<reponame>fossabot/ludwig<filename>ludwig/encoders/generic_encoders.py
#! /usr/bin/env python
# coding=utf-8
# Copyright (c) 2019 Uber Technologies, Inc.
#
# 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 logging

from tensorflow.keras.layers import Layer

from ludwig.modules.fully_connected_modules import FCStack

logger = logging.getLogger(__name__)


class PassthroughEncoder(Layer):

    def __init__(
            self,
            **kwargs
    ):
        super(PassthroughEncoder, self).__init__()
        logger.debug(' {}'.format(self.name))

    def call(self, inputs, training=None, mask=None):
        """
            :param inputs: The inputs fed into the encoder.
                   Shape: [batch x 1], type tf.float32
        """
        return {'encoder_output': inputs}


class DenseEncoder(Layer):

    def __init__(
            self,
            layers=None,
            num_layers=1,
            fc_size=256,
            use_bias=True,
            weights_initializer='glorot_uniform',
            bias_initializer='zeros',
            weights_regularizer=None,
            bias_regularizer=None,
            activity_regularizer=None,
            # weights_constraint=None,
            # bias_constraint=None,
            norm=None,
            norm_params=None,
            activation='relu',
            dropout_rate=0,
            **kwargs
    ):
        super(DenseEncoder, self).__init__()
        logger.debug(' {}'.format(self.name))

        logger.debug('  FCStack')
        self.fc_stack = FCStack(
            layers=layers,
            num_layers=num_layers,
            default_fc_size=fc_size,
            default_use_bias=use_bias,
            default_weights_initializer=weights_initializer,
            default_bias_initializer=bias_initializer,
            default_weights_regularizer=weights_regularizer,
            default_bias_regularizer=bias_regularizer,
            default_activity_regularizer=activity_regularizer,
            # default_weights_constraint=weights_constraint,
            # default_bias_constraint=bias_constraint,
            default_norm=norm,
            default_norm_params=norm_params,
            default_activation=activation,
            default_dropout_rate=dropout_rate,
        )

    def call(self, inputs, training=None, mask=None):
        """
            :param inputs: The inputs fed into the encoder.
                   Shape: [batch x 1], type tf.float32
        """
        return {'encoder_output': self.fc_stack(inputs)}

#! /usr/bin/env python
# coding=utf-8
# Copyright (c) 2019 Uber Technologies, Inc.
#
# 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 logging

from tensorflow.keras.layers import Layer

from ludwig.modules.fully_connected_modules import FCStack

logger = logging.getLogger(__name__)


class PassthroughEncoder(Layer):

    def __init__(
            self,
            **kwargs
    ):
        super(PassthroughEncoder, self).__init__()
        logger.debug(' {}'.format(self.name))

    def call(self, inputs, training=None, mask=None):
        """
            :param inputs: The inputs fed into the encoder.
                   Shape: [batch x 1], type tf.float32
        """
        return {'encoder_output': inputs}


class DenseEncoder(Layer):

    def __init__(
            self,
            layers=None,
            num_layers=1,
            fc_size=256,
            use_bias=True,
            weights_initializer='glorot_uniform',
            bias_initializer='zeros',
            weights_regularizer=None,
            bias_regularizer=None,
            activity_regularizer=None,
            # weights_constraint=None,
            # bias_constraint=None,
            norm=None,
            norm_params=None,
            activation='relu',
            dropout_rate=0,
            **kwargs
    ):
        super(DenseEncoder, self).__init__()
        logger.debug(' {}'.format(self.name))

        logger.debug('  FCStack')
        self.fc_stack = FCStack(
            layers=layers,
            num_layers=num_layers,
            default_fc_size=fc_size,
            default_use_bias=use_bias,
            default_weights_initializer=weights_initializer,
            default_bias_initializer=bias_initializer,
            default_weights_regularizer=weights_regularizer,
            default_bias_regularizer=bias_regularizer,
            default_activity_regularizer=activity_regularizer,
            # default_weights_constraint=weights_constraint,
            # default_bias_constraint=bias_constraint,
            default_norm=norm,
            default_norm_params=norm_params,
            default_activation=activation,
            default_dropout_rate=dropout_rate,
        )

    def call(self, inputs, training=None, mask=None):
        """
            :param inputs: The inputs fed into the encoder.
                   Shape: [batch x 1], type tf.float32
        """
        return {'encoder_output': self.fc_stack(inputs)}
#! /usr/bin/env python # coding=utf-8 # Copyright (c) 2019 Uber Technologies, Inc. # # 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. # ============================================================================== :param inputs: The inputs fed into the encoder.                    Shape: [batch x 1], type tf.float32 # weights_constraint=None, # bias_constraint=None, # default_weights_constraint=weights_constraint, # default_bias_constraint=bias_constraint, :param inputs: The inputs fed into the encoder.                    Shape: [batch x 1], type tf.float32