# Copyright 2018 Tensorforce Team. 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
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import tensorflow as tf
from tensorforce.core.layers import TransformationBase
[docs]class Dense(TransformationBase):
"""
Dense fully-connected layer (specification key: `dense`).
Args:
name (string): Layer name
(<span style="color:#00C000"><b>default</b></span>: internally chosen).
size (int >= 0): Layer output size, 0 implies additionally removing the axis
(<span style="color:#C00000"><b>required</b></span>).
bias (bool): Whether to add a trainable bias variable
(<span style="color:#00C000"><b>default</b></span>: true).
activation ('crelu' | 'elu' | 'leaky-relu' | 'none' | 'relu' | 'selu' | 'sigmoid' |
'softmax' | 'softplus' | 'softsign' | 'swish' | 'tanh'): Activation nonlinearity
(<span style="color:#00C000"><b>default</b></span>: "relu").
dropout (parameter, 0.0 <= float < 1.0): Dropout rate
(<span style="color:#00C000"><b>default</b></span>: 0.0).
is_trainable (bool): Whether layer variables are trainable
(<span style="color:#00C000"><b>default</b></span>: true).
input_spec (specification): Input tensor specification
(<span style="color:#00C000"><b>internal use</b></span>).
summary_labels ('all' | iter[string]): Labels of summaries to record
(<span style="color:#00C000"><b>default</b></span>: inherit value of parent module).
l2_regularization (float >= 0.0): Scalar controlling L2 regularization
(<span style="color:#00C000"><b>default</b></span>: inherit value of parent module).
"""
def __init__(
self, name, size, bias=True, activation='relu', dropout=0.0, is_trainable=True,
input_spec=None, summary_labels=None, l2_regularization=None
):
super().__init__(
name=name, size=size, bias=bias, activation=activation, dropout=dropout,
is_trainable=is_trainable, input_spec=input_spec, summary_labels=summary_labels,
l2_regularization=l2_regularization
)
def default_input_spec(self):
return dict(type='float', shape=(0,))
def get_output_spec(self, input_spec):
if self.squeeze:
input_spec['shape'] = input_spec['shape'][:-1]
else:
input_spec['shape'] = input_spec['shape'][:-1] + (self.size,)
input_spec.pop('min_value', None)
input_spec.pop('max_value', None)
return input_spec
def tf_initialize(self):
super().tf_initialize()
initializer = 'orthogonal'
if self.activation is not None and self.activation.nonlinearity == 'relu':
initializer += '-relu'
in_size = self.input_spec['shape'][0]
self.weights = self.add_variable(
name='weights', dtype='float', shape=(in_size, self.size),
is_trainable=self.is_trainable, initializer=initializer
)
def tf_apply(self, x):
# tf.assert_rank_in(x=x, ranks=(2, 3, 4))
x = tf.matmul(a=x, b=self.weights)
return super().tf_apply(x=x)
[docs]class Linear(Dense):
"""
Linear layer (specification key: `linear`).
Args:
name (string): Layer name
(<span style="color:#00C000"><b>default</b></span>: internally chosen).
size (int >= 0): Layer output size, 0 implies additionally removing the axis
(<span style="color:#C00000"><b>required</b></span>).
bias (bool): Whether to add a trainable bias variable
(<span style="color:#00C000"><b>default</b></span>: true).
is_trainable (bool): Whether layer variables are trainable
(<span style="color:#00C000"><b>default</b></span>: true).
input_spec (specification): Input tensor specification
(<span style="color:#00C000"><b>internal use</b></span>).
summary_labels ('all' | iter[string]): Labels of summaries to record
(<span style="color:#00C000"><b>default</b></span>: inherit value of parent module).
l2_regularization (float >= 0.0): Scalar controlling L2 regularization
(<span style="color:#00C000"><b>default</b></span>: inherit value of parent module).
"""
def __init__(
self, name, size, bias=True, is_trainable=True, input_spec=None, summary_labels=None,
l2_regularization=None
):
super().__init__(
name=name, size=size, bias=bias, activation=None, dropout=0.0,
is_trainable=is_trainable, input_spec=input_spec, summary_labels=summary_labels,
l2_regularization=l2_regularization
)