Source code for ashpy.models.fc.autoencoders

# Copyright 2019 Zuru Tech HK Limited. 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.

"""Collection of Fully Connected Autoencoders."""
from ashpy.models.fc.decoders import Decoder
from ashpy.models.fc.encoders import Encoder
from tensorflow import keras  # pylint: disable=no-name-in-module

__ALL__ = ["Autoencoder"]


[docs]class Autoencoder(keras.Model): # pylint: disable=no-member """ Primitive Model for all fully connected autoencoders. Examples: * Direct Usage: .. testcode:: autoencoder = Autoencoder( hidden_units=[256,128,64], encoding_dimension=100, output_shape=55 ) encoding, reconstruction = autoencoder(tf.zeros((1, 55))) print(encoding.shape) print(reconstruction.shape) .. testoutput:: (1, 100) (1, 55) """
[docs] def __init__(self, hidden_units, encoding_dimension, output_shape): """ Instantiate the :py:class:`Decoder`. Args: hidden_units (:obj:`tuple` of :obj:`int`): Number of units per hidden layer. encoding_dimension (int): encoding dimension. output_shape (int): output shape, usual equal to the input shape. Returns: :py:obj:`None` """ super().__init__() self._encoder = Encoder(hidden_units, encoding_dimension) self._decoder = Decoder(hidden_units[::-1], output_shape)
[docs] def call(self, inputs, training=True): """ Execute the model on input data. Args: inputs (:py:class:`tf.Tensor`): Input tensors. training (:obj:`bool`): Training flag. Returns: (encoding, reconstruction): Pair of tensors. """ encoding = self._encoder(inputs, training) reconstruction = self._decoder(encoding, training) return encoding, reconstruction