# 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 import util
from tensorforce.core.parameters import Parameter
[docs]class Random(Parameter):
"""
Random hyperparameter.
Args:
name (string): Module name
(<span style="color:#0000C0"><b>internal use</b></span>).
dtype ("bool" | "int" | "long" | "float"): Tensor type
(<span style="color:#C00000"><b>required</b></span>).
distribution ("normal" | "uniform"): Distribution type for random hyperparameter value
(<span style="color:#C00000"><b>required</b></span>).
shape (iter[int > 0]): Tensor shape
(<span style="color:#00C000"><b>default</b></span>: scalar).
summary_labels ('all' | iter[string]): Labels of summaries to record
(<span style="color:#00C000"><b>default</b></span>: inherit value of parent module).
kwargs: Additional arguments dependent on distribution type.<br>
Normal distribution:
<ul>
<li><b>mean</b> (<i>float</i>) – Mean
(<span style="color:#00C000"><b>default</b></span>: 0.0).</li>
<li><b>stddev</b> (<i>float > 0.0</i>) – Standard deviation
(<span style="color:#00C000"><b>default</b></span>: 1.0).</li>
</ul>
Uniform distribution:
<ul>
<li><b>minval</b> (<i>int / float</i>) – Lower bound
(<span style="color:#00C000"><b>default</b></span>: 0 / 0.0).</li>
<li><b>maxval</b> (<i>float > minval</i>) – Upper bound
(<span style="color:#00C000"><b>default</b></span>: 1.0 for float,
<span style="color:#C00000"><b>required</b></span> for int).</li>
</ul>
"""
def __init__(self, name, dtype, distribution, shape=(), summary_labels=None, **kwargs):
super().__init__(name=name, dtype=dtype, shape=shape, summary_labels=summary_labels)
assert distribution in ('normal', 'uniform')
self.distribution = distribution
self.kwargs = kwargs
def get_parameter_value(self):
if self.distribution == 'normal':
parameter = tf.random.normal(
shape=self.shape, dtype=util.tf_dtype(dtype=self.dtype),
mean=self.kwargs.get('mean', 0.0), stddev=self.kwargs.get('stddev', 1.0)
)
elif self.distribution == 'uniform':
parameter = tf.random.uniform(
shape=self.shape, dtype=util.tf_dtype(dtype=self.dtype),
minval=self.kwargs.get('minval', 0), maxval=self.kwargs.get('minval', None)
)
return parameter