#! /usr/bin/python
# -*- coding: utf-8 -*-
import numpy as np
import tensorflow as tf
import tensorlayer as tl
from tensorlayer import logging
from tensorlayer.decorators import deprecated_alias
from tensorlayer.layers.core import Layer
# from tensorlayer.layers.core import LayersConfig
__all__ = [
'Dense',
]
[docs]class Dense(Layer):
"""The :class:`Dense` class is a fully connected layer.
Parameters
----------
n_units : int
The number of units of this layer.
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.
in_channels: int
The number of channels of the previous layer.
If None, it will be automatically detected when the layer is forwarded for the first time.
name : None or str
A unique layer name. If None, a unique name will be automatically generated.
Examples
--------
With TensorLayer
>>> net = tl.layers.Input([100, 50], name='input')
>>> dense = tl.layers.Dense(n_units=800, act=tf.nn.relu, in_channels=50, name='dense_1')
>>> print(dense)
Dense(n_units=800, relu, in_channels='50', name='dense_1')
>>> tensor = tl.layers.Dense(n_units=800, act=tf.nn.relu, name='dense_2')(net)
>>> print(tensor)
tf.Tensor([...], shape=(100, 800), dtype=float32)
Notes
-----
If the layer input has more than two axes, it needs to be flatten by using :class:`Flatten`.
"""
def __init__(
self,
n_units,
act=None,
W_init=tl.initializers.truncated_normal(stddev=0.05),
b_init=tl.initializers.constant(value=0.0),
in_channels=None,
name=None, # 'dense',
):
super(Dense, self).__init__(name, act=act)
self.n_units = n_units
self.W_init = W_init
self.b_init = b_init
self.in_channels = in_channels
if self.in_channels is not None:
self.build(self.in_channels)
self._built = True
logging.info(
"Dense %s: %d %s" %
(self.name, self.n_units, self.act.__name__ if self.act is not None else 'No Activation')
)
def __repr__(self):
actstr = self.act.__name__ if self.act is not None else 'No Activation'
s = ('{classname}(n_units={n_units}, ' + actstr)
if self.in_channels is not None:
s += ', in_channels=\'{in_channels}\''
if self.name is not None:
s += ', name=\'{name}\''
s += ')'
return s.format(classname=self.__class__.__name__, **self.__dict__)
def build(self, inputs_shape):
if self.in_channels is None and len(inputs_shape) != 2:
raise AssertionError("The input dimension must be rank 2, please reshape or flatten it")
if self.in_channels:
shape = [self.in_channels, self.n_units]
else:
self.in_channels = inputs_shape[1]
shape = [inputs_shape[1], self.n_units]
self.W = self._get_weights("weights", shape=tuple(shape), init=self.W_init)
if self.b_init:
self.b = self._get_weights("biases", shape=(self.n_units, ), init=self.b_init)
# @tf.function
def forward(self, inputs):
z = tf.matmul(inputs, self.W)
if self.b_init:
z = tf.add(z, self.b)
if self.act:
z = self.act(z)
return z