Python tflearn.max_pool_2d() Examples
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code examples of tflearn.max_pool_2d().
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Example #1
| Source File: cnn.py From QARC with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def CNN_Core(x, reuse=False):
with tf.variable_scope('cnn_core', reuse=reuse):
network = tflearn.conv_2d(
x, KERNEL, 5, activation='relu', regularizer="L2", weight_decay=0.0001)
network = tflearn.max_pool_2d(network, 2)
network = tflearn.conv_2d(
network, KERNEL, 3, activation='relu', regularizer="L2", weight_decay=0.0001)
network = tflearn.max_pool_2d(network, 2)
network = tflearn.conv_2d(
network, KERNEL, 3, activation='relu', regularizer="L2", weight_decay=0.0001)
network = tflearn.max_pool_2d(network, 2)
network = tflearn.conv_2d(
network, KERNEL // 2, 3, activation='relu', regularizer="L2", weight_decay=0.0001)
# network = tflearn.fully_connected(
# network, DENSE_SIZE, activation='relu')
split_flat = tflearn.flatten(network)
return split_flat
Example #2
| Source File: cnn.py From QARC with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def CNN_Core(x, reuse=False):
with tf.variable_scope('cnn_core', reuse=reuse):
network = tflearn.conv_2d(
x, KERNEL, 5, activation='relu', regularizer="L2", weight_decay=0.0001)
network = tflearn.max_pool_2d(network, 2)
network = tflearn.conv_2d(
network, KERNEL, 3, activation='relu', regularizer="L2", weight_decay=0.0001)
network = tflearn.max_pool_2d(network, 2)
network = tflearn.conv_2d(
network, KERNEL, 3, activation='relu', regularizer="L2", weight_decay=0.0001)
network = tflearn.max_pool_2d(network, 2)
network = tflearn.conv_2d(
network, KERNEL // 2, 3, activation='relu', regularizer="L2", weight_decay=0.0001)
# network = tflearn.fully_connected(
# network, DENSE_SIZE, activation='relu')
split_flat = tflearn.flatten(network)
return split_flat
Example #3
| Source File: test_layers.py From FRU with MIT License | 6 votes |
|
def test_feed_dict_no_None(self):
X = [[0., 0., 0., 0.], [1., 1., 1., 1.], [0., 0., 1., 0.], [1., 1., 1., 0.]]
Y = [[1., 0.], [0., 1.], [1., 0.], [0., 1.]]
with tf.Graph().as_default():
g = tflearn.input_data(shape=[None, 4], name="X_in")
g = tflearn.reshape(g, new_shape=[-1, 2, 2, 1])
g = tflearn.conv_2d(g, 4, 2)
g = tflearn.conv_2d(g, 4, 1)
g = tflearn.max_pool_2d(g, 2)
g = tflearn.fully_connected(g, 2, activation='softmax')
g = tflearn.regression(g, optimizer='sgd', learning_rate=1.)
m = tflearn.DNN(g)
def do_fit():
m.fit({"X_in": X, 'non_existent': X}, Y, n_epoch=30, snapshot_epoch=False)
self.assertRaisesRegexp(Exception, "Feed dict asks for variable named 'non_existent' but no such variable is known to exist", do_fit)
Example #4
| Source File: model.py From tensorflow2caffe with MIT License | 5 votes |
|
def vgg_net_19(width, height):
network = input_data(shape=[None, height, width, 3], name='input')
network = conv_2d(network, 64, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = conv_2d(network, 64, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = max_pool_2d(network, 2, strides=2)
network = conv_2d(network, 128, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = conv_2d(network, 128, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = max_pool_2d(network, 2, strides=2)
network = conv_2d(network, 256, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = conv_2d(network, 256, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = conv_2d(network, 256, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = conv_2d(network, 256, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = max_pool_2d(network, 2, strides=2)
network = conv_2d(network, 512, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = conv_2d(network, 512, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = conv_2d(network, 512, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = conv_2d(network, 512, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = max_pool_2d(network, 2, strides=2)
network = conv_2d(network, 512, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = conv_2d(network, 512, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = conv_2d(network, 512, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = conv_2d(network, 512, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = max_pool_2d(network, 2, strides=2)
network = fully_connected(network, 4096, activation='relu', weight_decay=5e-4)
network = dropout(network, keep_prob=0.5)
network = fully_connected(network, 4096, activation='relu', weight_decay=5e-4)
network = dropout(network, keep_prob=0.5)
network = fully_connected(network, 1000, activation='softmax', weight_decay=5e-4)
opt = Momentum(learning_rate=0, momentum = 0.9)
network = regression(network, optimizer=opt, loss='categorical_crossentropy', name='targets')
model = DNN(network, checkpoint_path='', max_checkpoints=1, tensorboard_verbose=2, tensorboard_dir='')
return model
#model of vgg-19 for testing of the activations
#rename the output you want to test, connect it to the next layer and change the output layer at the bottom (model = DNN(...))
#make sure to use the correct test function (depending if your output is a tensor or a vector)
Example #5
| Source File: model.py From tensorflow2caffe with MIT License | 5 votes |
|
def vgg_net_19_activations(width, height):
network = input_data(shape=[None, height, width, 3], name='input')
network1 = conv_2d(network, 64, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network2 = conv_2d(network1, 64, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = max_pool_2d(network2, 2, strides=2)
network = conv_2d(network, 128, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = conv_2d(network, 128, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = max_pool_2d(network, 2, strides=2)
network = conv_2d(network, 256, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = conv_2d(network, 256, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = conv_2d(network, 256, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = conv_2d(network, 256, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = max_pool_2d(network, 2, strides=2)
network = conv_2d(network, 512, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = conv_2d(network, 512, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = conv_2d(network, 512, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = conv_2d(network, 512, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = max_pool_2d(network, 2, strides=2)
network = conv_2d(network, 512, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = conv_2d(network, 512, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = conv_2d(network, 512, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = conv_2d(network, 512, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = max_pool_2d(network, 2, strides=2)
network = fully_connected(network, 4096, activation='relu', weight_decay=5e-4)
network = dropout(network, keep_prob=0.5)
network = fully_connected(network, 4096, activation='relu', weight_decay=5e-4)
network = dropout(network, keep_prob=0.5)
network = fully_connected(network, 1000, activation='softmax', weight_decay=5e-4)
opt = Momentum(learning_rate=0, momentum = 0.9)
network = regression(network, optimizer=opt, loss='categorical_crossentropy', name='targets')
model = DNN(network1, checkpoint_path='', max_checkpoints=1, tensorboard_verbose=2, tensorboard_dir='')
return model
Example #6
| Source File: gray.py From QARC with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
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)
cnn_network = tflearn.conv_2d(
network, KERNEL * 2, 3, activation='relu', regularizer="L2", weight_decay=0.0001)
#network = tflearn.max_pool_2d(network, 2)
# network = tflearn.conv_2d(
# network, KERNEL * 4, 3, activation='relu', regularizer="L2", weight_decay=0.0001)
#network = tflearn.max_pool_2d(network, 2)
network = tflearn.global_avg_pool(cnn_network)
split_flat = tflearn.flatten(network)
#print split_flat.get_shape().as_list()
return split_flat, cnn_network
Example #7
| Source File: qarc.py From QARC with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def vqn_model(self, 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(self.CNN_Core(tmp_network))
else:
_split_array.append(self.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-cnn'):
net = tf.reshape(
merge_net, [-1, INPUT_SEQ, _count / INPUT_SEQ, 1])
network = tflearn.conv_2d(
net, KERNEL, 5, activation='relu', regularizer="L2", weight_decay=0.0001)
network = tflearn.max_pool_2d(network, 3)
network = tflearn.layers.normalization.batch_normalization(
network)
network = tflearn.conv_2d(
network, KERNEL, 3, activation='relu', regularizer="L2", weight_decay=0.0001)
network = tflearn.max_pool_2d(network, 2)
network = tflearn.layers.normalization.batch_normalization(
network)
cnn_result = tflearn.fully_connected(
network, DENSE_SIZE, activation='relu')
out = tflearn.fully_connected(
cnn_result, OUTPUT_DIM, activation='sigmoid')
return out
Example #8
| Source File: vqn-new.py From QARC with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
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
Example #9
| Source File: vqn.py From QARC with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def vgg16(input, num_class):
x = tflearn.conv_2d(input, 64, 3, activation='relu', scope='conv1_1')
x = tflearn.conv_2d(x, 64, 3, activation='relu', scope='conv1_2')
x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool1')
x = tflearn.conv_2d(x, 128, 3, activation='relu', scope='conv2_1')
x = tflearn.conv_2d(x, 128, 3, activation='relu', scope='conv2_2')
x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool2')
x = tflearn.conv_2d(x, 256, 3, activation='relu', scope='conv3_1')
x = tflearn.conv_2d(x, 256, 3, activation='relu', scope='conv3_2')
x = tflearn.conv_2d(x, 256, 3, activation='relu', scope='conv3_3')
x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool3')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv4_1')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv4_2')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv4_3')
x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool4')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv5_1')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv5_2')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv5_3')
x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool5')
x = tflearn.fully_connected(x, 4096, activation='relu', scope='fc6')
x = tflearn.dropout(x, 0.5, name='dropout1')
x = tflearn.fully_connected(x, 4096, activation='relu', scope='fc7')
x = tflearn.dropout(x, 0.5, name='dropout2')
x = tflearn.fully_connected(
x, num_class, activation='sigmoid', scope='fc8', restore=False)
return x
Example #10
| Source File: convert_VQPN.py From QARC with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def vqn_model(self, 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(self.CNN_Core(tmp_network))
else:
_split_array.append(self.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-cnn'):
net = tf.reshape(
merge_net, [-1, INPUT_SEQ, _count / INPUT_SEQ, 1])
network = tflearn.conv_2d(
net, KERNEL, 5, activation='relu', regularizer="L2", weight_decay=0.0001)
network = tflearn.max_pool_2d(network, 3)
network = tflearn.layers.normalization.batch_normalization(
network)
network = tflearn.conv_2d(
network, KERNEL, 3, activation='relu', regularizer="L2", weight_decay=0.0001)
network = tflearn.max_pool_2d(network, 2)
network = tflearn.layers.normalization.batch_normalization(
network)
cnn_result = tflearn.fully_connected(
network, DENSE_SIZE, activation='relu')
out = tflearn.fully_connected(
cnn_result, OUTPUT_DIM, activation='sigmoid')
return out
Example #11
| Source File: vgg16.py From models with MIT License | 5 votes |
|
def vgg16(placeholderX=None):
x = tflearn.input_data(shape=[None, 224, 224, 3], name='input',
placeholder=placeholderX)
x = tflearn.conv_2d(x, 64, 3, activation='relu', scope='conv1_1')
x = tflearn.conv_2d(x, 64, 3, activation='relu', scope='conv1_2')
x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool1')
x = tflearn.conv_2d(x, 128, 3, activation='relu', scope='conv2_1')
x = tflearn.conv_2d(x, 128, 3, activation='relu', scope='conv2_2')
x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool2')
x = tflearn.conv_2d(x, 256, 3, activation='relu', scope='conv3_1')
x = tflearn.conv_2d(x, 256, 3, activation='relu', scope='conv3_2')
x = tflearn.conv_2d(x, 256, 3, activation='relu', scope='conv3_3')
x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool3')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv4_1')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv4_2')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv4_3')
x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool4')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv5_1')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv5_2')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv5_3')
x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool5')
x = tflearn.fully_connected(x, 4096, activation='relu', scope='fc6')
x = tflearn.dropout(x, 0.5, name='dropout1')
x = tflearn.fully_connected(x, 4096, activation='relu', scope='fc7')
x = tflearn.dropout(x, 0.5, name='dropout2')
x = tflearn.fully_connected(x, 1000, activation='softmax', scope='fc8')
return x
Example #12
| Source File: vgg_network_finetuning.py From FRU with MIT License | 5 votes |
|
def vgg16(input, num_class):
x = tflearn.conv_2d(input, 64, 3, activation='relu', scope='conv1_1')
x = tflearn.conv_2d(x, 64, 3, activation='relu', scope='conv1_2')
x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool1')
x = tflearn.conv_2d(x, 128, 3, activation='relu', scope='conv2_1')
x = tflearn.conv_2d(x, 128, 3, activation='relu', scope='conv2_2')
x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool2')
x = tflearn.conv_2d(x, 256, 3, activation='relu', scope='conv3_1')
x = tflearn.conv_2d(x, 256, 3, activation='relu', scope='conv3_2')
x = tflearn.conv_2d(x, 256, 3, activation='relu', scope='conv3_3')
x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool3')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv4_1')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv4_2')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv4_3')
x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool4')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv5_1')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv5_2')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv5_3')
x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool5')
x = tflearn.fully_connected(x, 4096, activation='relu', scope='fc6')
x = tflearn.dropout(x, 0.5, name='dropout1')
x = tflearn.fully_connected(x, 4096, activation='relu', scope='fc7')
x = tflearn.dropout(x, 0.5, name='dropout2')
x = tflearn.fully_connected(x, num_class, activation='softmax', scope='fc8',
restore=False)
return x
Example #13
| Source File: test_layers.py From FRU with MIT License | 5 votes |
|
def test_conv_layers(self):
X = [[0., 0., 0., 0.], [1., 1., 1., 1.], [0., 0., 1., 0.], [1., 1., 1., 0.]]
Y = [[1., 0.], [0., 1.], [1., 0.], [0., 1.]]
with tf.Graph().as_default():
g = tflearn.input_data(shape=[None, 4])
g = tflearn.reshape(g, new_shape=[-1, 2, 2, 1])
g = tflearn.conv_2d(g, 4, 2, activation='relu')
g = tflearn.max_pool_2d(g, 2)
g = tflearn.fully_connected(g, 2, activation='softmax')
g = tflearn.regression(g, optimizer='sgd', learning_rate=1.)
m = tflearn.DNN(g)
m.fit(X, Y, n_epoch=100, snapshot_epoch=False)
# TODO: Fix test
#self.assertGreater(m.predict([[1., 0., 0., 0.]])[0][0], 0.5)
# Bulk Tests
with tf.Graph().as_default():
g = tflearn.input_data(shape=[None, 4])
g = tflearn.reshape(g, new_shape=[-1, 2, 2, 1])
g = tflearn.conv_2d(g, 4, 2)
g = tflearn.conv_2d(g, 4, 1)
g = tflearn.conv_2d_transpose(g, 4, 2, [2, 2])
g = tflearn.max_pool_2d(g, 2)
Example #14
| Source File: texture_net.py From TensorFlowBook with Apache License 2.0 | 5 votes |
|
def generator(input_image):
conv2d = tflearn.conv_2d
batch_norm = tflearn.batch_normalization
relu = tf.nn.relu
ratios = [16, 8, 4, 2, 1]
n_filter = 8
net = []
for i in range(len(ratios)):
net.append(tflearn.max_pool_2d(input_image, ratios[i], ratios[i]))
# block_i_0, block_i_1, block_i_2
for block in range(3):
ksize = 1 if (block + 1) % 3 == 0 else 3
net[i] = relu(batch_norm(conv2d(net[i], n_filter, ksize)))
if i != 0:
# concat with net[i-1]
upnet = batch_norm(net[i - 1])
downnet = batch_norm(net[i])
net[i] = tf.concat(3, [upnet, downnet])
# block_i_3, block_i_4, block_i_5
for block in range(3, 6):
ksize = 1 if (block + 1) % 3 == 0 else 3
net[i] = conv2d(net[i], n_filter * (i + 1), ksize)
net[i] = relu(batch_norm(net[i]))
if i != len(ratios) - 1:
# upsample for concat
net[i] = tflearn.upsample_2d(net[i], 2)
nn = len(ratios) - 1
output = conv2d(net[nn], 3, 1)
return output
Example #15
| Source File: vqn-cnn.py From QARC with BSD 3-Clause "New" or "Revised" License | 4 votes |
|
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-cnn'):
net = tf.reshape(merge_net, [-1, INPUT_SEQ, _count / INPUT_SEQ, 1])
network = tflearn.conv_2d(
net, KERNEL, 5, activation='relu', regularizer="L2", weight_decay=0.0001)
network = tflearn.max_pool_2d(network, 3)
network = tflearn.layers.normalization.batch_normalization(network)
network = tflearn.conv_2d(
network, KERNEL, 3, activation='relu', regularizer="L2", weight_decay=0.0001)
network = tflearn.max_pool_2d(network, 2)
network = tflearn.layers.normalization.batch_normalization(network)
CNN_result = tflearn.fully_connected(
network, DENSE_SIZE, activation='relu')
#CNN_result = tflearn.fully_connected(CNN_result, OUTPUT_DIM, activation='sigmoid')
# with tf.variable_scope('full-gru'):
# 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')
#gru_result = tflearn.fully_connected(gru_result, OUTPUT_DIM, activation='sigmoid')
merge_net = tflearn.merge([gru_result, CNN_result], 'concat')
out = tflearn.fully_connected(
CNN_result, OUTPUT_DIM, activation='sigmoid')
return out
