Python tensorflow.contrib.slim.repeat() Examples
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code examples of tensorflow.contrib.slim.repeat().
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Example #1
| Source File: vgg16.py From SSH-TensorFlow with MIT License | 6 votes |
|
def _image_to_head(self, is_training, reuse=None):
with tf.variable_scope(self._scope, self._scope, reuse=reuse):
net = slim.repeat(self._image, 2, slim.conv2d, 64, [3, 3],
trainable=False, scope='conv1')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool1')
net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3],
trainable=False, scope='conv2')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool2')
net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3],
trainable=is_training, scope='conv3')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool3')
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3],
trainable=is_training, scope='conv4')
self.end_points['conv4_3'] = net
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool4')
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3],
trainable=is_training, scope='conv5')
self.end_points['conv5_3'] = net
self._act_summaries.append(net)
self._layers['head'] = net
Example #2
| Source File: vgg16.py From tf-faster-rcnn with MIT License | 6 votes |
|
def _image_to_head(self, is_training, reuse=None):
with tf.variable_scope(self._scope, self._scope, reuse=reuse):
net = slim.repeat(self._image, 2, slim.conv2d, 64, [3, 3],
trainable=False, scope='conv1')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool1')
net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3],
trainable=False, scope='conv2')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool2')
net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3],
trainable=is_training, scope='conv3')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool3')
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3],
trainable=is_training, scope='conv4')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool4')
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3],
trainable=is_training, scope='conv5')
self._act_summaries.append(net)
self._layers['head'] = net
return net
Example #3
| Source File: vgg16.py From tf_ctpn with MIT License | 6 votes |
|
def _image_to_head(self, is_training, reuse=None):
with tf.variable_scope(self._scope, self._scope, reuse=reuse):
net = slim.repeat(self._image, 2, slim.conv2d, 64, [3, 3],
trainable=True, scope='conv1')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool1')
net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3],
trainable=True, scope='conv2')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool2')
net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3],
trainable=True, scope='conv3')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool3')
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3],
trainable=True, scope='conv4')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool4')
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3],
trainable=True, scope='conv5')
self._act_summaries.append(net)
self._layers['head'] = net
return net
Example #4
| Source File: vgg16.py From densecap-tensorflow with MIT License | 6 votes |
|
def _image_to_head(self, is_training, reuse=None):
with tf.variable_scope(self._vgg_scope, self._vgg_scope, reuse=reuse):
net = slim.repeat(self._image, 2, slim.conv2d, 64, [3, 3],
trainable=False, scope='conv1')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool1')
net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3],
trainable=False, scope='conv2')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool2')
net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3],
trainable=is_training, scope='conv3')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool3')
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3],
trainable=is_training, scope='conv4')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool4')
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3],
trainable=is_training, scope='conv5')
self._act_summaries.append(net)
self._layers['head'] = net
return net
Example #5
| Source File: vgg16.py From iter-reason with MIT License | 6 votes |
|
def _image_to_head(self, is_training, reuse=None):
with tf.variable_scope(self._scope, self._scope, reuse=reuse):
net = slim.repeat(self._image, 2, slim.conv2d, 64, [3, 3],
trainable=False, scope='conv1')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool1')
net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3],
trainable=False, scope='conv2')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool2')
net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3],
trainable=is_training, scope='conv3')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool3')
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3],
trainable=is_training, scope='conv4')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool4')
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3],
trainable=is_training, scope='conv5')
self._act_summaries.append(net)
self._layers['head'] = net
return net
Example #6
| Source File: tf_ops.py From long-term-video-prediction-without-supervision with Apache License 2.0 | 6 votes |
|
def get_repeat(end_points, prefix, final_endpoint):
"""Simulate `slim.repeat`, and add to endpoints dictionary."""
def repeat(net, repetitions, layer, *args, **kwargs):
base_scope = kwargs['scope']
add_and_check_is_final = get_add_and_check_is_final(end_points, prefix,
final_endpoint)
with tf.variable_scope(base_scope, [net]):
for i in xrange(repetitions):
kwargs['scope'] = base_scope + '_' + str(i + 1)
net = layer(net, *args, **kwargs)
if add_and_check_is_final('%s_%i' % (base_scope, i), net):
break
return net
return repeat
Example #7
| Source File: benchmark_tensorflow.py From vgg-benchmarks with MIT License | 6 votes |
|
def vgg16(inputs, num_classes, batch_size):
with slim.arg_scope([slim.conv2d, slim.fully_connected],
activation_fn=tf.nn.relu,
weights_initializer=tf.truncated_normal_initializer(0.0, 0.01)):
net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], padding="SAME", scope='conv1')
net = slim.max_pool2d(net, [2, 2], scope='pool1')
net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], padding="SAME", scope='conv2')
net = slim.max_pool2d(net, [2, 2], scope='pool2')
net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], padding="SAME", scope='conv3')
net = slim.max_pool2d(net, [2, 2], scope='pool3')
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], padding="SAME", scope='conv4')
net = slim.max_pool2d(net, [2, 2], scope='pool4')
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], padding="SAME", scope='conv5')
net = slim.max_pool2d(net, [2, 2], scope='pool5')
net = tf.reshape(net, (batch_size, 7 * 7 * 512))
net = slim.fully_connected(net, 4096, scope='fc6')
net = slim.dropout(net, 0.5, scope='dropout6')
net = slim.fully_connected(net, 4096, scope='fc7')
net = slim.dropout(net, 0.5, scope='dropout7')
net = slim.fully_connected(net, 1000, activation_fn=None, scope='fc8')
return net
Example #8
| Source File: test_tf_converter.py From tf-coreml with Apache License 2.0 | 5 votes |
|
def test_slim_repeat(self):
graph = tf.Graph()
with graph.as_default() as g:
inputs = tf.placeholder(tf.float32, shape=[None,16,16,3],
name='test_slim_repeat/input')
with slim.arg_scope([slim.conv2d], padding='SAME',
weights_initializer=tf.truncated_normal_initializer(stddev=0.3),
weights_regularizer=slim.l2_regularizer(0.0005)):
net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')
output_name = [net.op.name]
self._test_tf_model(graph,
{"test_slim_repeat/input:0":[1,16,16,3]},
output_name, delta=1e-2)
Example #9
| Source File: dfc_vae_resnet.py From facenet_mtcnn_to_mobile with MIT License | 5 votes |
|
def decoder(self, latent_var, is_training):
activation_fn = leaky_relu # tf.nn.relu
weight_decay = 0.0
with tf.variable_scope('decoder'):
with slim.arg_scope([slim.batch_norm],
is_training=is_training):
with slim.arg_scope([slim.conv2d, slim.fully_connected],
weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
weights_regularizer=slim.l2_regularizer(weight_decay),
normalizer_fn=slim.batch_norm,
normalizer_params=self.batch_norm_params):
net = slim.fully_connected(latent_var, 4096, activation_fn=None, normalizer_fn=None, scope='Fc_1')
net = tf.reshape(net, [-1,4,4,256], name='Reshape')
net = tf.image.resize_nearest_neighbor(net, size=(8,8), name='Upsample_1')
net = slim.conv2d(net, 128, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_1a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 128, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_1b')
net = tf.image.resize_nearest_neighbor(net, size=(16,16), name='Upsample_2')
net = slim.conv2d(net, 64, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_2a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 64, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_2b')
net = tf.image.resize_nearest_neighbor(net, size=(32,32), name='Upsample_3')
net = slim.conv2d(net, 32, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_3a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 32, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_3b')
net = tf.image.resize_nearest_neighbor(net, size=(64,64), name='Upsample_4')
net = slim.conv2d(net, 3, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_4a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 3, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_4b')
net = slim.conv2d(net, 3, [3, 3], 1, activation_fn=None, scope='Conv2d_4c')
return net
Example #10
| Source File: layers.py From simulated-unsupervised-tensorflow with Apache License 2.0 | 5 votes |
|
def repeat(inputs, repetitions, layer, layer_dict={}, **kargv):
outputs = slim.repeat(inputs, repetitions, layer, **kargv)
_update_dict(layer_dict, kargv['scope'], outputs)
return outputs
Example #11
| Source File: KalmanVariationalAutoencoder.py From kvae with MIT License | 5 votes |
|
def encoder(self, x):
""" Convolutional variational encoder to encode image into a low-dimensional latent code
If config.conv == False it is a MLP VAE. If config.use_vae == False, it is a normal encoder
:param x: sequence of images
:return: a, a_mu, a_var
"""
with tf.variable_scope('vae/encoder'):
if self.config.conv:
x_flat_conv = tf.reshape(x, (-1, self.d1, self.d2, 1))
enc_hidden = slim.stack(x_flat_conv,
slim.conv2d,
self.num_filters,
kernel_size=self.config.filter_size,
stride=2,
activation_fn=self.activation_fn,
padding='SAME')
enc_flat = slim.flatten(enc_hidden)
self.enc_shape = enc_hidden.get_shape().as_list()[1:]
else:
x_flat = tf.reshape(x, (-1, self.d1 * self.d2))
enc_flat = slim.repeat(x_flat, self.config.num_layers, slim.fully_connected,
self.config.vae_num_units, self.activation_fn)
a_mu = slim.fully_connected(enc_flat, self.config.dim_a, activation_fn=None)
if self.config.use_vae:
a_var = slim.fully_connected(enc_flat, self.config.dim_a, activation_fn=tf.nn.sigmoid)
a_var = self.config.noise_emission * a_var
a = simple_sample(a_mu, a_var)
else:
a_var = tf.constant(1., dtype=tf.float32, shape=())
a = a_mu
a_seq = tf.reshape(a, tf.stack((-1, self.ph_steps, self.config.dim_a)))
return a_seq, a_mu, a_var
Example #12
| Source File: dfc_vae_resnet.py From facenet-demo with MIT License | 5 votes |
|
def decoder(self, latent_var, is_training):
activation_fn = leaky_relu # tf.nn.relu
weight_decay = 0.0
with tf.variable_scope('decoder'):
with slim.arg_scope([slim.batch_norm],
is_training=is_training):
with slim.arg_scope([slim.conv2d, slim.fully_connected],
weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
weights_regularizer=slim.l2_regularizer(weight_decay),
normalizer_fn=slim.batch_norm,
normalizer_params=self.batch_norm_params):
net = slim.fully_connected(latent_var, 4096, activation_fn=None, normalizer_fn=None, scope='Fc_1')
net = tf.reshape(net, [-1,4,4,256], name='Reshape')
net = tf.image.resize_nearest_neighbor(net, size=(8,8), name='Upsample_1')
net = slim.conv2d(net, 128, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_1a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 128, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_1b')
net = tf.image.resize_nearest_neighbor(net, size=(16,16), name='Upsample_2')
net = slim.conv2d(net, 64, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_2a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 64, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_2b')
net = tf.image.resize_nearest_neighbor(net, size=(32,32), name='Upsample_3')
net = slim.conv2d(net, 32, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_3a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 32, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_3b')
net = tf.image.resize_nearest_neighbor(net, size=(64,64), name='Upsample_4')
net = slim.conv2d(net, 3, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_4a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 3, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_4b')
net = slim.conv2d(net, 3, [3, 3], 1, activation_fn=None, scope='Conv2d_4c')
return net
Example #13
| Source File: vgg16.py From Faster-RCNN-TensorFlow-Python3 with MIT License | 5 votes |
|
def build_head(self, is_training):
# Main network
# Layer 1
net = slim.repeat(self._image, 2, slim.conv2d, 64, [3, 3], trainable=False, scope='conv1')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool1')
# Layer 2
net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], trainable=False, scope='conv2')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool2')
# Layer 3
net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], trainable=is_training, scope='conv3')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool3')
# Layer 4
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], trainable=is_training, scope='conv4')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool4')
# Layer 5
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], trainable=is_training, scope='conv5')
# Append network to summaries
self._act_summaries.append(net)
# Append network as head layer
self._layers['head'] = net
return net
Example #14
| Source File: dfc_vae_resnet.py From facenet-demo with MIT License | 5 votes |
|
def encoder(self, images, is_training):
activation_fn = leaky_relu # tf.nn.relu
weight_decay = 0.0
with tf.variable_scope('encoder'):
with slim.arg_scope([slim.batch_norm],
is_training=is_training):
with slim.arg_scope([slim.conv2d, slim.fully_connected],
weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
weights_regularizer=slim.l2_regularizer(weight_decay),
normalizer_fn=slim.batch_norm,
normalizer_params=self.batch_norm_params):
net = images
net = slim.conv2d(net, 32, [4, 4], 2, activation_fn=activation_fn, scope='Conv2d_1a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 32, [4, 4], 1, activation_fn=activation_fn, scope='Conv2d_1b')
net = slim.conv2d(net, 64, [4, 4], 2, activation_fn=activation_fn, scope='Conv2d_2a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 64, [4, 4], 1, activation_fn=activation_fn, scope='Conv2d_2b')
net = slim.conv2d(net, 128, [4, 4], 2, activation_fn=activation_fn, scope='Conv2d_3a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 128, [4, 4], 1, activation_fn=activation_fn, scope='Conv2d_3b')
net = slim.conv2d(net, 256, [4, 4], 2, activation_fn=activation_fn, scope='Conv2d_4a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 256, [4, 4], 1, activation_fn=activation_fn, scope='Conv2d_4b')
net = slim.flatten(net)
fc1 = slim.fully_connected(net, self.latent_variable_dim, activation_fn=None, normalizer_fn=None, scope='Fc_1')
fc2 = slim.fully_connected(net, self.latent_variable_dim, activation_fn=None, normalizer_fn=None, scope='Fc_2')
return fc1, fc2
Example #15
| Source File: ssd.py From SSD_tensorflow_VOC with Apache License 2.0 | 5 votes |
|
def get_model(self,inputs, weight_decay=0.0005,is_training=False):
# End_points collect relevant activations for external use.
arg_scope = self.__arg_scope(weight_decay=weight_decay)
with slim.arg_scope(arg_scope):
end_points = {}
with tf.variable_scope('vgg_16', [inputs]):
# Original VGG-16 blocks.
net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')
end_points['block1'] = net
net = slim.max_pool2d(net, [2, 2], scope='pool1')
# Block 2.
net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')
end_points['block2'] = net
net = slim.max_pool2d(net, [2, 2], scope='pool2')
# Block 3.
net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3')
end_points['block3'] = net
net = slim.max_pool2d(net, [2, 2], scope='pool3')
# Block 4.
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv4')
end_points['block4'] = net
net = slim.max_pool2d(net, [2, 2], scope='pool4')
# Block 5.
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv5')
end_points['block5'] = net
net = slim.max_pool2d(net, [3, 3], stride=1, scope='pool5')
# Additional SSD blocks.
keep_prob=0.8
with slim.arg_scope([slim.conv2d],
activation_fn=None):
with slim.arg_scope([slim.batch_norm],
activation_fn=tf.nn.relu, is_training=is_training,updates_collections=None):
with slim.arg_scope([slim.dropout],
is_training=is_training,keep_prob=keep_prob):
with tf.variable_scope(self.model_name):
return self.__additional_ssd_block(end_points, net)
Example #16
| Source File: layers.py From tf_autoencoder with Apache License 2.0 | 5 votes |
|
def conv_encoder(inputs, num_filters, scope=None):
net = inputs
with tf.variable_scope(scope, 'encoder', [inputs]):
tf.assert_rank(inputs, 4)
for layer_id, num_outputs in enumerate(num_filters):
with tf.variable_scope('block{}'.format(layer_id)):
net = slim.repeat(net, 2, conv2d_fixed_padding, num_outputs=num_outputs)
net = tf.contrib.layers.max_pool2d(net)
net = tf.identity(net, name='output')
return net
Example #17
| Source File: vgg16.py From GeetChinese_crack with MIT License | 5 votes |
|
def build_head(self, is_training):
# Main network
# Layer 1
net = slim.repeat(self._image, 2, slim.conv2d, 64, [3, 3], trainable=False, scope='conv1')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool1')
# Layer 2
net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], trainable=False, scope='conv2')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool2')
# Layer 3
net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], trainable=is_training, scope='conv3')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool3')
# Layer 4
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], trainable=is_training, scope='conv4')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool4')
# Layer 5
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], trainable=is_training, scope='conv5')
# Append network to summaries
self._act_summaries.append(net)
# Append network as head layer
self._layers['head'] = net
return net
Example #18
| Source File: dfc_vae_resnet.py From facenet with MIT License | 5 votes |
|
def encoder(self, images, is_training):
activation_fn = leaky_relu # tf.nn.relu
weight_decay = 0.0
with tf.variable_scope('encoder'):
with slim.arg_scope([slim.batch_norm],
is_training=is_training):
with slim.arg_scope([slim.conv2d, slim.fully_connected],
weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
weights_regularizer=slim.l2_regularizer(weight_decay),
normalizer_fn=slim.batch_norm,
normalizer_params=self.batch_norm_params):
net = images
net = slim.conv2d(net, 32, [4, 4], 2, activation_fn=activation_fn, scope='Conv2d_1a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 32, [4, 4], 1, activation_fn=activation_fn, scope='Conv2d_1b')
net = slim.conv2d(net, 64, [4, 4], 2, activation_fn=activation_fn, scope='Conv2d_2a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 64, [4, 4], 1, activation_fn=activation_fn, scope='Conv2d_2b')
net = slim.conv2d(net, 128, [4, 4], 2, activation_fn=activation_fn, scope='Conv2d_3a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 128, [4, 4], 1, activation_fn=activation_fn, scope='Conv2d_3b')
net = slim.conv2d(net, 256, [4, 4], 2, activation_fn=activation_fn, scope='Conv2d_4a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 256, [4, 4], 1, activation_fn=activation_fn, scope='Conv2d_4b')
net = slim.flatten(net)
fc1 = slim.fully_connected(net, self.latent_variable_dim, activation_fn=None, normalizer_fn=None, scope='Fc_1')
fc2 = slim.fully_connected(net, self.latent_variable_dim, activation_fn=None, normalizer_fn=None, scope='Fc_2')
return fc1, fc2
Example #19
| Source File: dfc_vae_resnet.py From facenet with MIT License | 5 votes |
|
def decoder(self, latent_var, is_training):
activation_fn = leaky_relu # tf.nn.relu
weight_decay = 0.0
with tf.variable_scope('decoder'):
with slim.arg_scope([slim.batch_norm],
is_training=is_training):
with slim.arg_scope([slim.conv2d, slim.fully_connected],
weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
weights_regularizer=slim.l2_regularizer(weight_decay),
normalizer_fn=slim.batch_norm,
normalizer_params=self.batch_norm_params):
net = slim.fully_connected(latent_var, 4096, activation_fn=None, normalizer_fn=None, scope='Fc_1')
net = tf.reshape(net, [-1,4,4,256], name='Reshape')
net = tf.image.resize_nearest_neighbor(net, size=(8,8), name='Upsample_1')
net = slim.conv2d(net, 128, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_1a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 128, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_1b')
net = tf.image.resize_nearest_neighbor(net, size=(16,16), name='Upsample_2')
net = slim.conv2d(net, 64, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_2a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 64, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_2b')
net = tf.image.resize_nearest_neighbor(net, size=(32,32), name='Upsample_3')
net = slim.conv2d(net, 32, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_3a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 32, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_3b')
net = tf.image.resize_nearest_neighbor(net, size=(64,64), name='Upsample_4')
net = slim.conv2d(net, 3, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_4a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 3, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_4b')
net = slim.conv2d(net, 3, [3, 3], 1, activation_fn=None, scope='Conv2d_4c')
return net
Example #20
| Source File: dfc_vae_resnet.py From facenet_mtcnn_to_mobile with MIT License | 5 votes |
|
def encoder(self, images, is_training):
activation_fn = leaky_relu # tf.nn.relu
weight_decay = 0.0
with tf.variable_scope('encoder'):
with slim.arg_scope([slim.batch_norm],
is_training=is_training):
with slim.arg_scope([slim.conv2d, slim.fully_connected],
weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
weights_regularizer=slim.l2_regularizer(weight_decay),
normalizer_fn=slim.batch_norm,
normalizer_params=self.batch_norm_params):
net = images
net = slim.conv2d(net, 32, [4, 4], 2, activation_fn=activation_fn, scope='Conv2d_1a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 32, [4, 4], 1, activation_fn=activation_fn, scope='Conv2d_1b')
net = slim.conv2d(net, 64, [4, 4], 2, activation_fn=activation_fn, scope='Conv2d_2a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 64, [4, 4], 1, activation_fn=activation_fn, scope='Conv2d_2b')
net = slim.conv2d(net, 128, [4, 4], 2, activation_fn=activation_fn, scope='Conv2d_3a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 128, [4, 4], 1, activation_fn=activation_fn, scope='Conv2d_3b')
net = slim.conv2d(net, 256, [4, 4], 2, activation_fn=activation_fn, scope='Conv2d_4a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 256, [4, 4], 1, activation_fn=activation_fn, scope='Conv2d_4b')
net = slim.flatten(net)
fc1 = slim.fully_connected(net, self.latent_variable_dim, activation_fn=None, normalizer_fn=None, scope='Fc_1')
fc2 = slim.fully_connected(net, self.latent_variable_dim, activation_fn=None, normalizer_fn=None, scope='Fc_2')
return fc1, fc2
Example #21
| Source File: vgg16.py From MSDS-RCNN with MIT License | 5 votes |
|
def _image_to_rpn_single(self, is_training, initializer, reuse=False):
"""
Single modality input
"""
with tf.variable_scope(self._scope, self._scope, reuse=reuse):
net_rgb = slim.repeat(self._image, 2, slim.conv2d, 64, [3, 3],
trainable=is_training and 1 > cfg.VGG16.FIXED_BLOCKS, scope='conv1')
self._tensor4debug['net_rgb1'] = net_rgb
net_rgb = slim.max_pool2d(net_rgb, [2, 2], padding='SAME', scope='pool1')
net_rgb = slim.repeat(net_rgb, 2, slim.conv2d, 128, [3, 3],
trainable=is_training and 2 > cfg.VGG16.FIXED_BLOCKS, scope='conv2')
net_rgb = slim.max_pool2d(net_rgb, [2, 2], padding='SAME', scope='pool2')
net_rgb = slim.repeat(net_rgb, 3, slim.conv2d, 256, [3, 3],
trainable=is_training and 3 > cfg.VGG16.FIXED_BLOCKS, scope='conv3')
net_rgb = slim.max_pool2d(net_rgb, [2, 2], padding='SAME', scope='pool3')
net_rgb = slim.repeat(net_rgb, 3, slim.conv2d, 512, [3, 3],
trainable=is_training and 4 > cfg.VGG16.FIXED_BLOCKS, scope='conv4')
if not cfg.REMOVE_POOLING:
net_rgb = slim.max_pool2d(net_rgb, [2, 2], padding='SAME', scope='pool4')
net_rgb = slim.repeat(net_rgb, 3, slim.conv2d, 512, [3, 3],
trainable=is_training, scope='conv5')
self._act_summaries.append(net_rgb)
self._layers['head'] = net_rgb
self._tensor4debug['net_rgb'] = net_rgb
return net_rgb
Example #22
| Source File: dfc_vae_resnet.py From tindetheus with MIT License | 5 votes |
|
def decoder(self, latent_var, is_training):
activation_fn = leaky_relu # tf.nn.relu
weight_decay = 0.0
with tf.variable_scope('decoder'):
with slim.arg_scope([slim.batch_norm],
is_training=is_training):
with slim.arg_scope([slim.conv2d, slim.fully_connected],
weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
weights_regularizer=slim.l2_regularizer(weight_decay),
normalizer_fn=slim.batch_norm,
normalizer_params=self.batch_norm_params):
net = slim.fully_connected(latent_var, 4096, activation_fn=None, normalizer_fn=None, scope='Fc_1')
net = tf.reshape(net, [-1,4,4,256], name='Reshape')
net = tf.image.resize_nearest_neighbor(net, size=(8,8), name='Upsample_1')
net = slim.conv2d(net, 128, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_1a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 128, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_1b')
net = tf.image.resize_nearest_neighbor(net, size=(16,16), name='Upsample_2')
net = slim.conv2d(net, 64, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_2a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 64, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_2b')
net = tf.image.resize_nearest_neighbor(net, size=(32,32), name='Upsample_3')
net = slim.conv2d(net, 32, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_3a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 32, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_3b')
net = tf.image.resize_nearest_neighbor(net, size=(64,64), name='Upsample_4')
net = slim.conv2d(net, 3, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_4a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 3, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_4b')
net = slim.conv2d(net, 3, [3, 3], 1, activation_fn=None, scope='Conv2d_4c')
return net
Example #23
| Source File: dfc_vae_resnet.py From tindetheus with MIT License | 5 votes |
|
def encoder(self, images, is_training):
activation_fn = leaky_relu # tf.nn.relu
weight_decay = 0.0
with tf.variable_scope('encoder'):
with slim.arg_scope([slim.batch_norm],
is_training=is_training):
with slim.arg_scope([slim.conv2d, slim.fully_connected],
weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
weights_regularizer=slim.l2_regularizer(weight_decay),
normalizer_fn=slim.batch_norm,
normalizer_params=self.batch_norm_params):
net = images
net = slim.conv2d(net, 32, [4, 4], 2, activation_fn=activation_fn, scope='Conv2d_1a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 32, [4, 4], 1, activation_fn=activation_fn, scope='Conv2d_1b')
net = slim.conv2d(net, 64, [4, 4], 2, activation_fn=activation_fn, scope='Conv2d_2a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 64, [4, 4], 1, activation_fn=activation_fn, scope='Conv2d_2b')
net = slim.conv2d(net, 128, [4, 4], 2, activation_fn=activation_fn, scope='Conv2d_3a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 128, [4, 4], 1, activation_fn=activation_fn, scope='Conv2d_3b')
net = slim.conv2d(net, 256, [4, 4], 2, activation_fn=activation_fn, scope='Conv2d_4a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 256, [4, 4], 1, activation_fn=activation_fn, scope='Conv2d_4b')
net = slim.flatten(net)
fc1 = slim.fully_connected(net, self.latent_variable_dim, activation_fn=None, normalizer_fn=None, scope='Fc_1')
fc2 = slim.fully_connected(net, self.latent_variable_dim, activation_fn=None, normalizer_fn=None, scope='Fc_2')
return fc1, fc2
Example #24
| Source File: train.py From DeepBlending with Apache License 2.0 | 5 votes |
|
def vgg_16(inputs, scope='vgg_16'):
"""Computes deep image features as the first two maxpooling layers of a VGG16 network"""
with tf.variable_scope('vgg_16', 'vgg_16', [inputs], reuse=tf.AUTO_REUSE) as sc:
end_points_collection = sc.original_name_scope + '_end_points'
with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
outputs_collections=end_points_collection):
net_a = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')
net_b = slim.max_pool2d(net_a, [2, 2], scope='pool1')
net_c = slim.repeat(net_b, 2, slim.conv2d, 128, [3, 3], scope='conv2')
return net_a, net_c
Example #25
| Source File: utilities.py From bgsCNN with GNU General Public License v3.0 | 5 votes |
|
def vgg_16(inputs,
variables_collections=None,
scope='vgg_16',
reuse=None):
"""
modification of vgg_16 in TF-slim
see original code in https://github.com/tensorflow/models/blob/master/slim/nets/vgg.py
"""
with tf.variable_scope(scope, 'vgg_16', [inputs]) as sc:
# Collect outputs for conv2d, fully_connected and max_pool2d.
with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d]):
conv1 = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1', biases_initializer=None,
variables_collections=variables_collections, reuse=reuse)
pool1, argmax_1 = tf.nn.max_pool_with_argmax(conv1, [1,2,2,1], [1,2,2,1], padding='VALID', name='pool1')
conv2 = slim.repeat(pool1, 2, slim.conv2d, 128, [3, 3], scope='conv2', biases_initializer=None,
variables_collections=variables_collections, reuse=reuse)
pool2, argmax_2 = tf.nn.max_pool_with_argmax(conv2, [1,2,2,1], [1,2,2,1], padding='VALID', name='pool2')
conv3 = slim.repeat(pool2, 3, slim.conv2d, 256, [3, 3], scope='conv3', biases_initializer=None,
variables_collections=variables_collections, reuse=reuse)
pool3, argmax_3 = tf.nn.max_pool_with_argmax(conv3, [1,2,2,1], [1,2,2,1], padding='VALID', name='pool3')
conv4 = slim.repeat(pool3, 3, slim.conv2d, 512, [3, 3], scope='conv4', biases_initializer=None,
variables_collections=variables_collections, reuse=reuse)
pool4, argmax_4 = tf.nn.max_pool_with_argmax(conv4, [1,2,2,1], [1,2,2,1], padding='VALID', name='pool4')
conv5 = slim.repeat(pool4, 3, slim.conv2d, 512, [3, 3], scope='conv5', biases_initializer=None,
variables_collections=variables_collections, reuse=reuse)
pool5, argmax_5 = tf.nn.max_pool_with_argmax(conv5, [1,2,2,1], [1,2,2,1], padding='VALID', name='pool5')
# return argmax
argmax = (argmax_1, argmax_2, argmax_3, argmax_4, argmax_5)
# return feature maps
features = (conv1, conv2, conv3, conv4, conv5)
return pool5, argmax, features
Example #26
| Source File: archs.py From gaze_redirection with MIT License | 5 votes |
|
def vgg_16(inputs, scope='vgg_16', reuse=False):
""" VGG-16.
Parameters
----------
inputs: tensor.
scope: name of scope.
reuse: reuse the net if True.
Returns
-------
net: tensor, output tensor.
end_points: dict, collection of layers.
"""
with tf.variable_scope(scope, 'vgg_16', [inputs], reuse=reuse) as sc:
end_points_collection = sc.original_name_scope + '_end_points'
# Collect outputs for conv2d, fully_connected and max_pool2d.
with slim.arg_scope(
[slim.conv2d, slim.fully_connected, slim.max_pool2d],
outputs_collections=end_points_collection):
net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3],
scope='conv1')
net = slim.max_pool2d(net, [2, 2], scope='pool1')
net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')
net = slim.max_pool2d(net, [2, 2], scope='pool2')
net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3')
net = slim.max_pool2d(net, [2, 2], scope='pool3')
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv4')
net = slim.max_pool2d(net, [2, 2], scope='pool4')
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv5')
net = slim.max_pool2d(net, [2, 2], scope='pool5')
# Convert end_points_collection into a end_point dict.
end_points = slim.utils.convert_collection_to_dict(
end_points_collection)
return net, end_points
Example #27
| Source File: dfc_vae_resnet.py From TNT with GNU General Public License v3.0 | 5 votes |
|
def decoder(self, latent_var, is_training):
activation_fn = leaky_relu # tf.nn.relu
weight_decay = 0.0
with tf.variable_scope('decoder'):
with slim.arg_scope([slim.batch_norm],
is_training=is_training):
with slim.arg_scope([slim.conv2d, slim.fully_connected],
weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
weights_regularizer=slim.l2_regularizer(weight_decay),
normalizer_fn=slim.batch_norm,
normalizer_params=self.batch_norm_params):
net = slim.fully_connected(latent_var, 4096, activation_fn=None, normalizer_fn=None, scope='Fc_1')
net = tf.reshape(net, [-1,4,4,256], name='Reshape')
net = tf.image.resize_nearest_neighbor(net, size=(8,8), name='Upsample_1')
net = slim.conv2d(net, 128, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_1a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 128, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_1b')
net = tf.image.resize_nearest_neighbor(net, size=(16,16), name='Upsample_2')
net = slim.conv2d(net, 64, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_2a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 64, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_2b')
net = tf.image.resize_nearest_neighbor(net, size=(32,32), name='Upsample_3')
net = slim.conv2d(net, 32, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_3a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 32, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_3b')
net = tf.image.resize_nearest_neighbor(net, size=(64,64), name='Upsample_4')
net = slim.conv2d(net, 3, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_4a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 3, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_4b')
net = slim.conv2d(net, 3, [3, 3], 1, activation_fn=None, scope='Conv2d_4c')
return net
Example #28
| Source File: dfc_vae_resnet.py From TNT with GNU General Public License v3.0 | 5 votes |
|
def encoder(self, images, is_training):
activation_fn = leaky_relu # tf.nn.relu
weight_decay = 0.0
with tf.variable_scope('encoder'):
with slim.arg_scope([slim.batch_norm],
is_training=is_training):
with slim.arg_scope([slim.conv2d, slim.fully_connected],
weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
weights_regularizer=slim.l2_regularizer(weight_decay),
normalizer_fn=slim.batch_norm,
normalizer_params=self.batch_norm_params):
net = images
net = slim.conv2d(net, 32, [4, 4], 2, activation_fn=activation_fn, scope='Conv2d_1a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 32, [4, 4], 1, activation_fn=activation_fn, scope='Conv2d_1b')
net = slim.conv2d(net, 64, [4, 4], 2, activation_fn=activation_fn, scope='Conv2d_2a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 64, [4, 4], 1, activation_fn=activation_fn, scope='Conv2d_2b')
net = slim.conv2d(net, 128, [4, 4], 2, activation_fn=activation_fn, scope='Conv2d_3a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 128, [4, 4], 1, activation_fn=activation_fn, scope='Conv2d_3b')
net = slim.conv2d(net, 256, [4, 4], 2, activation_fn=activation_fn, scope='Conv2d_4a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 256, [4, 4], 1, activation_fn=activation_fn, scope='Conv2d_4b')
net = slim.flatten(net)
fc1 = slim.fully_connected(net, self.latent_variable_dim, activation_fn=None, normalizer_fn=None, scope='Fc_1')
fc2 = slim.fully_connected(net, self.latent_variable_dim, activation_fn=None, normalizer_fn=None, scope='Fc_2')
return fc1, fc2
Example #29
| Source File: KalmanVariationalAutoencoder.py From kvae with MIT License | 4 votes |
|
def decoder(self, a_seq):
""" Convolutional variational decoder to decode latent code to image reconstruction
If config.conv == False it is a MLP VAE. If config.use_vae == False it is a normal decoder
:param a_seq: latent code
:return: x_hat, x_mu, x_var
"""
# Create decoder
if self.config.out_distr == 'bernoulli':
activation_x_mu = tf.nn.sigmoid
else:
activation_x_mu = None
with tf.variable_scope('vae/decoder'):
a = tf.reshape(a_seq, (-1, self.config.dim_a))
if self.config.conv:
dec_upscale = slim.fully_connected(a, int(np.prod(self.enc_shape)), activation_fn=None)
dec_upscale = tf.reshape(dec_upscale, [-1] + self.enc_shape)
dec_hidden = dec_upscale
for filters in reversed(self.num_filters):
dec_hidden = slim.conv2d(dec_hidden, filters * 4, self.config.filter_size,
activation_fn=self.activation_fn)
dec_hidden = subpixel_reshape(dec_hidden, 2)
x_mu = slim.conv2d(dec_hidden, 1, 1, stride=1, activation_fn=activation_x_mu)
x_var = tf.constant(self.config.noise_pixel_var, dtype=tf.float32, shape=())
else:
dec_hidden = slim.repeat(a, self.config.num_layers, slim.fully_connected,
self.config.vae_num_units, self.activation_fn)
x_mu = slim.fully_connected(dec_hidden, self.d1 * self.d2, activation_fn=activation_x_mu)
x_mu = tf.reshape(x_mu, (-1, self.d1, self.d2, 1))
# x_var is not used for bernoulli outputs. Here we fix the output variance of the Gaussian,
# we could also learn it globally for each pixel (as we did in the pendulum experiment) or through a
# neural network.
x_var = tf.constant(self.config.noise_pixel_var, dtype=tf.float32, shape=())
if self.config.out_distr == 'bernoulli':
# For bernoulli we show the probabilities
x_hat = x_mu
else:
x_hat = simple_sample(x_mu, x_var)
return tf.reshape(x_hat, tf.stack((-1, self.ph_steps, self.d1, self.d2))), x_mu, x_var
Example #30
| Source File: KalmanVariationalAutoencoder.py From kvae with MIT License | 4 votes |
|
def alpha(self, inputs, state=None, u=None, buffer=None, reuse=None, init_buffer=False, name='alpha'):
"""The dynamics parameter network alpha for mixing transitions in a state space model.
This function is quite general and supports different architectures (NN, RNN, FIFO queue, learning the inputs)
Args:
inputs: tensor to condition mixing vector on
state: previous state if using RNN network to model alpha
u: pass-through variable if u is given (learn_u=False)
buffer: buffer for the FIFO network (used for fifo_size>1)
reuse: `True` or `None`; if `True`, we go into reuse mode for this scope as
well as all sub-scopes; if `None`, we just inherit the parent scope reuse.
init_buffer: initialize buffer for a_t
name: name of the scope
Returns:
alpha: mixing vector of dimension (batch size, K)
state: new state
u: either inferred u from model or pass-through
buffer: FIFO buffer
"""
# Increase the number of hidden units if we also learn u (learn_u=True)
num_units = self.config.alpha_units * 2 if self.config.learn_u else self.config.alpha_units
# Overwrite input buffer
if init_buffer:
buffer = tf.zeros((tf.shape(inputs)[0], self.config.dim_a, self.config.fifo_size), dtype=tf.float32)
# If K == 1, return inputs
if self.config.K == 1:
return tf.ones([self.config.batch_size, self.config.K]), state, u, buffer
with tf.variable_scope(name, reuse=reuse):
if self.config.alpha_rnn:
rnn_cell = BasicLSTMCell(num_units, reuse=reuse)
output, state = rnn_cell(inputs, state)
else:
# Shift buffer
buffer = tf.concat([buffer[:, :, 1:], tf.expand_dims(inputs, 2)], 2)
output = slim.repeat(
tf.reshape(buffer, (tf.shape(inputs)[0], self.config.dim_a * self.config.fifo_size)),
self.config.alpha_layers, slim.fully_connected, num_units,
get_activation_fn(self.config.alpha_activation), scope='hidden')
# Get Alpha as the first part of the output
alpha = slim.fully_connected(output[:, :self.config.alpha_units],
self.config.K,
activation_fn=tf.nn.softmax,
scope='alpha_var')
if self.config.learn_u:
# Get U as the second half of the output
u = slim.fully_connected(output[:, self.config.alpha_units:],
self.config.dim_u, activation_fn=None, scope='u_var')
return alpha, state, u, buffer
