# Copyright 2018 The Texar Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
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"""
Various neural networks and related utilities.
"""
import tensorflow as tf
from texar.tf.modules.networks.network_base import FeedForwardNetworkBase
from texar.tf.modules.networks.network_base import _build_layers
__all__ = [
"FeedForwardNetwork"
]
[docs]class FeedForwardNetwork(FeedForwardNetworkBase):
"""Feed-forward neural network that consists of a sequence of layers.
Args:
layers (list, optional): A list of :tf_main:`Layer <layers/Layer>`
instances composing the network. If not given, layers are created
according to :attr:`hparams`.
hparams (dict, optional): Embedder hyperparameters. Missing
hyperparamerter will be set to default values. See
:meth:`default_hparams` for the hyperparameter sturcture and
default values.
See :meth:`~texar.tf.modules.RNNDecoderBase._build` of
:class:`~texar.tf.modules.FeedForwardNetworkBase` for the inputs and outputs.
Example:
.. code-block:: python
hparams = { # Builds a two-layer dense NN
"layers": [
{ "type": "Dense", "kwargs": { "units": 256 },
{ "type": "Dense", "kwargs": { "units": 10 }
]
}
nn = FeedForwardNetwork(hparams=hparams)
inputs = tf.random_uniform([64, 100])
outputs = nn(inputs)
# outputs == Tensor of shape [64, 10]
"""
def __init__(self, layers=None, hparams=None):
FeedForwardNetworkBase.__init__(self, hparams)
with tf.variable_scope(self.variable_scope):
_build_layers(
self, layers=layers, layer_hparams=self._hparams.layers)
[docs] @staticmethod
def default_hparams():
"""Returns a dictionary of hyperparameters with default values.
.. code-block:: python
{
"layers": [],
"name": "NN"
}
Here:
"layers": list
A list of layer hyperparameters. See :func:`~texar.tf.core.get_layer`
for the details of layer hyperparameters.
"name": str
Name of the network.
"""
return {
"layers": [],
"name": "NN"
}