#! /usr/bin/python
# -*- coding: utf-8 -*-
import tensorflow as tf
from tensorlayer.layers.core import Layer
from tensorlayer.layers.utils import get_collection_trainable
from tensorlayer import logging
from tensorlayer.decorators import deprecated_alias
__all__ = [
'Conv1d',
'Conv2d',
]
[docs]class Conv1d(Layer):
"""Simplified version of :class:`Conv1dLayer`.
Parameters
----------
prev_layer : :class:`Layer`
Previous layer
n_filter : int
The number of filters
filter_size : int
The filter size
stride : int
The stride step
dilation_rate : int
Specifying the dilation rate to use for dilated convolution.
act : activation function
The function that is applied to the layer activations
padding : str
The padding algorithm type: "SAME" or "VALID".
data_format : str
channels_last 'NWC' (default) or channels_first.
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 (deprecated).
b_init_args : dictionary
The arguments for the bias vector initializer (deprecated).
name : str
A unique layer name
Examples
---------
>>> x = tf.placeholder(tf.float32, (batch_size, width))
>>> y_ = tf.placeholder(tf.int64, shape=(batch_size,))
>>> n = InputLayer(x, name='in')
>>> n = ReshapeLayer(n, (-1, width, 1), name='rs')
>>> n = Conv1d(n, 64, 3, 1, act=tf.nn.relu, name='c1')
>>> n = MaxPool1d(n, 2, 2, padding='valid', name='m1')
>>> n = Conv1d(n, 128, 3, 1, act=tf.nn.relu, name='c2')
>>> n = MaxPool1d(n, 2, 2, padding='valid', name='m2')
>>> n = Conv1d(n, 128, 3, 1, act=tf.nn.relu, name='c3')
>>> n = MaxPool1d(n, 2, 2, padding='valid', name='m3')
>>> n = FlattenLayer(n, name='f')
>>> n = DenseLayer(n, 500, tf.nn.relu, name='d1')
>>> n = DenseLayer(n, 100, tf.nn.relu, name='d2')
>>> n = DenseLayer(n, 2, None, name='o')
"""
@deprecated_alias(layer='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release
def __init__(
self, prev_layer, n_filter=32, filter_size=5, stride=1, dilation_rate=1, act=None, padding='SAME',
data_format="channels_last", 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='conv1d'
):
super(Conv1d, self
).__init__(prev_layer=prev_layer, act=act, W_init_args=W_init_args, b_init_args=b_init_args, name=name)
logging.info(
"Conv1d %s: n_filter: %d filter_size: %s stride: %d pad: %s act: %s dilation_rate: %d" % (
self.name, n_filter, filter_size, stride, padding,
self.act.__name__ if self.act is not None else 'No Activation', dilation_rate
)
)
_conv1d = tf.layers.Conv1D(
filters=n_filter, kernel_size=filter_size, strides=stride, padding=padding, data_format=data_format,
dilation_rate=dilation_rate, activation=self.act, use_bias=(True if b_init else False),
kernel_initializer=W_init, bias_initializer=b_init, name=name
)
# _conv1d.dtype = LayersConfig.tf_dtype # unsupport, it will use the same dtype of inputs
self.outputs = _conv1d(self.inputs)
# new_variables = _conv1d.weights # new_variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)
# new_variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=self.name) #vs.name)
new_variables = get_collection_trainable(self.name)
self._add_layers(self.outputs)
self._add_params(new_variables)
[docs]class Conv2d(Layer):
"""Simplified version of :class:`Conv2dLayer`.
Parameters
----------
prev_layer : :class:`Layer`
Previous layer.
n_filter : int
The number of filters.
filter_size : tuple of int
The filter size (height, width).
strides : tuple of int
The sliding window strides of corresponding input dimensions.
It must be in the same order as the ``shape`` parameter.
act : activation function
The activation function of this layer.
padding : str
The padding algorithm type: "SAME" or "VALID".
data_format : str
"channels_last" (NHWC, default) or "channels_first" (NCHW).
W_init : initializer
The initializer for the the weight matrix.
b_init : initializer or None
The initializer for the the bias vector. If None, skip biases.
W_init_args : dictionary
The arguments for the weight matrix initializer (for TF < 1.5).
b_init_args : dictionary
The arguments for the bias vector initializer (for TF < 1.5).
use_cudnn_on_gpu : bool
Default is False (for TF < 1.5).
name : str
A unique layer name.
Returns
-------
:class:`Layer`
A :class:`Conv2dLayer` object.
Examples
--------
>>> x = tf.placeholder(tf.float32, shape=(None, 28, 28, 1))
>>> net = InputLayer(x, name='inputs')
>>> net = Conv2d(net, 64, (3, 3), act=tf.nn.relu, name='conv1_1')
>>> net = Conv2d(net, 64, (3, 3), act=tf.nn.relu, name='conv1_2')
>>> net = MaxPool2d(net, (2, 2), name='pool1')
>>> net = Conv2d(net, 128, (3, 3), act=tf.nn.relu, name='conv2_1')
>>> net = Conv2d(net, 128, (3, 3), act=tf.nn.relu, name='conv2_2')
>>> net = MaxPool2d(net, (2, 2), name='pool2')
"""
@deprecated_alias(layer='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release
def __init__(
self,
prev_layer,
n_filter=32,
filter_size=(3, 3),
strides=(1, 1),
act=None,
padding='SAME',
data_format='channels_last',
dilation_rate=(1, 1),
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,
name='conv2d',
):
# if len(strides) != 2:
# raise ValueError("len(strides) should be 2, Conv2d and Conv2dLayer are different.")
# try:
# pre_channel = int(layer.outputs.get_shape()[-1])
# except Exception: # if pre_channel is ?, it happens when using Spatial Transformer Net
# pre_channel = 1
# logging.info("[warnings] unknow input channels, set to 1")
super(Conv2d, self
).__init__(prev_layer=prev_layer, act=act, W_init_args=W_init_args, b_init_args=b_init_args, name=name)
logging.info(
"Conv2d %s: n_filter: %d filter_size: %s strides: %s pad: %s act: %s" % (
self.name, n_filter, str(filter_size), str(strides), padding,
self.act.__name__ if self.act is not None else 'No Activation'
)
)
# with tf.variable_scope(name) as vs:
conv2d = tf.layers.Conv2D(
# inputs=self.inputs,
filters=n_filter,
kernel_size=filter_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
activation=self.act,
use_bias=(False if b_init is None else True),
kernel_initializer=W_init, # None,
bias_initializer=b_init, # f.zeros_initializer(),
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
trainable=True,
name=name,
# reuse=None,
)
self.outputs = conv2d(self.inputs) # must put before ``new_variables``
# new_variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=self.name) #vs.name)
new_variables = get_collection_trainable(self.name)
# new_variables = []
# for p in tf.trainable_variables():
# # print(p.name.rpartition('/')[0], self.name)
# if p.name.rpartition('/')[0] == self.name:
# new_variables.append(p)
# exit()
# TF_GRAPHKEYS_VARIABLES TF_GRAPHKEYS_VARIABLES
# print(self.name, name)
# print(tf.trainable_variables())#tf.GraphKeys.TRAINABLE_VARIABLES)
# print(new_variables)
# print(conv2d.weights)
self._add_layers(self.outputs)
self._add_params(new_variables) # conv2d.weights)