forked from TensorLayer/tensorlayer3
122 lines
3.8 KiB
Python
122 lines
3.8 KiB
Python
#! /usr/bin/python
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# -*- coding: utf-8 -*-
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import tensorlayer as tl
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from tensorlayer import logging
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from tensorlayer.layers.core import Module
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__all__ = [
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'Dense',
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]
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class Dense(Module):
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"""The :class:`Dense` class is a fully connected layer.
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Parameters
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----------
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n_units : int
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The number of units of this layer.
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act : activation function
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The activation function of this layer.
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W_init : initializer
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The initializer for the weight matrix.
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b_init : initializer or None
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The initializer for the bias vector. If None, skip biases.
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in_channels: int
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The number of channels of the previous layer.
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If None, it will be automatically detected when the layer is forwarded for the first time.
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name : None or str
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A unique layer name. If None, a unique name will be automatically generated.
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Examples
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--------
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With TensorLayer
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>>> net = tl.layers.Input([100, 50], name='input')
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>>> dense = tl.layers.Dense(n_units=800, act=tl.ReLU, in_channels=50, name='dense_1')
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>>> print(dense)
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Dense(n_units=800, relu, in_channels='50', name='dense_1')
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>>> tensor = tl.layers.Dense(n_units=800, act=tl.ReLU, name='dense_2')(net)
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>>> print(tensor)
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tf.Tensor([...], shape=(100, 800), dtype=float32)
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Notes
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-----
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If the layer input has more than two axes, it needs to be flatten by using :class:`Flatten`.
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"""
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def __init__(
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self,
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n_units,
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act=None,
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W_init=tl.initializers.truncated_normal(stddev=0.05),
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b_init=tl.initializers.constant(value=0.0),
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in_channels=None,
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name=None, # 'dense',
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):
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super(Dense, self).__init__(name, act=act)
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self.n_units = n_units
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self.W_init = W_init
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self.b_init = b_init
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self.in_channels = in_channels
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if self.in_channels is not None:
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self.build(self.in_channels)
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self._built = True
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logging.info(
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"Dense %s: %d %s" %
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(self.name, self.n_units, self.act.__class__.__name__ if self.act is not None else 'No Activation')
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)
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def __repr__(self):
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actstr = self.act.__class__.__name__ if self.act is not None else 'No Activation'
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s = ('{classname}(n_units={n_units}, ' + actstr)
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if self.in_channels is not None:
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s += ', in_channels=\'{in_channels}\''
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if self.name is not None:
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s += ', name=\'{name}\''
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s += ')'
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return s.format(classname=self.__class__.__name__, **self.__dict__)
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def build(self, inputs_shape):
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if self.in_channels is None and len(inputs_shape) != 2:
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raise AssertionError("The input dimension must be rank 2, please reshape or flatten it")
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if self.in_channels:
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shape = [self.in_channels, self.n_units]
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else:
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self.in_channels = inputs_shape[1]
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shape = [inputs_shape[1], self.n_units]
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self.W = self._get_weights("weights", shape=tuple(shape), init=self.W_init)
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self.b_init_flag = False
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if self.b_init:
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self.b = self._get_weights("biases", shape=(self.n_units, ), init=self.b_init)
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self.b_init_flag = True
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self.bias_add = tl.ops.BiasAdd()
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self.act_init_flag = False
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if self.act:
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self.act_init_flag = True
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self.matmul = tl.ops.MatMul()
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def forward(self, inputs):
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if self._forward_state == False:
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if self._built == False:
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self.build(tl.get_tensor_shape(inputs))
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self._built = True
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self._forward_state = True
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z = self.matmul(inputs, self.W)
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if self.b_init_flag:
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z = self.bias_add(z, self.b)
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if self.act_init_flag:
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z = self.act(z)
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return z
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