# Copyright 2019 The TensorFlow Authors. 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. # ============================================================================== """Built-in WideNDeep model classes.""" import tensorflow.compat.v2 as tf from keras import activations from keras import backend from keras import layers as layer_module from keras.engine import base_layer from keras.engine import data_adapter from keras.engine import training as keras_training from keras.utils import generic_utils # isort: off from tensorflow.python.util import deprecation from tensorflow.python.util.tf_export import keras_export @keras_export( "keras.experimental.WideDeepModel", v1=["keras.experimental.WideDeepModel", "keras.models.WideDeepModel"], ) @deprecation.deprecated_endpoints("keras.experimental.WideDeepModel") class WideDeepModel(keras_training.Model): r"""Wide & Deep Model for regression and classification problems. This model jointly train a linear and a dnn model. Example: ```python linear_model = LinearModel() dnn_model = keras.Sequential([keras.layers.Dense(units=64), keras.layers.Dense(units=1)]) combined_model = WideDeepModel(linear_model, dnn_model) combined_model.compile(optimizer=['sgd', 'adam'], loss='mse', metrics=['mse']) # define dnn_inputs and linear_inputs as separate numpy arrays or # a single numpy array if dnn_inputs is same as linear_inputs. combined_model.fit([linear_inputs, dnn_inputs], y, epochs) # or define a single `tf.data.Dataset` that contains a single tensor or # separate tensors for dnn_inputs and linear_inputs. dataset = tf.data.Dataset.from_tensors(([linear_inputs, dnn_inputs], y)) combined_model.fit(dataset, epochs) ``` Both linear and dnn model can be pre-compiled and trained separately before jointly training: Example: ```python linear_model = LinearModel() linear_model.compile('adagrad', 'mse') linear_model.fit(linear_inputs, y, epochs) dnn_model = keras.Sequential([keras.layers.Dense(units=1)]) dnn_model.compile('rmsprop', 'mse') dnn_model.fit(dnn_inputs, y, epochs) combined_model = WideDeepModel(linear_model, dnn_model) combined_model.compile(optimizer=['sgd', 'adam'], loss='mse', metrics=['mse']) combined_model.fit([linear_inputs, dnn_inputs], y, epochs) ``` """ def __init__(self, linear_model, dnn_model, activation=None, **kwargs): """Create a Wide & Deep Model. Args: linear_model: a premade LinearModel, its output must match the output of the dnn model. dnn_model: a `tf.keras.Model`, its output must match the output of the linear model. activation: Activation function. Set it to None to maintain a linear activation. **kwargs: The keyword arguments that are passed on to BaseLayer.__init__. Allowed keyword arguments include `name`. """ super().__init__(**kwargs) base_layer.keras_premade_model_gauge.get_cell("WideDeep").set(True) self.linear_model = linear_model self.dnn_model = dnn_model self.activation = activations.get(activation) def call(self, inputs, training=None): if not isinstance(inputs, (tuple, list)) or len(inputs) != 2: linear_inputs = dnn_inputs = inputs else: linear_inputs, dnn_inputs = inputs linear_output = self.linear_model(linear_inputs) if self.dnn_model._expects_training_arg: if training is None: training = backend.learning_phase() dnn_output = self.dnn_model(dnn_inputs, training=training) else: dnn_output = self.dnn_model(dnn_inputs) output = tf.nest.map_structure( lambda x, y: (x + y), linear_output, dnn_output ) if self.activation: return tf.nest.map_structure(self.activation, output) return output # This does not support gradient scaling and LossScaleOptimizer. def train_step(self, data): x, y, sample_weight = data_adapter.unpack_x_y_sample_weight(data) with tf.GradientTape() as tape: y_pred = self(x, training=True) loss = self.compiled_loss( y, y_pred, sample_weight, regularization_losses=self.losses ) self.compiled_metrics.update_state(y, y_pred, sample_weight) if isinstance(self.optimizer, (list, tuple)): linear_vars = self.linear_model.trainable_variables dnn_vars = self.dnn_model.trainable_variables linear_grads, dnn_grads = tape.gradient( loss, (linear_vars, dnn_vars) ) linear_optimizer = self.optimizer[0] dnn_optimizer = self.optimizer[1] linear_optimizer.apply_gradients(zip(linear_grads, linear_vars)) dnn_optimizer.apply_gradients(zip(dnn_grads, dnn_vars)) else: trainable_variables = self.trainable_variables grads = tape.gradient(loss, trainable_variables) self.optimizer.apply_gradients(zip(grads, trainable_variables)) return {m.name: m.result() for m in self.metrics} def _make_train_function(self): # Only needed for graph mode and model_to_estimator. has_recompiled = self._recompile_weights_loss_and_weighted_metrics() self._check_trainable_weights_consistency() # If we have re-compiled the loss/weighted metric sub-graphs then create # train function even if one exists already. This is because # `_feed_sample_weights` list has been updated on re-compile. if getattr(self, "train_function", None) is None or has_recompiled: # Restore the compiled trainable state. current_trainable_state = self._get_trainable_state() self._set_trainable_state(self._compiled_trainable_state) inputs = ( self._feed_inputs + self._feed_targets + self._feed_sample_weights ) if not isinstance(backend.symbolic_learning_phase(), int): inputs += [backend.symbolic_learning_phase()] if isinstance(self.optimizer, (list, tuple)): linear_optimizer = self.optimizer[0] dnn_optimizer = self.optimizer[1] else: linear_optimizer = self.optimizer dnn_optimizer = self.optimizer with backend.get_graph().as_default(): with backend.name_scope("training"): # Training updates updates = [] linear_updates = linear_optimizer.get_updates( params=self.linear_model.trainable_weights, loss=self.total_loss, ) updates += linear_updates dnn_updates = dnn_optimizer.get_updates( params=self.dnn_model.trainable_weights, loss=self.total_loss, ) updates += dnn_updates # Unconditional updates updates += self.get_updates_for(None) # Conditional updates relevant to this model updates += self.get_updates_for(self.inputs) metrics = self._get_training_eval_metrics() metrics_tensors = [ m._call_result for m in metrics if hasattr(m, "_call_result") ] with backend.name_scope("training"): # Gets loss and metrics. Updates weights at each call. fn = backend.function( inputs, [self.total_loss] + metrics_tensors, updates=updates, name="train_function", **self._function_kwargs ) setattr(self, "train_function", fn) # Restore the current trainable state self._set_trainable_state(current_trainable_state) def get_config(self): linear_config = generic_utils.serialize_keras_object(self.linear_model) dnn_config = generic_utils.serialize_keras_object(self.dnn_model) config = { "linear_model": linear_config, "dnn_model": dnn_config, "activation": activations.serialize(self.activation), } base_config = base_layer.Layer.get_config(self) return dict(list(base_config.items()) + list(config.items())) @classmethod def from_config(cls, config, custom_objects=None): linear_config = config.pop("linear_model") linear_model = layer_module.deserialize(linear_config, custom_objects) dnn_config = config.pop("dnn_model") dnn_model = layer_module.deserialize(dnn_config, custom_objects) activation = activations.deserialize( config.pop("activation", None), custom_objects=custom_objects ) return cls( linear_model=linear_model, dnn_model=dnn_model, activation=activation, **config )