本文整理汇总了Python中tensorflow.contrib.layers.xavier_initializer_conv2d方法的典型用法代码示例。如果您正苦于以下问题:Python layers.xavier_initializer_conv2d方法的具体用法?Python layers.xavier_initializer_conv2d怎么用?Python layers.xavier_initializer_conv2d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.layers
的用法示例。
在下文中一共展示了layers.xavier_initializer_conv2d方法的12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: conv2d_transpose_layer
# 需要导入模块: from tensorflow.contrib import layers [as 别名]
# 或者: from tensorflow.contrib.layers import xavier_initializer_conv2d [as 别名]
def conv2d_transpose_layer(inputs, filters, activation, name):
"""
A simple de-convolution layer.
:param inputs: batch of inputs.
:param filters: number of output filters.
:param activation: activation function to use.
:param name: name used to scope this operation.
:return: batch of outputs.
"""
return tf.layers.conv2d_transpose(
inputs=inputs,
filters=filters,
kernel_size=(4, 4),
strides=(2, 2),
padding='same',
activation=activation,
data_format='channels_last',
use_bias=False,
kernel_initializer=xavier_initializer_conv2d(uniform=False),
name=name,
reuse=tf.AUTO_REUSE)
示例2: upsample_conv
# 需要导入模块: from tensorflow.contrib import layers [as 别名]
# 或者: from tensorflow.contrib.layers import xavier_initializer_conv2d [as 别名]
def upsample_conv(inputs, num_outputs, kernel_size, sn, activation_fn=None,
normalizer_fn=None, normalizer_params=None,
weights_regularizer=None,
weights_initializer=ly.xavier_initializer_conv2d(),
biases_initializer=tf.zeros_initializer(),
data_format='NCHW'):
output = inputs
output = tf.concat([output, output, output, output], axis=1 if data_format == 'NCHW' else 3)
if data_format == 'NCHW':
output = tf.transpose(output, [0, 2, 3, 1])
output = tf.depth_to_space(output, 2)
if data_format == 'NCHW':
output = tf.transpose(output, [0, 3, 1, 2])
output = conv2d(output, num_outputs, kernel_size, sn=sn, activation_fn=activation_fn,
normalizer_fn=normalizer_fn, normalizer_params=normalizer_params,
weights_regularizer=weights_regularizer, weights_initializer=weights_initializer,
biases_initializer=biases_initializer,
data_format=data_format)
return output
示例3: conv_op
# 需要导入模块: from tensorflow.contrib import layers [as 别名]
# 或者: from tensorflow.contrib.layers import xavier_initializer_conv2d [as 别名]
def conv_op(input_op, filter_size, channel_out, step, name):
channel_in = input_op.get_shape()[-1].value
with tf.name_scope(name) as scope:
weights = tf.get_variable(shape=[filter_size, filter_size, channel_in, channel_out], dtype=tf.float32,
initializer=xavier_initializer_conv2d(), name=scope + 'weights')
biases = tf.Variable(tf.constant(value=0.0, shape=[channel_out], dtype=tf.float32),
trainable=True, name='biases')
conv = tf.nn.conv2d(input_op, weights, strides=[1, step, step, 1], padding='SAME') + biases
conv = tf.nn.relu(conv, name=scope)
return conv
示例4: full_connection
# 需要导入模块: from tensorflow.contrib import layers [as 别名]
# 或者: from tensorflow.contrib.layers import xavier_initializer_conv2d [as 别名]
def full_connection(input_op, channel_out, name):
channel_in = input_op.get_shape()[-1].value
with tf.name_scope(name) as scope:
weight = tf.get_variable(shape=[channel_in, channel_out], dtype=tf.float32,
initializer=xavier_initializer_conv2d(), name=scope + 'weight')
bias = tf.Variable(tf.constant(value=0.0, shape=[channel_out], dtype=tf.float32), name='bias')
fc = tf.nn.relu_layer(input_op, weight, bias, name=scope)
return fc
示例5: conv2d_pool_block
# 需要导入模块: from tensorflow.contrib import layers [as 别名]
# 或者: from tensorflow.contrib.layers import xavier_initializer_conv2d [as 别名]
def conv2d_pool_block(inputs, use_batch_norm, dropout_keep_prob, pool_padding, name):
"""
A macro function that implements the following in sequence:
- conv2d
- batch_norm
- relu activation
- dropout
- max_pool
:param inputs: batch of feature maps.
:param use_batch_norm: whether to use batch normalization or not.
:param dropout_keep_prob: keep probability parameter for dropout.
:param pool_padding: type of padding to use on the pooling operation.
:param name: first part of the name used to scope this sequence of operations.
:return: the processed batch of feature maps.
"""
h = tf.layers.conv2d(
inputs=inputs,
strides=(1, 1),
filters=64,
kernel_size=[3, 3],
padding="same",
kernel_initializer=xavier_initializer_conv2d(uniform=False),
use_bias=False,
name=(name + '_conv2d'),
reuse=tf.AUTO_REUSE)
if use_batch_norm:
h = tf.contrib.layers.batch_norm(
inputs=h,
epsilon=1e-5,
scope=(name + '_batch_norm'),
reuse=tf.AUTO_REUSE)
h = tf.nn.relu(features=h, name=(name + '_batch_relu'))
h = tf.nn.dropout(x=h, keep_prob=dropout_keep_prob, name=(name + '_dropout'))
h = tf.layers.max_pooling2d(inputs=h, pool_size=[2, 2], strides=2, padding=pool_padding, name=(name + '_pool'))
return h
示例6: conv_block
# 需要导入模块: from tensorflow.contrib import layers [as 别名]
# 或者: from tensorflow.contrib.layers import xavier_initializer_conv2d [as 别名]
def conv_block(input_tensor, kernel, filters, name, strides=(2, 2)):
""" Function to create block of ResNet network which include
three convolution layers and one skip-connection layer.
Args:
input_tensor: input tensorflow layer
kernel: tuple of kernel size in convolution layer
filters: list of nums filters in convolution layers
name: name of block
strides: typle of strides in convolution layer
Output:
x: Block output layer """
filters1, filters2, filters3 = filters
x = tf.layers.conv2d(input_tensor, filters1, (1, 1), strides, name='convfir' + name, activation=tf.nn.relu,\
kernel_initializer=xavier())
x = tf.layers.conv2d(x, filters2, kernel, name='convsec' + name, activation=tf.nn.relu, padding='SAME',\
kernel_initializer=xavier())
x = tf.layers.conv2d(x, filters3, (1, 1), name='convthr' + name,\
kernel_initializer=xavier())
shortcut = tf.layers.conv2d(input_tensor, filters3, (1, 1), strides, name='short' + name, \
kernel_initializer=xavier())
x = tf.concat([x, shortcut], axis=1)
x = tf.nn.relu(x)
return x
示例7: identity_block
# 需要导入模块: from tensorflow.contrib import layers [as 别名]
# 或者: from tensorflow.contrib.layers import xavier_initializer_conv2d [as 别名]
def identity_block(input_tensor, kernel, filters, name):
""" Function to create block of ResNet network which include
three convolution layers.
Args:
input_tensor: input tensorflow layer.
kernel: tuple of kernel size in convolution layer.
filters: list of nums filters in convolution layers.
name: name of block.
Output:
x: Block output layer """
filters1, filters2, filters3 = filters
x = tf.layers.conv2d(input_tensor, filters1, (1, 1), name='convfir' + name, activation=tf.nn.relu,\
kernel_initializer=xavier())
x = tf.layers.conv2d(x, filters2, kernel, name='convsec' + name, activation=tf.nn.relu, padding='SAME',\
kernel_initializer=xavier())
x = tf.layers.conv2d(x, filters3, (1, 1), name='convthr' + name,\
kernel_initializer=xavier())
x = tf.concat([x, input_tensor], axis=1)
x = tf.nn.relu(x)
return x
示例8: conv_mean_pool
# 需要导入模块: from tensorflow.contrib import layers [as 别名]
# 或者: from tensorflow.contrib.layers import xavier_initializer_conv2d [as 别名]
def conv_mean_pool(inputs, num_outputs, kernel_size, sn, rate=1,
activation_fn=None,
normalizer_fn=None, normalizer_params=None,
weights_regularizer=None,
weights_initializer=ly.xavier_initializer_conv2d(),
biases_initializer=tf.zeros_initializer(),
data_format='NCHW'):
output = conv2d(inputs, num_outputs, kernel_size, sn=sn, rate=rate, activation_fn=activation_fn,
normalizer_fn=normalizer_fn, normalizer_params=normalizer_params,
weights_regularizer=weights_regularizer, weights_initializer=weights_initializer,
biases_initializer=biases_initializer,
data_format=data_format)
output = tf.add_n(
[output[:, :, ::2, ::2], output[:, :, 1::2, ::2], output[:, :, ::2, 1::2], output[:, :, 1::2, 1::2]]) / 4.
return output
示例9: mean_pool_conv
# 需要导入模块: from tensorflow.contrib import layers [as 别名]
# 或者: from tensorflow.contrib.layers import xavier_initializer_conv2d [as 别名]
def mean_pool_conv(inputs, num_outputs, kernel_size, sn, rate=1,
activation_fn=None,
normalizer_fn=None, normalizer_params=None,
weights_regularizer=None,
weights_initializer=ly.xavier_initializer_conv2d(),
data_format='NCHW'):
output = inputs
output = tf.add_n(
[output[:, :, ::2, ::2], output[:, :, 1::2, ::2], output[:, :, ::2, 1::2], output[:, :, 1::2, 1::2]]) / 4.
output = conv2d(output, num_outputs, kernel_size, sn=sn, rate=rate, activation_fn=activation_fn,
normalizer_fn=normalizer_fn, normalizer_params=normalizer_params,
weights_regularizer=weights_regularizer, weights_initializer=weights_initializer,
data_format=data_format)
return output
示例10: upsample_conv_bilinear
# 需要导入模块: from tensorflow.contrib import layers [as 别名]
# 或者: from tensorflow.contrib.layers import xavier_initializer_conv2d [as 别名]
def upsample_conv_bilinear(inputs, num_outputs, kernel_size, sn, activation_fn=None,
normalizer_fn=None, normalizer_params=None,
weights_regularizer=None,
weights_initializer=ly.xavier_initializer_conv2d(),
data_format='NCHW'):
output = inputs
if data_format == 'NCHW':
output = tf.transpose(output, [0, 2, 3, 1])
batch_size, height, width, channel = [int(i) for i in output.get_shape()]
# output = tf.Print(output, [tf.reduce_min(output), tf.reduce_max(output)], message='before')
output = tf.image.resize_bilinear(output, [height * 2, width * 2])
# output = tf.Print(output, [tf.reduce_min(output), tf.reduce_max(output)], message='after')
slice0 = output[:, :, :, 0::4]
slice1 = output[:, :, :, 1::4]
slice2 = output[:, :, :, 2::4]
slice3 = output[:, :, :, 3::4]
output = slice0 + slice1 + slice2 + slice3
if data_format == 'NCHW':
output = tf.transpose(output, [0, 3, 1, 2])
output = conv2d(output, num_outputs, kernel_size, sn=sn, activation_fn=activation_fn,
normalizer_fn=normalizer_fn, normalizer_params=normalizer_params,
weights_regularizer=weights_regularizer, weights_initializer=weights_initializer,
data_format=data_format)
return output
# Sigmoid Gates
示例11: resnet
# 需要导入模块: from tensorflow.contrib import layers [as 别名]
# 或者: from tensorflow.contrib.layers import xavier_initializer_conv2d [as 别名]
def resnet(self):
""" Simple implementation of Resnet.
Args:
self
Outputs:
Method return list with len = 2 and some params:
[0][0]: indices - Placeholder which takes batch indices.
[0][1]: all_data - Placeholder which takes all images.
[0][2]; all_lables - Placeholder for lables.
[0][3]: loss - Value of loss function.
[0][4]: train - List of train optimizers.
[0][5]: prob - softmax output, need to prediction.
[1][0]: accuracy - Current accuracy
[1][1]: session - tf session """
with tf.Graph().as_default():
indices = tf.placeholder(tf.int32, shape=[None, 1])
all_data = tf.placeholder(tf.float32, shape=[50000, 28, 28])
input_batch = tf.gather_nd(all_data, indices)
x1_to_tens = tf.reshape(input_batch, shape=[-1, 28, 28, 1])
net1 = tf.layers.conv2d(x1_to_tens, 32, (7, 7), strides=(2, 2), padding='SAME', activation=tf.nn.relu, \
kernel_initializer=xavier(), name='11')
net1 = tf.layers.max_pooling2d(net1, (2, 2), (2, 2))
net1 = conv_block(net1, 3, [32, 32, 128], name='22', strides=(1, 1))
net1 = identity_block(net1, 3, [32, 32, 128], name='33')
net1 = conv_block(net1, 3, [64, 64, 256], name='53', strides=(1, 1))
net1 = identity_block(net1, 3, [64, 64, 256], name='63')
net1 = tf.layers.average_pooling2d(net1, (7, 7), strides=(1, 1))
net1 = tf.contrib.layers.flatten(net1)
with tf.variable_scope('dense3'):
net1 = tf.layers.dense(net1, 10, kernel_initializer=tf.contrib.layers.xavier_initializer())
prob1 = tf.nn.softmax(net1)
all_lables = tf.placeholder(tf.float32, [None, 10])
y = tf.gather_nd(all_lables, indices)
loss1 = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=net1, labels=y), name='loss3')
train1 = tf.train.MomentumOptimizer(0.03, 0.8, use_nesterov=True).minimize(loss1)
lables_hat1 = tf.cast(tf.argmax(net1, axis=1), tf.float32, name='lables_3at')
lables1 = tf.cast(tf.argmax(y, axis=1), tf.float32, name='labl3es')
accuracy1 = tf.reduce_mean(tf.cast(tf.equal(lables_hat1, lables1), tf.float32, name='a3ccuracy'))
session = tf.Session()
session.run(tf.global_variables_initializer())
return [[indices, all_data, all_lables, loss1, train1, prob1], [accuracy1, session]]
示例12: mru_deconv
# 需要导入模块: from tensorflow.contrib import layers [as 别名]
# 或者: from tensorflow.contrib.layers import xavier_initializer_conv2d [as 别名]
def mru_deconv(x, ht, filter_depth, sn, stride=2, num_blocks=2,
last_unit=False,
activation_fn=tf.nn.relu,
normalizer_fn=None,
normalizer_params=None,
weights_initializer=ly.xavier_initializer_conv2d(),
weight_decay_rate=1e-5,
unit_num=0, data_format='NCHW'):
assert len(ht) == num_blocks
def norm_activ(tensor_in):
if normalizer_fn is not None:
_normalizer_params = normalizer_params or {}
tensor_normed = normalizer_fn(tensor_in, **_normalizer_params)
else:
tensor_normed = tf.identity(tensor_in)
if activation_fn is not None:
tensor_normed = activation_fn(tensor_normed)
return tensor_normed
# cell_block = mru_deconv_block
cell_block = mru_deconv_block_v2
hts_new = []
inp = x
with tf.variable_scope('mru_deconv_unit_t_%d_layer_0' % unit_num):
ht_new = cell_block(inp, ht[0], filter_depth, sn=sn, stride=stride,
activation_fn=activation_fn,
normalizer_fn=normalizer_fn,
normalizer_params=normalizer_params,
weights_initializer=weights_initializer,
data_format=data_format,
weight_decay_rate=weight_decay_rate)
hts_new.append(ht_new)
inp = ht_new
for i in range(1, num_blocks):
if stride == 2:
ht[i] = upsample(ht[i], data_format=data_format)
with tf.variable_scope('mru_deconv_unit_t_%d_layer_%d' % (unit_num, i)):
ht_new = cell_block(inp, ht[i], filter_depth, sn=sn, stride=1,
activation_fn=activation_fn,
normalizer_fn=normalizer_fn,
normalizer_params=normalizer_params,
weights_initializer=weights_initializer,
data_format=data_format,
weight_decay_rate=weight_decay_rate)
hts_new.append(ht_new)
inp = ht_new
# if last_unit:
# with tf.variable_scope('mru_deconv_unit_last_norm'):
# hts_new[-1] = norm_activ(hts_new[-1])
return hts_new