#! /usr/bin/python
# -*- coding: utf-8 -*-
import tensorflow as tf
from tensorlayer.layers.core import Layer
from tensorlayer.layers.core import LayersConfig
from tensorlayer import logging
from tensorlayer.decorators import deprecated_alias
__all__ = [
'Conv1dLayer',
'Conv2dLayer',
'Conv3dLayer',
]
[docs]class Conv1dLayer(Layer):
"""
The :class:`Conv1dLayer` class is a 1D CNN layer, see `tf.nn.convolution <https://www.tensorflow.org/api_docs/python/tf/nn/convolution>`__.
Parameters
----------
prev_layer : :class:`Layer`
Previous layer.
act : activation function
The activation function of this layer.
shape : tuple of int
The shape of the filters: (filter_length, in_channels, out_channels).
stride : int
The number of entries by which the filter is moved right at a step.
dilation_rate : int
Filter up-sampling/input down-sampling rate.
padding : str
The padding algorithm type: "SAME" or "VALID".
data_format : str
Default is 'NWC' as it is a 1D CNN.
W_init : initializer
The initializer for the weight matrix.
b_init : initializer or None
The initializer for the bias vector. If None, skip biases.
W_init_args : dictionary
The arguments for the weight matrix initializer.
b_init_args : dictionary
The arguments for the bias vector initializer.
name : str
A unique layer name
"""
@deprecated_alias(layer='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release
def __init__(
self,
prev_layer,
act=None,
shape=(5, 1, 5),
stride=1,
dilation_rate=1,
padding='SAME',
data_format='NWC',
W_init=tf.truncated_normal_initializer(stddev=0.02),
b_init=tf.constant_initializer(value=0.0),
W_init_args=None,
b_init_args=None,
name='cnn1d',
):
super(Conv1dLayer, self
).__init__(prev_layer=prev_layer, act=act, W_init_args=W_init_args, b_init_args=b_init_args, name=name)
logging.info(
"Conv1dLayer %s: shape: %s stride: %s pad: %s act: %s" % (
self.name, str(shape), str(stride), padding,
self.act.__name__ if self.act is not None else 'No Activation'
)
)
with tf.variable_scope(name):
W = tf.get_variable(
name='W_conv1d', shape=shape, initializer=W_init, dtype=LayersConfig.tf_dtype, **self.W_init_args
)
self.outputs = tf.nn.convolution(
self.inputs, W, strides=(stride, ), padding=padding, dilation_rate=(dilation_rate, )
)
if b_init:
b = tf.get_variable(
name='b_conv1d', shape=(shape[-1]), initializer=b_init, dtype=LayersConfig.tf_dtype,
**self.b_init_args
)
self.outputs = tf.nn.bias_add(self.outputs, b, name='bias_add')
self.outputs = self._apply_activation(self.outputs)
self._add_layers(self.outputs)
if b_init:
self._add_params([W, b])
else:
self._add_params(W)
[docs]class Conv2dLayer(Layer):
"""
The :class:`Conv2dLayer` class is a 2D CNN layer, see `tf.nn.conv2d <https://www.tensorflow.org/versions/master/api_docs/python/nn.html#conv2d>`__.
Parameters
----------
prev_layer : :class:`Layer`
Previous layer.
act : activation function
The activation function of this layer.
shape : tuple of int
The shape of the filters: (filter_height, filter_width, in_channels, out_channels).
strides : tuple of int
The sliding window strides of corresponding input dimensions.
It must be in the same order as the ``shape`` parameter.
padding : str
The padding algorithm type: "SAME" or "VALID".
W_init : initializer
The initializer for the weight matrix.
b_init : initializer or None
The initializer for the bias vector. If None, skip biases.
W_init_args : dictionary
The arguments for the weight matrix initializer.
b_init_args : dictionary
The arguments for the bias vector initializer.
use_cudnn_on_gpu : bool
Default is False.
data_format : str
"NHWC" or "NCHW", default is "NHWC".
name : str
A unique layer name.
Notes
-----
- shape = [h, w, the number of output channel of previous layer, the number of output channels]
- the number of output channel of a layer is its last dimension.
Examples
--------
With TensorLayer
>>> x = tf.placeholder(tf.float32, shape=(None, 28, 28, 1))
>>> net = tl.layers.InputLayer(x, name='input_layer')
>>> net = tl.layers.Conv2dLayer(net,
... act = tf.nn.relu,
... shape = (5, 5, 1, 32), # 32 features for each 5x5 patch
... strides = (1, 1, 1, 1),
... padding='SAME',
... W_init=tf.truncated_normal_initializer(stddev=5e-2),
... b_init = tf.constant_initializer(value=0.0),
... name ='cnn_layer1') # output: (?, 28, 28, 32)
>>> net = tl.layers.PoolLayer(net,
... ksize=(1, 2, 2, 1),
... strides=(1, 2, 2, 1),
... padding='SAME',
... pool = tf.nn.max_pool,
... name ='pool_layer1',) # output: (?, 14, 14, 32)
Without TensorLayer, you can implement 2D convolution as follow.
>>> W = tf.Variable(W_init(shape=[5, 5, 1, 32], ), name='W_conv')
>>> b = tf.Variable(b_init(shape=[32], ), name='b_conv')
>>> outputs = tf.nn.relu( tf.nn.conv2d(inputs, W,
... strides=[1, 1, 1, 1],
... padding='SAME') + b )
"""
@deprecated_alias(layer='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release
def __init__(
self,
prev_layer,
act=None,
shape=(5, 5, 1, 100),
strides=(1, 1, 1, 1),
padding='SAME',
W_init=tf.truncated_normal_initializer(stddev=0.02),
b_init=tf.constant_initializer(value=0.0),
W_init_args=None,
b_init_args=None,
use_cudnn_on_gpu=None,
data_format=None,
name='cnn_layer',
):
super(Conv2dLayer, self
).__init__(prev_layer=prev_layer, act=act, W_init_args=W_init_args, b_init_args=b_init_args, name=name)
logging.info(
"Conv2dLayer %s: shape: %s strides: %s pad: %s act: %s" % (
self.name, str(shape), str(strides), padding,
self.act.__name__ if self.act is not None else 'No Activation'
)
)
with tf.variable_scope(name):
W = tf.get_variable(
name='W_conv2d', shape=shape, initializer=W_init, dtype=LayersConfig.tf_dtype, **self.W_init_args
)
self.outputs = tf.nn.conv2d(
self.inputs, W, strides=strides, padding=padding, use_cudnn_on_gpu=use_cudnn_on_gpu,
data_format=data_format
)
if b_init:
b = tf.get_variable(
name='b_conv2d', shape=(shape[-1]), initializer=b_init, dtype=LayersConfig.tf_dtype,
**self.b_init_args
)
self.outputs = tf.nn.bias_add(self.outputs, b, name='bias_add')
self.outputs = self._apply_activation(self.outputs)
self._add_layers(self.outputs)
if b_init:
self._add_params([W, b])
else:
self._add_params(W)
[docs]class Conv3dLayer(Layer):
"""
The :class:`Conv3dLayer` class is a 3D CNN layer, see `tf.nn.conv3d <https://www.tensorflow.org/versions/master/api_docs/python/nn.html#conv3d>`__.
Parameters
----------
prev_layer : :class:`Layer`
Previous layer.
shape : tuple of int
Shape of the filters: (filter_depth, filter_height, filter_width, in_channels, out_channels).
strides : tuple of int
The sliding window strides for corresponding input dimensions.
Must be in the same order as the shape dimension.
padding : str
The padding algorithm type: "SAME" or "VALID".
act : activation function
The activation function of this layer.
W_init : initializer
The initializer for the weight matrix.
b_init : initializer or None
The initializer for the bias vector. If None, skip biases.
W_init_args : dictionary
The arguments for the weight matrix initializer.
b_init_args : dictionary
The arguments for the bias vector initializer.
name : str
A unique layer name.
Examples
---------
>>> x = tf.placeholder(tf.float32, (None, 100, 100, 100, 3))
>>> n = tl.layers.InputLayer(x, name='in3')
>>> n = tl.layers.Conv3dLayer(n, shape=(2, 2, 2, 3, 32), strides=(1, 2, 2, 2, 1))
[None, 50, 50, 50, 32]
"""
@deprecated_alias(layer='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release
def __init__(
self,
prev_layer,
shape=(2, 2, 2, 3, 32),
strides=(1, 2, 2, 2, 1),
padding='SAME',
act=None,
W_init=tf.truncated_normal_initializer(stddev=0.02),
b_init=tf.constant_initializer(value=0.0),
W_init_args=None,
b_init_args=None,
name='cnn3d_layer',
):
super(Conv3dLayer, self
).__init__(prev_layer=prev_layer, act=act, W_init_args=W_init_args, b_init_args=b_init_args, name=name)
logging.info(
"Conv3dLayer %s: shape: %s strides: %s pad: %s act: %s" % (
self.name, str(shape), str(strides), padding,
self.act.__name__ if self.act is not None else 'No Activation'
)
)
with tf.variable_scope(name):
W = tf.get_variable(
name='W_conv3d', shape=shape, initializer=W_init, dtype=LayersConfig.tf_dtype, **self.W_init_args
)
self.outputs = tf.nn.conv3d(self.inputs, W, strides=strides, padding=padding, name=None)
if b_init:
b = tf.get_variable(
name='b_conv3d', shape=(shape[-1]), initializer=b_init, dtype=LayersConfig.tf_dtype,
**self.b_init_args
)
self.outputs = tf.nn.bias_add(self.outputs, b, name='bias_add')
self.outputs = self._apply_activation(self.outputs)
self._add_layers(self.outputs)
if b_init:
self._add_params([W, b])
else:
self._add_params([W])