Intelegentny_Pszczelarz/.venv/Lib/site-packages/keras/premade_models/wide_deep.py

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2023-06-19 00:49:18 +02:00
# 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.saving.legacy import serialization
# 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 = serialization.serialize_keras_object(self.linear_model)
dnn_config = serialization.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
)