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#! /usr/bin/python

# -*- coding: utf-8 -*-

import tensorflow as tf

from tensorlayer.layers.core import Layer

from tensorlayer.layers.core import TF_GRAPHKEYS_VARIABLES

from tensorlayer import logging

from tensorlayer.decorators import deprecated_alias

__all__ = [

'LambdaLayer',

'ElementwiseLambdaLayer',

]

class LambdaLayer(Layer):

"""A layer that takes a user-defined function using TensorFlow Lambda, for multiple inputs see :class:`ElementwiseLambdaLayer`.

Parameters

----------

prev_layer : :class:`Layer`

Previous layer.

fn : function

The function that applies to the outputs of previous layer.

fn_args : dictionary or None

The arguments for the function (option).

name : str

A unique layer name.

Examples

---------

Non-parametric case

>>> import tensorflow as tf

>>> import tensorlayer as tl

>>> x = tf.placeholder(tf.float32, shape=[None, 1], name='x')

>>> net = tl.layers.InputLayer(x, name='input')

>>> net = tl.layers.LambdaLayer(net, lambda x: 2*x, name='lambda')

Parametric case, merge other wrappers into TensorLayer

>>> from keras.layers import *

>>> from tensorlayer.layers import *

>>> def keras_block(x):

>>> x = Dropout(0.8)(x)

>>> x = Dense(800, activation='relu')(x)

>>> x = Dropout(0.5)(x)

>>> x = Dense(800, activation='relu')(x)

>>> x = Dropout(0.5)(x)

>>> logits = Dense(10, activation='linear')(x)

>>> return logits

>>> net = InputLayer(x, name='input')

>>> net = LambdaLayer(net, fn=keras_block, name='keras')

"""

@deprecated_alias(layer='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release

def __init__(

self,

prev_layer,

fn,

fn_args=None,

name='lambda_layer',

):

super(LambdaLayer, self).__init__(prev_layer=prev_layer, fn_args=fn_args, name=name)

logging.info("LambdaLayer %s" % self.name)

if fn is None:

raise AssertionError("The `fn` argument cannot be None")

with tf.variable_scope(name) as vs:

self.outputs = fn(self.inputs, **self.fn_args)

variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)

self._add_layers(self.outputs)

self._add_params(variables)

class ElementwiseLambdaLayer(Layer):

"""A layer that use a custom function to combine multiple :class:`Layer` inputs.

Parameters

----------

layers : list of :class:`Layer`

The list of layers to combine.

fn : function

The function that applies to the outputs of previous layer.

fn_args : dictionary or None

The arguments for the function (option).

act : activation function

The activation function of this layer.

name : str

A unique layer name.

Examples

--------

z = mean + noise * tf.exp(std * 0.5)

>>> import tensorflow as tf

>>> import tensorlayer as tl

>>> def func(noise, mean, std):

>>> return mean + noise * tf.exp(std * 0.5)

>>> x = tf.placeholder(tf.float32, [None, 200])

>>> noise_tensor = tf.random_normal(tf.stack([tf.shape(x)[0], 200]))

>>> noise = tl.layers.InputLayer(noise_tensor)

>>> net = tl.layers.InputLayer(x)

>>> net = tl.layers.DenseLayer(net, n_units=200, act=tf.nn.relu, name='dense1')

>>> mean = tl.layers.DenseLayer(net, n_units=200, name='mean')

>>> std = tl.layers.DenseLayer(net, n_units=200, name='std')

>>> z = tl.layers.ElementwiseLambdaLayer([noise, mean, std], fn=func, name='z')

"""

def __init__(

self,

layers,

fn,

fn_args=None,

act=None,

name='elementwiselambda_layer',

):

super(ElementwiseLambdaLayer, self).__init__(prev_layer=layers, act=act, fn_args=fn_args, name=name)

logging.info("ElementwiseLambdaLayer %s" % self.name)

with tf.variable_scope(name) as vs:

self.outputs = self._apply_activation(fn(*self.inputs, **self.fn_args))

variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)

self._add_layers(self.outputs)

self._add_params(variables)