本文整理汇总了Python中tensorflow.contrib.slim.repeat方法的典型用法代码示例。如果您正苦于以下问题：Python slim.repeat方法的具体用法？Python slim.repeat怎么用？Python slim.repeat使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.contrib.slim的用法示例。
在下文中一共展示了slim.repeat方法的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _image_to_head
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import repeat [as 别名]
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 示例2: _image_to_head
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import repeat [as 别名]
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 示例3: _image_to_head
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import repeat [as 别名]
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 示例4: _image_to_head
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import repeat [as 别名]
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 示例5: vgg16
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import repeat [as 别名]
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 示例6: get_repeat
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import repeat [as 别名]
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 示例7: encoder
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import repeat [as 别名]
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 示例8: encoder
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import repeat [as 别名]
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 示例9: decoder
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import repeat [as 别名]
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 示例10: vgg_16
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import repeat [as 别名]
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 示例11: vgg_16
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import repeat [as 别名]
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 示例12: vgg_16
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import repeat [as 别名]
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 示例13: _image_to_rpn_single
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import repeat [as 别名]
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 示例14: repeat
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import repeat [as 别名]
def repeat(inputs, repetitions, layer, layer_dict={}, **kargv):
outputs = slim.repeat(inputs, repetitions, layer, **kargv)
_update_dict(layer_dict, kargv['scope'], outputs)
return outputs 示例15: build_head
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import repeat [as 别名]
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