220 lines
8.9 KiB
Python
220 lines
8.9 KiB
Python
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Built-in WideNDeep model classes."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.python.eager import backprop
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from tensorflow.python.keras import activations
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from tensorflow.python.keras import backend as K
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from tensorflow.python.keras import layers as layer_module
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from tensorflow.python.keras.engine import base_layer
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from tensorflow.python.keras.engine import data_adapter
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from tensorflow.python.keras.engine import training as keras_training
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from tensorflow.python.keras.utils import generic_utils
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from tensorflow.python.util import nest
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from tensorflow.python.util.tf_export import keras_export
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@keras_export('keras.experimental.WideDeepModel')
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class WideDeepModel(keras_training.Model):
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r"""Wide & Deep Model for regression and classification problems.
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This model jointly train a linear and a dnn model.
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Example:
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```python
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linear_model = LinearModel()
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dnn_model = keras.Sequential([keras.layers.Dense(units=64),
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keras.layers.Dense(units=1)])
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combined_model = WideDeepModel(linear_model, dnn_model)
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combined_model.compile(optimizer=['sgd', 'adam'], 'mse', ['mse'])
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# define dnn_inputs and linear_inputs as separate numpy arrays or
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# a single numpy array if dnn_inputs is same as linear_inputs.
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combined_model.fit([linear_inputs, dnn_inputs], y, epochs)
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# or define a single `tf.data.Dataset` that contains a single tensor or
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# separate tensors for dnn_inputs and linear_inputs.
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dataset = tf.data.Dataset.from_tensors(([linear_inputs, dnn_inputs], y))
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combined_model.fit(dataset, epochs)
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```
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Both linear and dnn model can be pre-compiled and trained separately
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before jointly training:
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Example:
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```python
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linear_model = LinearModel()
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linear_model.compile('adagrad', 'mse')
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linear_model.fit(linear_inputs, y, epochs)
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dnn_model = keras.Sequential([keras.layers.Dense(units=1)])
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dnn_model.compile('rmsprop', 'mse')
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dnn_model.fit(dnn_inputs, y, epochs)
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combined_model = WideDeepModel(linear_model, dnn_model)
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combined_model.compile(optimizer=['sgd', 'adam'], 'mse', ['mse'])
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combined_model.fit([linear_inputs, dnn_inputs], y, epochs)
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```
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"""
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def __init__(self, linear_model, dnn_model, activation=None, **kwargs):
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"""Create a Wide & Deep Model.
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Args:
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linear_model: a premade LinearModel, its output must match the output of
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the dnn model.
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dnn_model: a `tf.keras.Model`, its output must match the output of the
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linear model.
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activation: Activation function. Set it to None to maintain a linear
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activation.
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**kwargs: The keyword arguments that are passed on to BaseLayer.__init__.
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Allowed keyword arguments include `name`.
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"""
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super(WideDeepModel, self).__init__(**kwargs)
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base_layer.keras_premade_model_gauge.get_cell('WideDeep').set(True)
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self.linear_model = linear_model
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self.dnn_model = dnn_model
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self.activation = activations.get(activation)
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def call(self, inputs, training=None):
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if not isinstance(inputs, (tuple, list)) or len(inputs) != 2:
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linear_inputs = dnn_inputs = inputs
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else:
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linear_inputs, dnn_inputs = inputs
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linear_output = self.linear_model(linear_inputs)
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# pylint: disable=protected-access
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if self.dnn_model._expects_training_arg:
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if training is None:
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training = K.learning_phase()
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dnn_output = self.dnn_model(dnn_inputs, training=training)
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else:
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dnn_output = self.dnn_model(dnn_inputs)
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output = nest.map_structure(lambda x, y: (x + y), linear_output, dnn_output)
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if self.activation:
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return nest.map_structure(self.activation, output)
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return output
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# This does not support gradient scaling and LossScaleOptimizer.
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def train_step(self, data):
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x, y, sample_weight = data_adapter.unpack_x_y_sample_weight(data)
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x, y, sample_weight = data_adapter.expand_1d((x, y, sample_weight))
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with backprop.GradientTape() as tape:
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y_pred = self(x, training=True)
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loss = self.compiled_loss(
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y, y_pred, sample_weight, regularization_losses=self.losses)
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self.compiled_metrics.update_state(y, y_pred, sample_weight)
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if isinstance(self.optimizer, (list, tuple)):
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linear_vars = self.linear_model.trainable_variables
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dnn_vars = self.dnn_model.trainable_variables
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linear_grads, dnn_grads = tape.gradient(loss, (linear_vars, dnn_vars))
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linear_optimizer = self.optimizer[0]
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dnn_optimizer = self.optimizer[1]
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linear_optimizer.apply_gradients(zip(linear_grads, linear_vars))
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dnn_optimizer.apply_gradients(zip(dnn_grads, dnn_vars))
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else:
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trainable_variables = self.trainable_variables
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grads = tape.gradient(loss, trainable_variables)
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self.optimizer.apply_gradients(zip(grads, trainable_variables))
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return {m.name: m.result() for m in self.metrics}
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def _make_train_function(self):
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# Only needed for graph mode and model_to_estimator.
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has_recompiled = self._recompile_weights_loss_and_weighted_metrics()
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self._check_trainable_weights_consistency()
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# If we have re-compiled the loss/weighted metric sub-graphs then create
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# train function even if one exists already. This is because
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# `_feed_sample_weights` list has been updated on re-compile.
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if getattr(self, 'train_function', None) is None or has_recompiled:
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# Restore the compiled trainable state.
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current_trainable_state = self._get_trainable_state()
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self._set_trainable_state(self._compiled_trainable_state)
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inputs = (
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self._feed_inputs + self._feed_targets + self._feed_sample_weights)
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if not isinstance(K.symbolic_learning_phase(), int):
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inputs += [K.symbolic_learning_phase()]
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if isinstance(self.optimizer, (list, tuple)):
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linear_optimizer = self.optimizer[0]
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dnn_optimizer = self.optimizer[1]
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else:
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linear_optimizer = self.optimizer
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dnn_optimizer = self.optimizer
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with K.get_graph().as_default():
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with K.name_scope('training'):
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# Training updates
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updates = []
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linear_updates = linear_optimizer.get_updates(
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params=self.linear_model.trainable_weights, # pylint: disable=protected-access
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loss=self.total_loss)
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updates += linear_updates
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dnn_updates = dnn_optimizer.get_updates(
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params=self.dnn_model.trainable_weights, # pylint: disable=protected-access
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loss=self.total_loss)
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updates += dnn_updates
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# Unconditional updates
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updates += self.get_updates_for(None)
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# Conditional updates relevant to this model
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updates += self.get_updates_for(self.inputs)
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metrics = self._get_training_eval_metrics()
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metrics_tensors = [
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m._call_result for m in metrics if hasattr(m, '_call_result') # pylint: disable=protected-access
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]
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with K.name_scope('training'):
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# Gets loss and metrics. Updates weights at each call.
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fn = K.function(
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inputs, [self.total_loss] + metrics_tensors,
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updates=updates,
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name='train_function',
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**self._function_kwargs)
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setattr(self, 'train_function', fn)
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# Restore the current trainable state
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self._set_trainable_state(current_trainable_state)
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def get_config(self):
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linear_config = generic_utils.serialize_keras_object(self.linear_model)
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dnn_config = generic_utils.serialize_keras_object(self.dnn_model)
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config = {
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'linear_model': linear_config,
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'dnn_model': dnn_config,
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'activation': activations.serialize(self.activation),
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}
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base_config = base_layer.Layer.get_config(self)
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return dict(list(base_config.items()) + list(config.items()))
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@classmethod
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def from_config(cls, config, custom_objects=None):
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linear_config = config.pop('linear_model')
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linear_model = layer_module.deserialize(linear_config, custom_objects)
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dnn_config = config.pop('dnn_model')
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dnn_model = layer_module.deserialize(dnn_config, custom_objects)
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activation = activations.deserialize(
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config.pop('activation', None), custom_objects=custom_objects)
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return cls(
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linear_model=linear_model,
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dnn_model=dnn_model,
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activation=activation,
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**config)
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