalization
def __init__(self, inputs, output_size, non_linearity,
noise_level, is_training_phase, reuse_variables = None):
with tf.name_scope("encoder_layer") as scope:
self._create_or_reuse_variables(reuse_variables, _layer_size(inputs), output_size)
self.pre_normalization = tf.matmul(inputs, self.weights)
pre_noise, self.batch_mean, self.batch_std = batch_norm(
self.pre_normalization, is_training_phase = is_training_phase)
self.pre_activation = self._add_noise(pre_noise, noise_level)
beta_gamma = self.gamma * (self.pre_activation + self.beta)
self.post_activation = non_linearity(beta_gamma)
def __init__(self,
inputs,
output_size,
non_linearity,
noise_level,
is_training_phase,
reuse_variables=None):
with tf.name_scope("encoder_layer") as scope:
self._create_or_reuse_variables(reuse_variables,
_layer_size(inputs), output_size)
self.pre_normalization = tf.matmul(inputs, self.weights)
pre_noise, self.batch_mean, self.batch_std = batch_norm(
self.pre_normalization, is_training_phase=is_training_phase)
self.pre_activation = self._add_noise(pre_noise, noise_level)
beta_gamma = self.gamma * (self.pre_activation + self.beta)
self.post_activation = non_linearity(beta_gamma)
def __init__(self,
clean_encoder_layer, corrupted_encoder_layer,
previous_decoder_layer = None, is_training_phase = True):
with tf.name_scope("decoder_layer") as scope:
is_first_decoder_layer = previous_decoder_layer is None
if is_first_decoder_layer:
pre_1st_normalization = corrupted_encoder_layer.post_activation
else:
input_size = _layer_size(previous_decoder_layer.post_denoising)
output_size = _layer_size(clean_encoder_layer.post_activation)
weights = _weight_variable([input_size, output_size], name = 'V')
pre_1st_normalization = tf.matmul(
previous_decoder_layer.post_denoising, weights)
pre_denoising, _, _ = batch_norm(pre_1st_normalization, is_training_phase = is_training_phase)
post_denoising = self._denoise(
corrupted_encoder_layer.pre_activation, pre_denoising)
post_2nd_normalization = \
(post_denoising - clean_encoder_layer.batch_mean) / clean_encoder_layer.batch_std
self.post_denoising = post_denoising
self.post_2nd_normalization = post_2nd_normalization
def __init__(self, num_states, num_actions):
self.sess = tf.InteractiveSession()
# Critic Q Network:
self.critic_state_in = tf.placeholder("float", [None, num_states])
self.critic_action_in = tf.placeholder("float", [None, num_actions])
self.W1_c = tf.Variable(tf.random_uniform([num_states, N_HIDDEN_1],
-1 / math.sqrt(num_states),
1 / math.sqrt(num_states)),
name="W1_c")
self.B1_c = tf.Variable(tf.random_uniform([N_HIDDEN_1],
-1 / math.sqrt(num_states),
1 / math.sqrt(num_states)),
name="B1_c")
self.W2_c = tf.Variable(tf.random_uniform(
[N_HIDDEN_1, N_HIDDEN_2], -1 / math.sqrt(N_HIDDEN_1 + num_actions),
1 / math.sqrt(N_HIDDEN_1 + num_actions)),
name="W2_c")
self.B2_c = tf.Variable(tf.random_uniform(
[N_HIDDEN_2], -1 / math.sqrt(N_HIDDEN_1 + num_actions),
1 / math.sqrt(N_HIDDEN_1 + num_actions)),
name="B2_c")
self.W2_action_c = tf.Variable(
tf.random_uniform([num_actions, N_HIDDEN_2],
-1 / math.sqrt(N_HIDDEN_1 + num_actions),
1 / math.sqrt(N_HIDDEN_1 + num_actions)),
name="W2_action_c")
self.W3_c = tf.Variable(tf.random_uniform([N_HIDDEN_2, 1], -0.003,
0.003),
name="W3_c")
self.B3_c = tf.Variable(tf.random_uniform([1], -0.003, 0.003),
name="B3_c")
self.is_training = tf.placeholder(tf.bool, [])
self.H1_t = tf.matmul(self.critic_state_in, self.W1_c)
self.H1_c_bn = batch_norm(self.H1_t, N_HIDDEN_1, self.is_training,
self.sess)
self.H1_c = tf.nn.relu(self.H1_c_bn.bnorm) + self.B1_c
self.H2_t = tf.matmul(self.H1_c, self.W2_c) + tf.matmul(
self.critic_action_in, self.W2_action_c)
self.H2_c_bn = batch_norm(self.H2_t, N_HIDDEN_2, self.is_training,
self.sess)
self.H2_c = tf.nn.relu(self.H2_c_bn.bnorm) + self.B2_c
self.critic_q_model = tf.matmul(self.H2_c, self.W3_c) + self.B3_c
# Target Critic Q Network:
self.t_critic_state_in = tf.placeholder("float", [None, num_states])
self.t_critic_action_in = tf.placeholder("float", [None, num_actions])
self.t_W1_c = tf.Variable(tf.random_uniform([num_states, N_HIDDEN_1],
-1 / math.sqrt(num_states),
1 / math.sqrt(num_states)),
name="t_W1_c")
self.t_B1_c = tf.Variable(tf.random_uniform([N_HIDDEN_1],
-1 / math.sqrt(num_states),
1 / math.sqrt(num_states)),
name="t_B1_c")
self.t_W2_c = tf.Variable(tf.random_uniform(
[N_HIDDEN_1, N_HIDDEN_2], -1 / math.sqrt(N_HIDDEN_1 + num_actions),
1 / math.sqrt(N_HIDDEN_1 + num_actions)),
name="t_W2_c")
self.t_W2_action_c = tf.Variable(
tf.random_uniform([num_actions, N_HIDDEN_2],
-1 / math.sqrt(N_HIDDEN_1 + num_actions),
1 / math.sqrt(N_HIDDEN_1 + num_actions)),
name="t_W2_action_c")
self.t_B2_c = tf.Variable(tf.random_uniform(
[N_HIDDEN_2], -1 / math.sqrt(N_HIDDEN_1 + num_actions),
1 / math.sqrt(N_HIDDEN_1 + num_actions)),
name="t_B2_c")
self.t_W3_c = tf.Variable(tf.random_uniform([N_HIDDEN_2, 1], -0.003,
0.003),
name="t_W3_c")
self.t_B3_c = tf.Variable(tf.random_uniform([1], -0.003, 0.003),
name="t_B3_c")
self.t_H1_t = tf.matmul(self.t_critic_state_in, self.t_W1_c)
self.t_H1_c_bn = batch_norm(self.t_H1_t, N_HIDDEN_1, self.is_training,
self.sess, self.H1_c_bn)
self.t_H1_c = tf.nn.relu(self.t_H1_c_bn.bnorm) + self.t_B1_c
self.t_H2_t = tf.matmul(self.t_H1_c, self.t_W2_c) + tf.matmul(
self.t_critic_action_in, self.t_W2_action_c)
self.t_H2_c_bn = batch_norm(self.t_H2_t, N_HIDDEN_2, self.is_training,
self.sess, self.H2_c_bn)
self.t_H2_c = tf.nn.relu(self.t_H2_c_bn.bnorm) + self.t_B2_c
self.t_critic_q_model = tf.matmul(self.t_H2_c,
self.t_W3_c) + self.t_B3_c
self.t_critic_q_model = tf.matmul(self.t_H2_c,
self.t_W3_c) + self.t_B3_c
self.q_value_in = tf.placeholder("float", [None, 1]) # supervisor
# self.l2_regularizer_loss = tf.nn.l2_loss(self.W1_c)+tf.nn.l2_loss(self.W2_c)+ tf.nn.l2_loss(self.W2_action_c) + tf.nn.l2_loss(self.W3_c)+tf.nn.l2_loss(self.B1_c)+tf.nn.l2_loss(self.B2_c)+tf.nn.l2_loss(self.B3_c)
# self.l2_regularizer_loss = 0.01 * tf.reduce_sum(tf.pow(self.W2_c, 2)) + \
# 0.01 * tf.reduce_sum(tf.pow(self.W1_c, 2)) + \
# 0.01 * tf.reduce_sum(tf.pow(self.W3_c, 2)) + \
# 0.01 * tf.reduce_sum(tf.pow(self.B1_c, 2)) + \
# 0.01 * tf.reduce_sum(tf.pow(self.B2_c, 2)) + \
# 0.01 * tf.reduce_sum(tf.pow(self.B3_c, 2)) + \
# 0.01 * tf.reduce_sum(tf.pow(self.W2_action_c, 2))
self.cost = (tf.reduce_mean(pow(self.critic_q_model - self.q_value_in, 2))) \
# + self.l2_regularizer_loss
self.optimizer = tf.train.GradientDescentOptimizer(
learning_rate=LEARNING_RATE).minimize(self.cost)
self.act_grad_v = tf.gradients(self.critic_q_model,
self.critic_action_in)
self.action_gradients = [self.act_grad_v[0]]
# / tf.to_float(tf.shape(self.act_grad_v[0])[0])]
#
self.check_fl = self.action_gradients
self.sess.run(tf.initialize_all_variables())
# self.saver = tf.train.Saver()
# self.saver.save(self.sess, 'DDPG_MIMO', global_step=1000)
# To initialize critic and target with the same values:
self.sess.run([
self.t_W1_c.assign(self.W1_c),
self.t_B1_c.assign(self.B1_c),
self.t_W2_c.assign(self.W2_c),
self.t_W2_action_c.assign(self.W2_action_c),
self.t_B2_c.assign(self.B2_c),
self.t_W3_c.assign(self.W3_c),
self.t_B3_c.assign(self.B3_c)
])
self.update_target_critic_op = [
self.t_W1_c.assign(TAU * self.W1_c + (1 - TAU) * self.t_W1_c),
self.t_B1_c.assign(TAU * self.B1_c + (1 - TAU) * self.t_B1_c),
self.t_W2_c.assign(TAU * self.W2_c + (1 - TAU) * self.t_W2_c),
self.t_W2_action_c.assign(TAU * self.W2_action_c +
(1 - TAU) * self.t_W2_action_c),
self.t_B2_c.assign(TAU * self.B2_c + (1 - TAU) * self.t_B2_c),
self.t_W3_c.assign(TAU * self.W3_c + (1 - TAU) * self.t_W3_c),
self.t_B3_c.assign(TAU * self.B3_c + (1 - TAU) * self.t_B3_c),
self.t_H1_c_bn.updateTarget, self.t_H2_c_bn.updateTarget
]
L3 = tf.nn.pool(L3,
pooling_type='MAX',
window_shape=[2],
strides=[2],
padding='SAME')
print(L3.shape)
'''
(?, 15, 32)
'''
L3_flat = tf.reshape(L3, [-1, 15 * 32])
W4 = tf.get_variable("W4",
shape=[15 * 32, 500],
initializer=tf.contrib.layers.xavier_initializer())
b4 = tf.Variable(tf.random_normal([500]))
Bn4 = batch_norm(tf.matmul(L3_flat, W4) + b4)
L4 = tf.nn.relu(Bn4)
L4 = tf.nn.dropout(L4, keep_prob=keep_prob)
W5 = tf.get_variable("W5",
shape=[500, nb_transmitter],
initializer=tf.contrib.layers.xavier_initializer())
b5 = tf.Variable(tf.random_normal([nb_transmitter]))
hypothesis = tf.matmul(L4, W5) + b5
# logits = tf.matmul(L2, W3) + b3
# hypothesis = tf.nn.softmax(logits)
cost = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits_v2(logits=hypothesis,
labels=tf.stop_gradient(
def __init__(self, num_states, num_actions):
self.sess = tf.InteractiveSession()
# actor network model parameters:
self.actor_state_in = tf.placeholder("float", [None, num_states])
self.W1_a = tf.Variable(tf.random_uniform([num_states, N_HIDDEN_1],
-1 / math.sqrt(num_states),
1 / math.sqrt(num_states)),
name="W1_a")
self.B1_a = tf.Variable(tf.random_uniform([N_HIDDEN_1],
-1 / math.sqrt(num_states),
1 / math.sqrt(num_states)),
name="B1_a")
self.W2_a = tf.Variable(tf.random_uniform([N_HIDDEN_1, N_HIDDEN_2],
-1 / math.sqrt(N_HIDDEN_1),
1 / math.sqrt(N_HIDDEN_1)),
name="W2_a")
self.B2_a = tf.Variable(tf.random_uniform([N_HIDDEN_2],
-1 / math.sqrt(N_HIDDEN_1),
1 / math.sqrt(N_HIDDEN_1)),
name="B2_a")
self.W3_a = tf.Variable(tf.random_uniform([N_HIDDEN_2, num_actions],
-0.003, 0.003),
name="W3_a")
self.B3_a = tf.Variable(tf.random_uniform([num_actions], -0.003,
0.003),
name="B3_a")
self.is_training = tf.placeholder(tf.bool, [])
self.H1_t = tf.matmul(self.actor_state_in, self.W1_a)
self.H1_a_bn = batch_norm(self.H1_t, N_HIDDEN_1, self.is_training,
self.sess)
self.H1_a = tf.nn.relu(self.H1_a_bn.bnorm) + self.B1_a
self.H2_t = tf.matmul(self.H1_a, self.W2_a)
self.H2_a_bn = batch_norm(self.H2_t, N_HIDDEN_2, self.is_training,
self.sess)
self.H2_a = tf.nn.relu(self.H2_a_bn.bnorm) + self.B2_a
self.actor_model = tf.matmul(self.H2_a, self.W3_a) + self.B3_a
# target actor network model parameters:
self.t_actor_state_in = tf.placeholder("float", [None, num_states])
self.t_W1_a = tf.Variable(tf.random_uniform([num_states, N_HIDDEN_1],
-1 / math.sqrt(num_states),
1 / math.sqrt(num_states)),
name="t_W1_a")
self.t_B1_a = tf.Variable(tf.random_uniform([N_HIDDEN_1],
-1 / math.sqrt(num_states),
1 / math.sqrt(num_states)),
name="t_B1_a")
self.t_W2_a = tf.Variable(tf.random_uniform([N_HIDDEN_1, N_HIDDEN_2],
-1 / math.sqrt(N_HIDDEN_1),
1 / math.sqrt(N_HIDDEN_1)),
name="t_W2_a")
self.t_B2_a = tf.Variable(tf.random_uniform([N_HIDDEN_2],
-1 / math.sqrt(N_HIDDEN_1),
1 / math.sqrt(N_HIDDEN_1)),
name="t_B2_a")
self.t_W3_a = tf.Variable(tf.random_uniform([N_HIDDEN_2, num_actions],
-0.003, 0.003),
name="t_W3_a")
self.t_B3_a = tf.Variable(tf.random_uniform([num_actions], -0.003,
0.003),
name="t_B3_a")
self.t_is_training = tf.placeholder(tf.bool, [])
self.t_H1_t = tf.matmul(self.t_actor_state_in, self.t_W1_a)
self.t_H1_a_bn = batch_norm(self.t_H1_t, N_HIDDEN_1,
self.t_is_training, self.sess,
self.H1_a_bn)
self.t_H1_a = tf.nn.relu(self.t_H1_a_bn.bnorm) + self.t_B1_a
self.t_H2_t = tf.matmul(self.t_H1_a, self.t_W2_a)
self.t_H2_a_bn = batch_norm(self.t_H2_t, N_HIDDEN_2,
self.t_is_training, self.sess,
self.H2_a_bn)
self.t_H2_a = tf.nn.relu(self.t_H2_a_bn.bnorm) + self.t_B2_a
self.t_actor_model = tf.matmul(self.t_H2_a, self.t_W3_a) + self.t_B3_a
# cost of actor network:
self.q_gradient_input = tf.placeholder("float", [None, num_actions])
self.actor_parameters = [
self.W1_a, self.B1_a, self.W2_a, self.B2_a, self.W3_a, self.B3_a,
self.H1_a_bn.scale, self.H1_a_bn.beta, self.H2_a_bn.scale,
self.H2_a_bn.beta
]
self.parameters_gradients = tf.gradients(
self.actor_model, self.actor_parameters,
-self.q_gradient_input / BATCH_SIZE)
self.optimizer = tf.train.GradientDescentOptimizer(
learning_rate=LEARNING_RATE).apply_gradients(
zip(self.parameters_gradients, self.actor_parameters))
# initialize all tensor variable parameters:
self.sess.run(tf.initialize_all_variables())
# self.saver = tf.train.Saver()
# self.saver.save(self.sess, 'DDPG_MIMO', global_step=1000)
self.sess.run([
self.t_W1_a.assign(self.W1_a),
self.t_B1_a.assign(self.B1_a),
self.t_W2_a.assign(self.W2_a),
self.t_B2_a.assign(self.B2_a),
self.t_W3_a.assign(self.W3_a),
self.t_B3_a.assign(self.B3_a)
])
self.update_target_actor_op = [
self.t_W1_a.assign(TAU * self.W1_a + (1 - TAU) * self.t_W1_a),
self.t_B1_a.assign(TAU * self.B1_a + (1 - TAU) * self.t_B1_a),
self.t_W2_a.assign(TAU * self.W2_a + (1 - TAU) * self.t_W2_a),
self.t_B2_a.assign(TAU * self.B2_a + (1 - TAU) * self.t_B2_a),
self.t_W3_a.assign(TAU * self.W3_a + (1 - TAU) * self.t_W3_a),
self.t_B3_a.assign(TAU * self.B3_a + (1 - TAU) * self.t_B3_a),
self.t_H1_a_bn.updateTarget,
self.t_H2_a_bn.updateTarget,
]