Table Of Contents
Table Of Contents

Source code for gluonts.model.deep_factor.RNNModel

# Copyright 2018 Amazon.com, Inc. or its affiliates. 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.
# A copy of the License is located at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license" file accompanying this file. This file 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.

# Third-party imports
from mxnet.gluon import HybridBlock, nn

# First-party imports
from gluonts.block.rnn import RNN
from gluonts.core.component import validated


[docs]class RNNModel(HybridBlock): @validated() def __init__( self, mode, num_hidden, num_layers, num_output, bidirectional=False, **kwargs, ): super(RNNModel, self).__init__(**kwargs) self.num_output = num_output with self.name_scope(): self.rnn = RNN( mode=mode, num_hidden=num_hidden, num_layers=num_layers, bidirectional=bidirectional, ) self.decoder = nn.Dense( num_output, in_units=num_hidden, flatten=False )
[docs] def hybrid_forward(self, F, inputs): return self.decoder(self.rnn(inputs))