def create_actor_network(self):
with tf.variable_scope('actor'):
inputs = tflearn.input_data(shape=[None, self.s_dim[0], self.s_dim[1]])
split_0 = tflearn.fully_connected(inputs[:, 0:1, -1], 64, activation='relu')
split_1 = tflearn.fully_connected(inputs[:, 1:2, -1], 64, activation='relu')
split_2 = tflearn.fully_connected(inputs[:, 4:5, -1], 64, activation='relu')
reshape_0 = tflearn.reshape(inputs[:, 2:4, :], [-1, 2, self.s_dim[1], 1])
split_3 = tflearn.conv_2d(reshape_0, 128, 3, activation='relu')
split_4 = tflearn.conv_1d(inputs[:, 5:6, :], 128, 4, activation='relu')
split_5 = tflearn.conv_1d(inputs[:, 6:7, :], 128, 4, activation='relu')
flatten_0 = tflearn.flatten(split_3)
flatten_1 = tflearn.flatten(split_4)
flatten_2 = tflearn.flatten(split_5)
merge_net = tflearn.merge([split_0, split_1, split_2, flatten_0, flatten_1, flatten_2], 'concat')
dense_net_0 = tflearn.fully_connected(merge_net, 128, activation='relu')
# for multiple video, mask out the invalid actions
linear_out = tflearn.fully_connected(dense_net_0, self.a_dim, activation='linear')
linear_out = tf.transpose(linear_out) # [None, a_dim] -> [a_dim, None]
mask_out = tf.boolean_mask(linear_out, self.mask) # [a_dim, None] -> [masked, None]
mask_out = tf.transpose(mask_out) # [masked, None] -> [None, masked]
softmax_out = tf.nn.softmax(mask_out)
return inputs, softmax_out
def create_actor_network(self):
with tf.variable_scope('actor'):
inputs = tflearn.input_data(
shape=[None, self.s_dim[0], self.s_dim[1]])
split_0 = tflearn.fully_connected(inputs[:, 0:1, -1],
128,
activation='relu')
split_1 = tflearn.conv_1d(inputs[:, 1:2, :],
128,
4,
activation='relu')
split_2 = tflearn.conv_1d(inputs[:, 2:3, :NUM_OF_TRACKS],
128,
4,
activation='relu')
split_3 = tflearn.fully_connected(inputs[:, 3:4, -1],
128,
activation='relu')
split_1_flat = tflearn.flatten(split_1)
split_2_flat = tflearn.flatten(split_2)
merge_net = tflearn.merge(
[split_0, split_1_flat, split_2_flat, split_3], 'concat')
dense_net_0 = tflearn.fully_connected(merge_net,
128,
activation='relu')
out = tflearn.fully_connected(dense_net_0,
self.a_dim,
activation='softmax')
return inputs, out
def create_actor_network(self,reuse = tf.AUTO_REUSE):
with tf.variable_scope('actor',reuse = tf.AUTO_REUSE):
# tf.Graph()
inputs = tflearn.input_data(shape=[None, self.s_dim[0], self.s_dim[1],self.s_dim[2]], name = "actor_input") # input layer
# input_loss = tflearn.layers.normalization.batch_normalization(inputs[:, 0:1, :, :])
# input_dealy_interval = tflearn.layers.normalization.batch_normalization(inputs[:, 1:2, :, :])
# print input_loss.shape
# print input_dealy_interval.shape
# split_1 = tflearn.conv_2d(inputs[:, 0:1, :, :], 64, 3, activation='LeakyReLU',restore = False)
# split_2 = tflearn.conv_2d(inputs[:, 1:2, :, :], 64, 3, activation='LeakyReLU')
# # split_2 = tflearn.fully_connected(inputs[:, 1:2, -1], 128, activation='relu', reuse=tf.AUTO_REUSE, scope = 'fc2')
split_1_flat = tflearn.flatten(inputs[:, 0:1, :, :])
split_2_flat = tflearn.flatten(inputs[:, 1:2, :, :])
# merge_net = tflearn.merge([split_1_flat, split_2_flat], 'concat')
dense_net_0 = tflearn.fully_connected(split_1_flat, 64, activation='LeakyReLU')
dense_net_1 = tflearn.fully_connected(split_2_flat, 64, activation='LeakyReLU')
merge_net = tflearn.merge([dense_net_0, dense_net_1], 'concat')
print('aaaaaaaaaaaaaaaaaaaaaaaaaaa')
print(dense_net_0)
out = tflearn.fully_connected(merge_net, self.a_dim, activation='softmax', name = "actor_output") # output layer
print('type out',type(out),out.name)
return inputs, out
def create_critic_network(self):
with tf.variable_scope('critic'):
inputs = tflearn.input_data(shape=[None, self.s_dim[0], self.s_dim[1],self.s_dim[2]], name = 'critic_input')
# split_1 = tflearn.conv_2d(inputs[:, 0:1,:, :], 128, 4, activation='relu')
# split_2 = tflearn.conv_2d(inputs[:, 1:2,:, :], 128, 4, activation='relu')
# split_1_flat_1 = tflearn.flatten(split_1)
# split_1_flat_2 = tflearn.flatten(split_2)
# merge_net = tflearn.merge([split_1_flat_1, split_1_flat_2], 'concat')
# dense_net_0 = tflearn.fully_connected(merge_net, 128, activation='relu')
split_1_flat = tflearn.flatten(inputs[:, 0:1, :, :])
split_2_flat = tflearn.flatten(inputs[:, 1:2, :, :])
# merge_net = tflearn.merge([split_1_flat, split_2_flat], 'concat')
dense_net_0 = tflearn.fully_connected(split_1_flat, 64, activation='LeakyReLU')
dense_net_1 = tflearn.fully_connected(split_2_flat, 64, activation='LeakyReLU')
merge_net = tflearn.merge([dense_net_0, dense_net_1], 'concat')
out = tflearn.fully_connected(merge_net, 1, activation='linear', name = 'critic_output')
return inputs, out
def create_critic_network(self):
with tf.variable_scope('critic'):
inputs = tflearn.input_data(shape=[None, self.s_dim[0], self.s_dim[1],self.s_dim[2]])
print 'self.s_dim[0]',self.s_dim[0]
# split_0 = tflearn.fully_connected(inputs[:, 0:1, -1], 128, activation='relu', reuse=tf.AUTO_REUSE, scope = 'critic_fc1')
print 'inputs[:, 0:1, :].shape:', inputs[:, 0:1, :].shape
print 'inputs[:, 1:2, :].shape:', inputs[:, 1:2, :].shape
x_reshape1 = tflearn.reshape(inputs[:, 0:1, :], [-1, self.s_dim[1],self.s_dim[2],1])
x_reshape2 = tflearn.reshape(inputs[:, 1:2, :], [-1, self.s_dim[1],self.s_dim[2],1])
print 'x_reshape1.shape:', x_reshape1.shape
print 'x_reshape2.shape:', x_reshape2.shape
split_1 = tflearn.conv_2d(x_reshape1, 128, 4, activation='relu', scope = 'critic_conv1_1')
print 'split_1.shape:', split_1.shape
# split_2 = tflearn.fully_connected(inputs[:, 1:2, -1], 128, activation='relu', reuse=tf.AUTO_REUSE, scope = 'critic_fc2')
split_2 = tflearn.conv_2d(x_reshape2, 128, 4, activation='relu', scope = 'critic_conv1_2')
print 'split_2.shape:', split_2.shape
split_1_flat_1 = tflearn.flatten(split_1)
split_1_flat_2 = tflearn.flatten(split_2)
merge_net = tflearn.merge([split_1_flat_1, split_1_flat_2], 'concat')
dense_net_0 = tflearn.fully_connected(merge_net, 128, activation='relu', scope = 'critic_fc3')
out = tflearn.fully_connected(dense_net_0, 1, activation='linear', scope = 'critic_fc4')
return inputs, out
def CreateNetwork(self, inputs):
with tf.variable_scope('actor'):
split_0 = tflearn.fully_connected(
inputs[:, 0:1, -1], FEATURE_NUM, activation='relu')
split_1 = tflearn.fully_connected(
inputs[:, 1:2, -1], FEATURE_NUM, activation='relu')
split_2 = tflearn.conv_1d(
inputs[:, 2:3, :], FEATURE_NUM, 4, activation='relu')
split_3 = tflearn.conv_1d(
inputs[:, 3:4, :], FEATURE_NUM, 4, activation='relu')
split_4 = tflearn.conv_1d(
inputs[:, 4:5, :self.a_dim], FEATURE_NUM, 4, activation='relu')
split_5 = tflearn.fully_connected(
inputs[:, 5:6, -1], FEATURE_NUM, activation='relu')
split_2_flat = tflearn.flatten(split_2)
split_3_flat = tflearn.flatten(split_3)
split_4_flat = tflearn.flatten(split_4)
merge_net = tflearn.merge(
[split_0, split_1, split_2_flat, split_3_flat, split_4_flat, split_5], 'concat')
pi_net = tflearn.fully_connected(
merge_net, FEATURE_NUM, activation='relu')
pi = tflearn.fully_connected(pi_net, self.a_dim, activation='softmax')
val_net = tflearn.fully_connected(
merge_net, FEATURE_NUM, activation='relu')
val = tflearn.fully_connected(val_net, 1, activation='tanh')
return pi, val
def stock_predictor(inputs, feature_number, predictor_type, use_batch_norm, activation_function, weight_decay):
window_length = inputs.get_shape()[2]
assert predictor_type in ['cnn', 'lstm'], 'type must be either cnn or lstm'
if predictor_type == 'cnn':
net = tflearn.conv_2d(inputs, 32, (1, 3), padding='valid', weights_init = 'xavier',\
regularizer='L2', weight_decay = weight_decay)
if use_batch_norm:
net = tflearn.layers.normalization.batch_normalization(net)
# net = tflearn.activations.relu(net)
net = activation_net(net, activation_function)
net = tflearn.conv_2d(net, 32, (1, window_length - 2), padding='valid', weights_init = 'xavier',\
regularizer='L2', weight_decay = weight_decay)
if use_batch_norm:
net = tflearn.layers.normalization.batch_normalization(net)
# net = tflearn.activations.relu(net)
net = activation_net(net, activation_function)
#################################################
net = tflearn.conv_2d(net, 1, (1, 1), padding='valid', weights_init = 'xavier',\
regularizer='L2', weight_decay = weight_decay)
if use_batch_norm:
net = tflearn.layers.normalization.batch_normalization(net)
# net = tflearn.activations.relu(net)
net = activation_net(net, activation_function)
##################################################
if DEBUG:
print('After conv2d:', net.shape)
net = tflearn.flatten(net)
if DEBUG:
print('Output:', net.shape)
elif predictor_type == 'lstm':
num_stocks = inputs.get_shape()[1]
hidden_dim = 32
net = tflearn.reshape(inputs, new_shape=[-1, window_length, feature_number])
if DEBUG:
print('Reshaped input:', net.shape)
net = tflearn.lstm(net, hidden_dim, activation = activation_function, weights_init = 'xavier')
if DEBUG:
print('After LSTM:', net.shape)
net = tflearn.reshape(net, new_shape=[-1, num_stocks, hidden_dim,1]) ## reshape for conv2d in the next step
if DEBUG:
print('After reshape:', net.shape)
#################################################
net = tflearn.conv_2d(net, 1, (1, hidden_dim), padding='valid', weights_init = 'xavier',\
regularizer='L2', weight_decay = weight_decay)
if use_batch_norm:
net = tflearn.layers.normalization.batch_normalization(net)
# net = tflearn.activations.relu(net)
net = activation_net(net, activation_function)
##################################################
net = tflearn.flatten(net)
if DEBUG:
print('Output:', net.shape)
else:
raise NotImplementedError
return net
def create_actor_network(self):
with tf.variable_scope('actor'):
inputs = tflearn.input_data(
shape=[None, self.s_dim[0], self.s_dim[1]])
split_0 = tflearn.fully_connected(inputs[:, 0:1, -1],
128,
activation='relu')
split_1 = tflearn.fully_connected(inputs[:, 1:2, -1],
128,
activation='relu')
split_2 = tflearn.conv_1d(inputs[:, 2:3, -self.s_len:],
128,
4,
activation='relu')
split_3 = tflearn.conv_1d(inputs[:, 3:4, -self.s_len:],
128,
4,
activation='relu')
split_4 = tflearn.conv_1d(inputs[:, 4:5, :A_DIM],
128,
4,
activation='relu')
split_5 = tflearn.fully_connected(inputs[:, 4:5, -1],
128,
activation='relu')
#add a param
#success ,only in actor network
#test_6 = tflearn.fully_connected(inputs[:, 5:6, -1], 128, activation='relu')
# [buffer1,bitrate1,buffer2,bitrate2,...]
otherAgentData = tflearn.conv_1d(inputs[:, 5:6, :],
128,
2,
strides=2,
activation='relu')
split_2_flat = tflearn.flatten(split_2)
split_3_flat = tflearn.flatten(split_3)
split_4_flat = tflearn.flatten(split_4)
otherAgentData_flat = tflearn.flatten(otherAgentData)
merge_net = tflearn.merge([
split_0, split_1, split_2_flat, split_3_flat, split_4_flat,
split_5, otherAgentData_flat
], 'concat')
dense_net_0 = tflearn.fully_connected(merge_net,
128,
activation='relu')
out = tflearn.fully_connected(dense_net_0,
self.a_dim,
activation='softmax')
return inputs, out
def _build_c(self, s, a, scope, trainable):
with tf.variable_scope(scope):
inputs = tf.reshape(s, [-1, S_INFO, S_LEN])
split_0 = tflearn.fully_connected(inputs[:, 0:1, :],
FEATURE_NUM,
activation='relu',
trainable=trainable)
split_1 = tflearn.fully_connected(inputs[:, 1:2, :],
FEATURE_NUM,
activation='relu',
trainable=trainable)
split_2 = tflearn.conv_1d(inputs[:, 2:3, :],
FEATURE_NUM,
4,
activation='relu',
trainable=trainable)
split_3 = tflearn.conv_1d(inputs[:, 3:4, :],
FEATURE_NUM,
4,
activation='relu',
trainable=trainable)
split_4 = tflearn.conv_1d(inputs[:, 4:5, :A_DIM],
FEATURE_NUM,
4,
activation='relu',
trainable=trainable)
split_5 = tflearn.fully_connected(inputs[:, 5:6, :],
FEATURE_NUM,
activation='relu',
trainable=trainable)
split_6 = tflearn.fully_connected(a,
FEATURE_NUM,
activation='relu',
trainable=trainable)
split_2_flat = tflearn.flatten(split_2)
split_3_flat = tflearn.flatten(split_3)
split_4_flat = tflearn.flatten(split_4)
net = tf.stack([
split_0, split_1, split_2_flat, split_3_flat, split_4_flat,
split_5, split_6
],
axis=1)
net = tflearn.fully_connected(net,
FEATURE_NUM,
activation='relu',
trainable=trainable)
net = tflearn.fully_connected(net,
1,
activation='linear',
trainable=trainable)
return net
def create_actor_network(self):
with tf.variable_scope('actor'):
inputs = tflearn.input_data(
shape=[None, self.s_dim[0], self.s_dim[1]])
split_0 = tflearn.fully_connected(inputs[:, 0:1, -1],
128,
activation='relu')
split_1 = tflearn.fully_connected(inputs[:, 1:2, -1],
128,
activation='relu')
split_2 = tflearn.conv_1d(inputs[:, 2:3, :],
128,
4,
activation='relu')
split_3 = tflearn.conv_1d(inputs[:, 3:4, :],
128,
4,
activation='relu')
split_4 = tflearn.conv_1d(inputs[:, 4:5, :A_DIM],
128,
4,
activation='relu')
split_5 = tflearn.fully_connected(inputs[:, 4:5, -1],
128,
activation='relu')
split_2_flat = tflearn.flatten(split_2)
split_3_flat = tflearn.flatten(split_3)
split_4_flat = tflearn.flatten(split_4)
merge_net = tflearn.merge([
split_0, split_1, split_2_flat, split_3_flat, split_4_flat,
split_5
], 'concat')
dense_net_0 = tflearn.fully_connected(merge_net,
128,
activation='relu')
out = tflearn.fully_connected(dense_net_0,
self.a_dim,
activation='softmax')
# print("inputs2",inputs[:,2:3,:])
# print("split_0",split_0.shape)
# print("split_1",split_1.shape)
# print("split_2",split_2.shape,"split_2_flat",split_2_flat.shape)
# print("split_3",split_3.shape,"split_3_flat",split_3_flat.shape)
# print("split_4",split_4.shape,"split_4_flat",split_4_flat.shape)
# print("split_5",split_5.shape)
# print("merge_net",merge_net.shape)
# exit()
return inputs, out
def create_network(self, s, scope='Actor/eval', trainable=True):
with tf.variable_scope(scope):
inputs = tf.reshape(s, [-1, S_INFO, S_LEN])
split_0 = tflearn.fully_connected(inputs[:, 0:1, :],
FEATURE_NUM,
activation='relu',
trainable=trainable)
split_1 = tflearn.fully_connected(inputs[:, 1:2, :],
FEATURE_NUM,
activation='relu',
trainable=trainable)
split_2 = tflearn.conv_1d(inputs[:, 2:3, :],
FEATURE_NUM,
4,
activation='relu',
trainable=trainable)
split_3 = tflearn.conv_1d(inputs[:, 3:4, :],
FEATURE_NUM,
4,
activation='relu',
trainable=trainable)
split_4 = tflearn.conv_1d(inputs[:, 4:5, :A_DIM],
FEATURE_NUM,
4,
activation='relu',
trainable=trainable)
split_5 = tflearn.fully_connected(inputs[:, 5:6, :],
FEATURE_NUM,
activation='relu',
trainable=trainable)
split_2_flat = tflearn.flatten(split_2)
split_3_flat = tflearn.flatten(split_3)
split_4_flat = tflearn.flatten(split_4)
net = tf.stack([
split_0, split_1, split_2_flat, split_3_flat, split_4_flat,
split_5
],
axis=1)
net = tflearn.fully_connected(net,
FEATURE_NUM,
activation='relu',
trainable=trainable)
merge_net, alphas = self.attention(net, FEATURE_NUM)
dense_net_0 = self.nac(merge_net, FEATURE_NUM, trainable=trainable)
out = self.nac(dense_net_0, 2, trainable=trainable)
out = tf.nn.sigmoid(out, trainable=trainable)
#a = tflearn.fully_connected(
# net, 2, activation='sigmoid', trainable=trainable)
return tf.multiply(out, 60., name='scaled_a'), alphas
def create_network(self):
with tf.variable_scope('actor'):
inputs = tflearn.input_data(shape=[None, self.S_INFO, self.S_LEN])
split_0 = tflearn.fully_connected(inputs[:, 0:1, -1],
FEATURE_NUM,
activation='relu')
split_1 = tflearn.fully_connected(inputs[:, 1:2, -1],
FEATURE_NUM,
activation='relu')
split_2 = tflearn.conv_1d(inputs[:, 2:3, :],
FEATURE_NUM,
4,
activation='relu')
split_3 = tflearn.conv_1d(inputs[:, 3:4, :],
FEATURE_NUM,
4,
activation='relu')
split_4 = tflearn.conv_1d(inputs[:, 4:5, :self.A_DIM],
FEATURE_NUM,
4,
activation='relu')
split_5 = tflearn.conv_1d(inputs[:, 5:6, :self.A_DIM],
FEATURE_NUM,
4,
activation='relu')
split_6 = tflearn.fully_connected(inputs[:, 6:7, -1],
FEATURE_NUM,
activation='relu')
split_2_flat = tflearn.flatten(split_2)
split_3_flat = tflearn.flatten(split_3)
split_4_flat = tflearn.flatten(split_4)
split_5_flat = tflearn.flatten(split_5)
merge_net = tf.stack([
split_0, split_1, split_2_flat, split_3_flat, split_4_flat,
split_5_flat, split_6
],
axis=-1)
# shuffle to fit gru layer
merge_net = tf.transpose(merge_net, [0, 2, 1])
dense_net_0 = tflearn.gru(merge_net,
FEATURE_NUM,
activation='relu')
out = tflearn.fully_connected(dense_net_0,
self.A_DIM,
activation='softmax')
return inputs, out
def _build_a(self, s, scope, trainable):
with tf.variable_scope(scope):
inputs = tf.reshape(s, [-1, S_INFO, S_LEN])
split_0 = tflearn.fully_connected(inputs[:, 0:1, :],
FEATURE_NUM,
activation='relu',
trainable=trainable)
split_1 = tflearn.fully_connected(inputs[:, 1:2, :],
FEATURE_NUM,
activation='relu',
trainable=trainable)
split_2 = tflearn.conv_1d(inputs[:, 2:3, :],
FEATURE_NUM,
4,
activation='relu',
trainable=trainable)
split_3 = tflearn.conv_1d(inputs[:, 3:4, :],
FEATURE_NUM,
4,
activation='relu',
trainable=trainable)
split_4 = tflearn.conv_1d(inputs[:, 4:5, :A_DIM],
FEATURE_NUM,
4,
activation='relu',
trainable=trainable)
split_5 = tflearn.fully_connected(inputs[:, 5:6, :],
FEATURE_NUM,
activation='relu',
trainable=trainable)
split_2_flat = tflearn.flatten(split_2)
split_3_flat = tflearn.flatten(split_3)
split_4_flat = tflearn.flatten(split_4)
net = tf.stack([
split_0, split_1, split_2_flat, split_3_flat, split_4_flat,
split_5
],
axis=1)
net = tflearn.fully_connected(net,
FEATURE_NUM,
activation='relu',
trainable=trainable)
a = tflearn.fully_connected(net,
self.a_dim,
activation='sigmoid',
trainable=trainable)
return tf.multiply(a, self.a_bound, name='scaled_a')
def hybrid_header(x, reuse=False):
size = 3
inputs_shape = x.get_shape().as_list()
with tf.variable_scope('1d-cnn'):
split_array = []
for t in xrange(S_LEN):
tmp_split = tflearn.conv_1d(
x[:, t:t + 1, :], FEATURE_NUM, size, activation='relu')
tmp_split_flat = tflearn.flatten(tmp_split)
tmp_split_flat = tflearn.layers.normalization.batch_normalization(tmp_split_flat)
split_array.append(tmp_split_flat)
merge_net = tflearn.merge(split_array, 'concat')
_count = merge_net.get_shape().as_list()[1]
out_cnn = tf.reshape(
merge_net, [-1, inputs_shape[1], _count / inputs_shape[1]])
with tf.variable_scope('gru'):
net = tflearn.gru(out_cnn, FEATURE_NUM, return_seq=True)
out_gru = tflearn.gru(net, FEATURE_NUM)
out_gru = tf.expand_dims(out_gru, 1)
conv_1d_net = tflearn.conv_1d(out_gru, FEATURE_NUM, size, activation='relu')
conv_1d_net_flattern = tflearn.flatten(conv_1d_net)
# with tf.name_scope('1d-cnn'):
# network_array = []
# for p in xrange(S_INFO - 1):
# branch_array = []
# for i in xrange(2,4):
# sp_branch = tflearn.conv_1d(x[:, :, p:p+1], FEATURE_NUM, i, padding='valid', activation='relu', regularizer="L2")
# branch_array.append(sp_branch)
# branch = tflearn.merge(branch_array, mode='concat', axis=1)
# branch = tf.expand_dims(branch, 2)
# branch = global_max_pool(branch)
# branch = tflearn.dropout(branch, 0.5)
# network_array.append(branch)
# out_cnn = tflearn.merge(network_array, 'concat')
#with tf.name_scope('gru'):
# #net = tflearn.gru(x, FEATURE_NUM, return_seq=True)
# net = tflearn.gru(x, FEATURE_NUM)
# out_gru = tflearn.fully_connected(
# net, FEATURE_NUM, activation='relu')
# out_gru = tflearn.dropout(out_gru, 0.5)
#merge_net = tflearn.merge([out_cnn, out_gru], 'concat')
return conv_1d_net_flattern
def create_actor_network(self):
with tf.variable_scope('actor'):
inputs = tflearn.input_data(
shape=[None, self.s_dim[0], self.s_dim[1]])
split_0 = tflearn.fully_connected(inputs[:, 0:1, -1],
128,
activation='relu')
split_1 = tflearn.fully_connected(inputs[:, 1:2, -1],
128,
activation='relu')
split_2 = tflearn.conv_1d(inputs[:, 2:3, :],
128,
4,
activation='relu')
split_3 = tflearn.conv_1d(inputs[:, 3:4, :],
128,
4,
activation='relu')
split_4 = tflearn.conv_1d(inputs[:, 4:5, :A_DIM],
128,
4,
activation='relu')
split_5 = tflearn.fully_connected(inputs[:, 5:6, -1],
128,
activation='relu')
# split_5 = tflearn.fully_connected(inputs[:, 4:5, -1], 128, activation='relu')
#split_6 = tflearn.fully_connected(inputs[:, 6:7, -1], 128, activation='relu')
#split_7 = tflearn.fully_connected(inputs[:, 7:8, -1], 128, activation='relu')
#split_8 = tflearn.fully_connected(inputs[:, 8:9, -1], 128, activation='relu')
split_2_flat = tflearn.flatten(split_2)
split_3_flat = tflearn.flatten(split_3)
split_4_flat = tflearn.flatten(split_4)
merge_net = tflearn.merge([
split_0, split_1, split_2_flat, split_3_flat, split_4_flat,
split_5
], 'concat')
dense_net_0 = tflearn.fully_connected(merge_net,
128,
activation='relu')
out = tflearn.fully_connected(dense_net_0,
self.a_dim,
activation='softmax')
return inputs, out
def vqn_model(x):
with tf.variable_scope('vqn'):
inputs = tflearn.input_data(placeholder=x)
_split_array = []
for i in range(INPUT_SEQ):
tmp_network = tf.reshape(inputs[:, i:i + 1, :, :, :],
[-1, INPUT_H, INPUT_W, INPUT_D])
if i == 0:
_split_array.append(CNN_Core(tmp_network))
else:
_split_array.append(CNN_Core(tmp_network, True))
merge_net = tflearn.merge(_split_array, 'concat')
merge_net = tflearn.flatten(merge_net)
_count = merge_net.get_shape().as_list()[1]
with tf.variable_scope('full-lstm'):
net = tf.reshape(merge_net, [-1, INPUT_SEQ, _count / INPUT_SEQ])
net = tflearn.gru(net, DENSE_SIZE, return_seq=True)
out_gru = tflearn.gru(net, DENSE_SIZE, dropout=0.8)
gru_result = tflearn.fully_connected(out_gru,
DENSE_SIZE,
activation='relu')
out = tflearn.fully_connected(gru_result,
OUTPUT_DIM,
activation='sigmoid')
return out
def create_actor_network(self):
with tf.variable_scope(self.scope + '-actor'):
inputs = tflearn.input_data(
shape=[None, self.s_dim[0], self.s_dim[1]])
split_array = []
for i in range(self.s_dim[0]):
tmp = tf.reshape(inputs[:, i:i + 1, :], (-1, self.s_dim[1], 1))
split = tflearn.conv_1d(tmp,
FEATURE_NUM // 4,
KERNEL,
activation='relu')
split = tflearn.avg_pool_1d(split, 2)
split = tflearn.conv_1d(tmp,
FEATURE_NUM // 2,
KERNEL,
activation='relu')
split = tflearn.avg_pool_1d(split, 2)
split = tflearn.conv_1d(tmp,
FEATURE_NUM,
KERNEL,
activation='relu')
#split = tflearn.avg_pool_1d(split, 2)
flattern = tflearn.flatten(split)
split_array.append(flattern)
dense_net_0 = tflearn.merge(split_array, 'concat')
dense_net_0 = tflearn.fully_connected(dense_net_0,
FEATURE_NUM,
activation='relu')
out = tflearn.fully_connected(dense_net_0,
self.a_dim,
activation='softmax')
return inputs, out
def CNN_Core(x, reuse=False):
with tf.variable_scope('cnn_core', reuse=reuse):
network = tflearn.conv_2d(x,
KERNEL,
3,
activation='relu',
regularizer="L2",
weight_decay=0.0001)
network = tflearn.max_pool_2d(network, 2)
network = tflearn.conv_2d(network,
KERNEL,
2,
activation='relu',
regularizer="L2",
weight_decay=0.0001)
network = tflearn.max_pool_2d(network, 2)
network = tflearn.conv_2d(network,
KERNEL,
2,
activation='relu',
regularizer="L2",
weight_decay=0.0001)
network = tflearn.max_pool_2d(network, 2)
split_flat = tflearn.flatten(network)
return split_flat
def net_alzheimer_cnn(image_dims):
net = layers.core.input_data(shape=[None, image_dims[0], image_dims[1], image_dims[2], image_dims[3]], dtype=tf.float32)
#3d convolutions layers
net = layers.conv.conv_3d(net, 8, 3, strides=1, activation='relu')
net = layers.conv.max_pool_3d(net, [1,2,2,2,1], strides=[1,2,2,2,1])
net = layers.conv.conv_3d(net, 8, 3, strides=1, activation='relu')
net = layers.conv.max_pool_3d(net, [1,2,2,2,1], strides=[1,2,2,2,1])
net = layers.conv.conv_3d(net, 8, 3, strides=1, activation='relu')
net = layers.conv.max_pool_3d(net, [1,2,2,2,1], strides=[1,2,2,2,1])
#fully connected with 1x1 conv
net = tflearn.flatten(net)
net = layers.core.fully_connected(net, 2000, activation='relu')
net = layers.core.dropout(net, 0.8)
net = layers.core.fully_connected(net, 500, activation='relu')
net = layers.core.dropout(net, 0.8)
#classification layer
net = layers.core.fully_connected(net, 2, activation='softmax')
net = layers.estimator.regression(net, optimizer='adam',
loss='categorical_crossentropy',
learning_rate=0.001)
return net
def build_hybrid_net(x):
inputs = tflearn.input_data(placeholder=x)
with tf.name_scope('1d-cnn'):
network_array = []
for p in xrange(S_INFO):
branch = tflearn.conv_1d(inputs[:, :, p:p + 1],
FEATURE_NUM,
3,
activation='relu',
regularizer="L2")
branch = tflearn.flatten(branch)
network_array.append(branch)
out_cnn = tflearn.merge(network_array, 'concat')
with tf.name_scope('gru'):
net = tflearn.gru(inputs, FEATURE_NUM, return_seq=True)
out_gru = tflearn.gru(net, FEATURE_NUM)
header = tflearn.merge([out_cnn, out_gru], 'concat')
dense_net = tflearn.fully_connected(header,
FEATURE_NUM * 2,
activation='relu',
regularizer="L2")
dense_net = tflearn.fully_connected(dense_net,
FEATURE_NUM * 1,
activation='relu',
regularizer="L2")
out = tflearn.fully_connected(dense_net,
1,
activation='sigmoid',
regularizer="L2")
return out, header
def create_conv_part(net_inputs):
"""Creates an input stream from depth image."""
net = tflearn.conv_2d(incoming=net_inputs,
nb_filter=32,
filter_size=5,
strides=5,
padding='valid',
activation='relu')
net = tflearn.max_pool_2d(incoming=net, kernel_size=2, strides=2)
net = tflearn.conv_2d(incoming=net,
nb_filter=32,
filter_size=2,
strides=2,
padding='valid',
activation='relu')
net = tflearn.max_pool_2d(incoming=net, kernel_size=2, strides=2)
net = tflearn.conv_2d(incoming=net,
nb_filter=64,
filter_size=2,
strides=2,
padding='valid',
activation='relu')
net = tflearn.max_pool_2d(incoming=net, kernel_size=2, strides=2)
net = tflearn.flatten(incoming=net)
return net
def create_actor_network(self):
with tf.variable_scope('actor'):
inputs = tflearn.input_data(
shape=[None, self.s_dim[0], self.s_dim[1]])
split_array = []
for i in xrange(self.s_dim[0] - 1):
split = tflearn.conv_1d(inputs[:, i:i + 1, :],
FEATURE_NUM,
KERNEL,
activation='relu')
flattern = tflearn.flatten(split)
split_array.append(flattern)
dense_net = tflearn.fully_connected(inputs[:, -1:, :],
FEATURE_NUM,
activation='relu')
split_array.append(dense_net)
merge_net = tflearn.merge(split_array, 'concat')
dense_net_0 = tflearn.fully_connected(merge_net,
64,
activation='relu')
# dense_net_0 = tflearn.dropout(dense_net_0, 0.8)
out = tflearn.fully_connected(dense_net_0,
self.a_dim,
activation='softmax')
return inputs, out
def vqn_model(x):
with tf.variable_scope('vqn'):
inputs = tflearn.input_data(placeholder=x)
_split_array = []
_cnn_array = []
for i in range(INPUT_SEQ):
tmp_network = tf.reshape(inputs[:, i:i + 1, :, :, :],
[-1, INPUT_H, INPUT_W, INPUT_D])
if i == 0:
_tmp_split, _tmp_cnn = CNN_Core(tmp_network)
else:
_tmp_split, _tmp_cnn = CNN_Core(tmp_network, True)
_split_array.append(_tmp_split)
_cnn_array.append(_tmp_cnn)
merge_net = tflearn.merge(_split_array, 'concat')
merge_net = tflearn.flatten(merge_net)
_count = merge_net.get_shape().as_list()[1]
with tf.variable_scope('full-lstm'):
net = tf.reshape(merge_net, [-1, INPUT_SEQ, _count / INPUT_SEQ])
net = tflearn.gru(net, HIDDEN_UNIT, return_seq=True)
net = tflearn.gru(net, HIDDEN_UNIT, return_seq=True)
net, alphas = attention(net, HIDDEN_UNIT)
out = tflearn.fully_connected(net,
OUTPUT_DIM,
activation='sigmoid')
return out, tf.stack(_cnn_array, axis=0), alphas
def create_critic_network(self):
with tf.variable_scope('critic'):
inputs = tflearn.input_data(
shape=[None, self.s_dim[0], self.s_dim[1]]) #(None, 7, 91)
'''
split_0 = tflearn.fully_connected(inputs[:, 0:1, -1], 128, activation='relu')
split_1 = tflearn.fully_connected(inputs[:, 1:2, -1], 128, activation='relu')
split_2 = tflearn.conv_1d(inputs[:, 2:3, :], 128, 4, activation='relu')
split_3 = tflearn.conv_1d(inputs[:, 3:4, :], 128, 4, activation='relu')
split_4 = tflearn.conv_1d(inputs[:, 4:5, :A_DIM], 128, 4, activation='relu')
split_5 = tflearn.fully_connected(inputs[:, 4:5, -1], 128, activation='relu')
split_2_flat = tflearn.flatten(split_2)
split_3_flat = tflearn.flatten(split_3)
split_4_flat = tflearn.flatten(split_4)
merge_net = tflearn.merge([split_0, split_1, split_2_flat, split_3_flat, split_4_flat, split_5], 'concat')
'''
split = tflearn.conv_1d(inputs, 128, 1,
activation='relu') #(None, 7, 128)
split_flat = tflearn.flatten(split)
#dense_net_0 = tflearn.fully_connected(split_flat, 1024, activation='relu')
#dense_net_1 = tflearn.fully_connected(dense_net_0, 512, activation='relu')
dense_net_2 = tflearn.fully_connected(split_flat,
256,
activation='relu')
out = tflearn.fully_connected(dense_net_2, 1, activation='linear')
return inputs, out
def build_network(self, num_classes, input_shape, model): network = tflearn.input_data(shape=[None, input_shape[0], input_shape[1], input_shape[2]]) if model == 'DeepFace': conv_1 = tflearn.relu(tflearn.conv_2d(network, 32, 11, strides=1, padding='VALID', name='Conv2d_1')) maxpool_1 = tflearn.max_pool_2d(conv_1, 3, strides=2, padding='VALID', name='MaxPool_1') conv_2 = tflearn.relu(tflearn.conv_2d(maxpool_1, 32, 9, strides=1, padding='VALID', name='Conv2d_2')) local_1 = tflearn.relu(self.local(conv_2, 16, 9, 1, 'Local_1')) local_2 = tflearn.relu(self.local(local_1, 16, 7, 1, 'Local_2')) local_3 = tflearn.relu(self.local(local_2, 16, 5, 1, 'Local_3')) flatterned = tflearn.flatten(local_3) full_1 = tflearn.dropout(tflearn.relu(tflearn.fully_connected(flatterned, 4096, name='Fully_Connected_1')), 0.5) output = tflearn.fully_connected(full_1, num_classes, activation='softmax', name='Output') elif model == 'Song': conv_1 = tflearn.relu(tflearn.conv_2d(network, 64, 5, strides=1, padding='VALID', name='Conv_1')) maxpool_1 = tflearn.max_pool_2d(conv_1, 3, strides=2, padding='VALID', name='MaxPool_1') conv_2 = tflearn.relu(tflearn.conv_2d(maxpool_1, 64 , 5, strides=1, padding='VALID', name='Conv_2')) maxpool_2 = tflearn.max_pool_2d(conv_2, 3, strides=2, padding='VALID', name='MaxPool_2') local_1 = tflearn.dropout(tflearn.relu(self.local(maxpool_2, 32, 3, 1, 'Local_1')), 1) local_2 = tflearn.dropout(tflearn.relu(self.local(local_1, 32, 3, 1, 'Local_2')), 1) flatterned = tflearn.flatten(local_2) output = tflearn.fully_connected(flatterned, num_classes, activation='softmax', name='Output') else: conv_1 = tflearn.relu(tflearn.conv_2d(network, 64, 7, strides=2, bias=True, padding='VALID', name='Conv2d_1')) maxpool_1 = tflearn.batch_normalization(tflearn.max_pool_2d(conv_1, 3, strides=2, padding='VALID', name='MaxPool_1')) conv_2a = tflearn.relu(tflearn.conv_2d(maxpool_1, 96, 1, strides=1, padding='VALID', name='Conv_2a_FX1')) maxpool_2a = tflearn.max_pool_2d(maxpool_1, 3, strides=1, padding='VALID', name='MaxPool_2a_FX1') conv_2b = tflearn.relu(tflearn.conv_2d(conv_2a, 208, 3, strides=1, padding='VALID', name='Conv_2b_FX1')) conv_2c = tflearn.relu(tflearn.conv_2d(maxpool_2a, 64, 1, strides=1, padding='VALID', name='Conv_2c_FX1')) FX1_out = tflearn.merge([conv_2b, conv_2c], mode='concat', axis=3, name='FX1_out') conv_3a = tflearn.relu(tflearn.conv_2d(FX1_out, 96, 1, strides=1, padding='VALID', name='Conv_3a_FX2')) maxpool_3a = tflearn.max_pool_2d(FX1_out, 3, strides=1, padding='VALID', name='MaxPool_3a_FX2') conv_3b = tflearn.relu(tflearn.conv_2d(conv_3a, 208, 3, strides=1, padding='VALID', name='Conv_3b_FX2')) conv_3c = tflearn.relu(tflearn.conv_2d(maxpool_3a, 64, 1, strides=1, padding='VALID', name='Conv_3c_FX2')) FX2_out = tflearn.merge([conv_3b, conv_3c], mode='concat', axis=3, name='FX2_out') net = tflearn.flatten(FX2_out) output = tflearn.fully_connected(net, num_classes, activation='softmax', name='Output') return tflearn.regression(output, optimizer='Adam', loss='categorical_crossentropy', learning_rate=0.000001)
def CNN_Core(self, x, reuse=False):
with tf.variable_scope(self.scope + '-cnn_core', reuse=reuse):
tmp = tflearn.conv_1d(x,
FEATURE_NUM // 4,
KERNEL,
activation='relu')
tmp = tflearn.batch_normalization(tmp)
tmp = tflearn.flatten(tmp)
return tmp
def build_dqn(self):
'''Defines the value function model'''
with tf.variable_scope(self.scope):
self.inputs = tf.placeholder(tf.float32, [None] + list(INPUT_SHAPE))
net = tflearn.flatten(self.inputs)
net = tflearn.fully_connected(net,NUM_HIDDEN_UNITS,activation='relu')
self.qvals = tflearn.fully_connected(net, self.num_actions)
return
def __init__(self, h_size=512, n_actions=4, learning_rate=1e-4):
input_x = tf.placeholder(dtype=tf.float32, shape=[None, 84, 84, 3], name="Input")
conv1 = tflearn.conv_2d(input_x, nb_filter=32, filter_size=8, strides=4,
padding='VALID', weights_init='xavier',
activation='relu', name="Conv1")
conv2 = tflearn.conv_2d(conv1, nb_filter=64, filter_size=4, strides=2,
padding='VALID', weights_init='xavier',
activation='relu', name="Conv2")
conv3 = tflearn.conv_2d(conv2, nb_filter=64, filter_size=3, strides=1,
padding='VALID', weights_init='xavier',
activation='relu', name="Conv3")
conv4 = tflearn.conv_2d(conv3, nb_filter=h_size, filter_size=7, strides=1,
padding='VALID', weights_init='xavier',
activation='relu', name="Conv4")
# We take the output from the final convolutional layer and split it into separate advantage and value streams.
streamAC, streamVC = tf.split(conv4, 2, 3)
streamA = tflearn.flatten(streamAC, name="streamA")
streamV = tflearn.flatten(streamVC, name="streamV")
Advantage = tflearn.fully_connected(streamA, n_units=n_actions, activation='linear',
weights_init='xavier', name="Advantage")
Value = tflearn.fully_connected(streamV, n_units=1, activation='linear',
weights_init='xavier', name="Value")
Qout = tf.add(Value, tf.subtract(Advantage, tf.reduce_mean(Advantage, axis=1, keep_dims=True)), name="Qout")
predict = tf.argmax(Qout, 1, name="Predict")
targetQ = tf.placeholder(shape=[None], dtype=tf.float32)
actions = tf.placeholder(shape=[None], dtype=tf.int32)
actions_onehot = tf.one_hot(actions, n_actions, dtype=tf.float32)
Q = tf.reduce_sum(tf.multiply(Qout, actions_onehot), axis=1)
td_error = tf.square(targetQ - Q)
loss = tf.reduce_mean(td_error)
trainer = tf.train.AdamOptimizer(learning_rate=learning_rate)
update_model = trainer.minimize(loss)
self.input_x = input_x
self.predict = predict
self.loss = loss
self.Qout = Qout
self.targetQ = targetQ
self.actions = actions
self.update_model = update_model
def create_generate_network(self):
with tf.variable_scope(self.scope + '-gan-g', reuse=self.reuse_gan):
inputs = tflearn.input_data(
shape=[None, self.s_dim[0], self.s_dim[1]])
gan_inputs = tflearn.input_data(shape=[None, GAN_CORE])
_input = tflearn.flatten(inputs)
_com = tflearn.merge([_input, gan_inputs], 'concat')
_com = tflearn.flatten(_com)
net = tflearn.fully_connected(_com,
FEATURE_NUM,
activation='leakyrelu')
net = tflearn.batch_normalization(net)
net = tflearn.fully_connected(net,
FEATURE_NUM // 2,
activation='leakyrelu')
net = tflearn.batch_normalization(net)
out = tflearn.fully_connected(net, GAN_CORE, activation='sigmoid')
self.reuse_gan = True
return inputs, gan_inputs, out
def Network(data_input, training = True):
x = tflearn.conv_2d(data_input, 32, 3, strides = 1, activation='prelu', weights_init = 'xavier')
x = tflearn.conv_2d(x, 32, 3, strides = 2, activation='prelu', weights_init = 'xavier')
x = tflearn.conv_2d(x, 64, 3, strides = 1, activation='prelu', weights_init = 'xavier')
x = tflearn.conv_2d(x, 64, 3, strides = 2, activation='prelu', weights_init = 'xavier')
x = tflearn.conv_2d(x, 128, 3, strides = 1, activation='prelu', weights_init = 'xavier')
x = tflearn.conv_2d(x, 128, 3, strides = 2, activation='prelu', weights_init = 'xavier')
x = tflearn.flatten(x)
feat = tflearn.fully_connected(x, 2, weights_init = 'xavier')
return feat
def test_core_layers(self):
X = [[0., 0.], [0., 1.], [1., 0.], [1., 1.]]
Y_nand = [[1.], [1.], [1.], [0.]]
Y_or = [[0.], [1.], [1.], [1.]]
# Graph definition
with tf.Graph().as_default():
# Building a network with 2 optimizers
g = tflearn.input_data(shape=[None, 2])
# Nand operator definition
g_nand = tflearn.fully_connected(g, 32, activation='linear')
g_nand = tflearn.fully_connected(g_nand, 32, activation='linear')
g_nand = tflearn.fully_connected(g_nand, 1, activation='sigmoid')
g_nand = tflearn.regression(g_nand, optimizer='sgd',
learning_rate=2.,
loss='binary_crossentropy')
# Or operator definition
g_or = tflearn.fully_connected(g, 32, activation='linear')
g_or = tflearn.fully_connected(g_or, 32, activation='linear')
g_or = tflearn.fully_connected(g_or, 1, activation='sigmoid')
g_or = tflearn.regression(g_or, optimizer='sgd',
learning_rate=2.,
loss='binary_crossentropy')
# XOR merging Nand and Or operators
g_xor = tflearn.merge([g_nand, g_or], mode='elemwise_mul')
# Training
m = tflearn.DNN(g_xor)
m.fit(X, [Y_nand, Y_or], n_epoch=400, snapshot_epoch=False)
# Testing
self.assertLess(m.predict([[0., 0.]])[0][0], 0.01)
self.assertGreater(m.predict([[0., 1.]])[0][0], 0.9)
self.assertGreater(m.predict([[1., 0.]])[0][0], 0.9)
self.assertLess(m.predict([[1., 1.]])[0][0], 0.01)
# Bulk Tests
with tf.Graph().as_default():
net = tflearn.input_data(shape=[None, 2])
net = tflearn.flatten(net)
net = tflearn.reshape(net, new_shape=[-1])
net = tflearn.activation(net, 'relu')
net = tflearn.dropout(net, 0.5)
net = tflearn.single_unit(net)
def encoder(inputs,hidden_layer):
net = tflearn.conv_2d(inputs, 16, 3, strides=2)
net = tflearn.batch_normalization(net)
net = tflearn.elu(net)
print "========================"
print "enc-L1",net.get_shape()
print "========================"
net = tflearn.conv_2d(net, 16, 3, strides=1)
net = tflearn.batch_normalization(net)
net = tflearn.elu(net)
print "========================"
print "enc-L2",net.get_shape()
print "========================"
net = tflearn.conv_2d(net, 32, 3, strides=2)
net = tflearn.batch_normalization(net)
net = tflearn.elu(net)
print "========================"
print "enc-L3",net.get_shape()
print "========================"
net = tflearn.conv_2d(net, 32, 3, strides=1)
net = tflearn.batch_normalization(net)
net = tflearn.elu(net)
print "========================"
print "enc-L4",net.get_shape()
print "========================"
net = tflearn.flatten(net)
#net = tflearn.fully_connected(net, nb_feature,activation="sigmoid")
net = tflearn.fully_connected(net, nb_feature)
hidden_layer = net
net = tflearn.batch_normalization(net)
net = tflearn.sigmoid(net)
print "========================"
print "hidden",net.get_shape()
print "========================"
return [net,hidden_layer]
def _build_network(self, layers):
network = tf.transpose(self.input_tensor, [0, 2, 3, 1])
# [batch, assets, window, features]
network = network / network[:, :, -1, 0, None, None]
for layer_number, layer in enumerate(layers):
if layer["type"] == "DenseLayer":
network = tflearn.layers.core.fully_connected(network,
int(layer["neuron_number"]),
layer["activation_function"],
regularizer=layer["regularizer"],
weight_decay=layer["weight_decay"] )
elif layer["type"] == "DropOut":
network = tflearn.layers.core.dropout(network, layer["keep_probability"])
elif layer["type"] == "EIIE_Dense":
width = network.get_shape()[2]
network = tflearn.layers.conv_2d(network, int(layer["filter_number"]),
[1, width],
[1, 1],
"valid",
layer["activation_function"],
regularizer=layer["regularizer"],
weight_decay=layer["weight_decay"])
elif layer["type"] == "ConvLayer":
network = tflearn.layers.conv_2d(network, int(layer["filter_number"]),
allint(layer["filter_shape"]),
allint(layer["strides"]),
layer["padding"],
layer["activation_function"],
regularizer=layer["regularizer"],
weight_decay=layer["weight_decay"])
elif layer["type"] == "MaxPooling":
network = tflearn.layers.conv.max_pool_2d(network, layer["strides"])
elif layer["type"] == "AveragePooling":
network = tflearn.layers.conv.avg_pool_2d(network, layer["strides"])
elif layer["type"] == "LocalResponseNormalization":
network = tflearn.layers.normalization.local_response_normalization(network)
elif layer["type"] == "EIIE_Output":
width = network.get_shape()[2]
network = tflearn.layers.conv_2d(network, 1, [1, width], padding="valid",
regularizer=layer["regularizer"],
weight_decay=layer["weight_decay"])
network = network[:, :, 0, 0]
btc_bias = tf.ones((self.input_num, 1))
network = tf.concat([btc_bias, network], 1)
network = tflearn.layers.core.activation(network, activation="softmax")
elif layer["type"] == "Output_WithW":
network = tflearn.flatten(network)
network = tf.concat([network,self.previous_w], axis=1)
network = tflearn.fully_connected(network, self._rows+1,
activation="softmax",
regularizer=layer["regularizer"],
weight_decay=layer["weight_decay"])
elif layer["type"] == "EIIE_Output_WithW":
width = network.get_shape()[2]
height = network.get_shape()[1]
features = network.get_shape()[3]
network = tf.reshape(network, [self.input_num, int(height), 1, int(width*features)])
w = tf.reshape(self.previous_w, [-1, int(height), 1, 1])
network = tf.concat([network, w], axis=3)
network = tflearn.layers.conv_2d(network, 1, [1, 1], padding="valid",
regularizer=layer["regularizer"],
weight_decay=layer["weight_decay"])
network = network[:, :, 0, 0]
#btc_bias = tf.zeros((self.input_num, 1))
btc_bias = tf.get_variable("btc_bias", [1, 1], dtype=tf.float32,
initializer=tf.zeros_initializer)
btc_bias = tf.tile(btc_bias, [self.input_num, 1])
network = tf.concat([btc_bias, network], 1)
self.voting = network
network = tflearn.layers.core.activation(network, activation="softmax")
elif layer["type"] == "EIIE_LSTM" or\
layer["type"] == "EIIE_RNN":
network = tf.transpose(network, [0, 2, 3, 1])
resultlist = []
reuse = False
for i in range(self._rows):
if i > 0:
reuse = True
if layer["type"] == "EIIE_LSTM":
result = tflearn.layers.lstm(network[:, :, :, i],
int(layer["neuron_number"]),
dropout=layer["dropouts"],
scope="lstm"+str(layer_number),
reuse=reuse)
else:
result = tflearn.layers.simple_rnn(network[:, :, :, i],
int(layer["neuron_number"]),
dropout=layer["dropouts"],
scope="rnn"+str(layer_number),
reuse=reuse)
resultlist.append(result)
network = tf.stack(resultlist)
network = tf.transpose(network, [1, 0, 2])
network = tf.reshape(network, [-1, self._rows, 1, int(layer["neuron_number"])])
else:
raise ValueError("the layer {} not supported.".format(layer["type"]))
return network