subsampling_step.py

# 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 TensorforceError, util
from tensorforce.core import parameter_modules
from tensorforce.core.optimizers import MetaOptimizer


class SubsamplingStep(MetaOptimizer):
    """
    The subsampling-step meta optimizer randomly samples a subset of batch instances to calculate
    the optimization step of another optimizer.
    """

    def __init__(self, name, optimizer, fraction, summary_labels=None):
        """
        Subsampling-step optimizer constructor.

        Args:
            fraction (parameter, 0.0 < float <= 1.0): Fraction of instances of the batch to
                subsample (**required**).
        """
        super().__init__(name=name, optimizer=optimizer, summary_labels=summary_labels)

        self.fraction = self.add_module(
            name='fraction', module=fraction, modules=parameter_modules, dtype='float'
        )

    def tf_step(self, variables, arguments, **kwargs):
        # Get some (batched) argument to determine batch size.
        arguments_iter = iter(arguments.values())
        some_argument = next(arguments_iter)

        try:
            while not isinstance(some_argument, tf.Tensor) or util.rank(x=some_argument) == 0:
                if isinstance(some_argument, dict):
                    if some_argument:
                        arguments_iter = iter(some_argument.values())
                    some_argument = next(arguments_iter)
                elif isinstance(some_argument, list):
                    if some_argument:
                        arguments_iter = iter(some_argument)
                    some_argument = next(arguments_iter)
                elif some_argument is None or util.rank(x=some_argument) == 0:
                    # Non-batched argument
                    some_argument = next(arguments_iter)
                else:
                    raise TensorforceError("Invalid argument type.")
        except StopIteration:
            raise TensorforceError("Invalid argument type.")

        if util.tf_dtype(dtype='int') in (tf.int32, tf.int64):
            batch_size = tf.shape(input=some_argument, out_type=util.tf_dtype(dtype='int'))[0]
        else:
            batch_size = tf.dtypes.cast(
                x=tf.shape(input=some_argument)[0], dtype=util.tf_dtype(dtype='int')
            )
        fraction = self.fraction.value()
        num_samples = fraction * tf.dtypes.cast(x=batch_size, dtype=util.tf_dtype('float'))
        num_samples = tf.dtypes.cast(x=num_samples, dtype=util.tf_dtype('int'))
        one = tf.constant(value=1, dtype=util.tf_dtype('int'))
        num_samples = tf.maximum(x=num_samples, y=one)
        indices = tf.random.uniform(
            shape=(num_samples,), maxval=batch_size, dtype=util.tf_dtype(dtype='int')
        )

        function = (lambda x: tf.gather(params=x, indices=indices))
        subsampled_arguments = util.fmap(function=function, xs=arguments)

        return self.optimizer.step(variables=variables, arguments=subsampled_arguments, **kwargs)