219 lines
7.9 KiB
Python
219 lines
7.9 KiB
Python
|
# 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 linear model classes."""
|
||
|
|
||
|
import tensorflow.compat.v2 as tf
|
||
|
|
||
|
from keras import activations
|
||
|
from keras import initializers
|
||
|
from keras import regularizers
|
||
|
from keras.engine import base_layer
|
||
|
from keras.engine import input_spec
|
||
|
from keras.engine import training
|
||
|
from keras.layers import core
|
||
|
|
||
|
# isort: off
|
||
|
from tensorflow.python.util import deprecation
|
||
|
from tensorflow.python.util.tf_export import keras_export
|
||
|
|
||
|
|
||
|
@keras_export(
|
||
|
"keras.experimental.LinearModel",
|
||
|
v1=["keras.experimental.LinearModel", "keras.models.LinearModel"],
|
||
|
)
|
||
|
@deprecation.deprecated_endpoints("keras.experimental.LinearModel")
|
||
|
class LinearModel(training.Model):
|
||
|
r"""Linear Model for regression and classification problems.
|
||
|
|
||
|
This model approximates the following function:
|
||
|
$$y = \beta + \sum_{i=1}^{N} w_{i} * x_{i}$$
|
||
|
where $$\beta$$ is the bias and $$w_{i}$$ is the weight for each feature.
|
||
|
|
||
|
Example:
|
||
|
|
||
|
```python
|
||
|
model = LinearModel()
|
||
|
model.compile(optimizer='sgd', loss='mse')
|
||
|
model.fit(x, y, epochs=epochs)
|
||
|
```
|
||
|
|
||
|
This model accepts sparse float inputs as well:
|
||
|
|
||
|
Example:
|
||
|
```python
|
||
|
model = LinearModel()
|
||
|
opt = tf.keras.optimizers.Adam()
|
||
|
loss_fn = tf.keras.losses.MeanSquaredError()
|
||
|
with tf.GradientTape() as tape:
|
||
|
output = model(sparse_input)
|
||
|
loss = tf.reduce_mean(loss_fn(target, output))
|
||
|
grads = tape.gradient(loss, model.weights)
|
||
|
opt.apply_gradients(zip(grads, model.weights))
|
||
|
```
|
||
|
|
||
|
"""
|
||
|
|
||
|
def __init__(
|
||
|
self,
|
||
|
units=1,
|
||
|
activation=None,
|
||
|
use_bias=True,
|
||
|
kernel_initializer="zeros",
|
||
|
bias_initializer="zeros",
|
||
|
kernel_regularizer=None,
|
||
|
bias_regularizer=None,
|
||
|
**kwargs,
|
||
|
):
|
||
|
"""Create a Linear Model.
|
||
|
|
||
|
Args:
|
||
|
units: Positive integer, output dimension without the batch size.
|
||
|
activation: Activation function to use.
|
||
|
If you don't specify anything, no activation is applied.
|
||
|
use_bias: whether to calculate the bias/intercept for this model. If
|
||
|
set to False, no bias/intercept will be used in calculations, e.g.,
|
||
|
the data is already centered.
|
||
|
kernel_initializer: Initializer for the `kernel` weights matrices.
|
||
|
bias_initializer: Initializer for the bias vector.
|
||
|
kernel_regularizer: regularizer for kernel vectors.
|
||
|
bias_regularizer: regularizer for bias vector.
|
||
|
**kwargs: The keyword arguments that are passed on to
|
||
|
BaseLayer.__init__.
|
||
|
"""
|
||
|
|
||
|
self.units = units
|
||
|
self.activation = activations.get(activation)
|
||
|
self.use_bias = use_bias
|
||
|
self.kernel_initializer = initializers.get(kernel_initializer)
|
||
|
self.bias_initializer = initializers.get(bias_initializer)
|
||
|
self.kernel_regularizer = regularizers.get(kernel_regularizer)
|
||
|
self.bias_regularizer = regularizers.get(bias_regularizer)
|
||
|
super().__init__(**kwargs)
|
||
|
base_layer.keras_premade_model_gauge.get_cell("Linear").set(True)
|
||
|
|
||
|
def build(self, input_shape):
|
||
|
if isinstance(input_shape, dict):
|
||
|
names = sorted(list(input_shape.keys()))
|
||
|
self.input_specs = []
|
||
|
self.dense_layers = []
|
||
|
for name in names:
|
||
|
shape = input_shape[name]
|
||
|
layer = core.Dense(
|
||
|
units=self.units,
|
||
|
use_bias=False,
|
||
|
kernel_initializer=self.kernel_initializer,
|
||
|
kernel_regularizer=self.kernel_regularizer,
|
||
|
name=name,
|
||
|
)
|
||
|
layer.build(shape)
|
||
|
self.input_specs.append(
|
||
|
input_spec.InputSpec(shape=shape, name=name)
|
||
|
)
|
||
|
self.dense_layers.append(layer)
|
||
|
elif isinstance(input_shape, (tuple, list)) and all(
|
||
|
isinstance(shape, tf.TensorShape) for shape in input_shape
|
||
|
):
|
||
|
self.dense_layers = []
|
||
|
for shape in input_shape:
|
||
|
layer = core.Dense(
|
||
|
units=self.units,
|
||
|
use_bias=False,
|
||
|
kernel_initializer=self.kernel_initializer,
|
||
|
kernel_regularizer=self.kernel_regularizer,
|
||
|
)
|
||
|
layer.build(shape)
|
||
|
self.dense_layers.append(layer)
|
||
|
else:
|
||
|
# input_shape can be a single TensorShape or a tuple of ints.
|
||
|
layer = core.Dense(
|
||
|
units=self.units,
|
||
|
use_bias=False,
|
||
|
kernel_initializer=self.kernel_initializer,
|
||
|
kernel_regularizer=self.kernel_regularizer,
|
||
|
)
|
||
|
layer.build(input_shape)
|
||
|
self.dense_layers = [layer]
|
||
|
|
||
|
if self.use_bias:
|
||
|
self.bias = self.add_weight(
|
||
|
"bias",
|
||
|
shape=self.units,
|
||
|
initializer=self.bias_initializer,
|
||
|
regularizer=self.bias_regularizer,
|
||
|
dtype=self.dtype,
|
||
|
trainable=True,
|
||
|
)
|
||
|
else:
|
||
|
self.bias = None
|
||
|
self.built = True
|
||
|
|
||
|
def call(self, inputs):
|
||
|
result = None
|
||
|
if isinstance(inputs, dict):
|
||
|
names = [layer.name for layer in self.dense_layers]
|
||
|
different_keys = set(names) - set(inputs.keys())
|
||
|
if different_keys:
|
||
|
raise ValueError(
|
||
|
"The `inputs` dictionary does not match "
|
||
|
"the structure expected by the model."
|
||
|
f"\n\tExpected keys: {set(names)}"
|
||
|
f"\n\tReceived keys: {set(inputs.keys())}"
|
||
|
f"\n\tMissing keys: {different_keys}"
|
||
|
)
|
||
|
inputs = [inputs[name] for name in names]
|
||
|
for inp, layer in zip(inputs, self.dense_layers):
|
||
|
output = layer(inp)
|
||
|
if result is None:
|
||
|
result = output
|
||
|
else:
|
||
|
result += output
|
||
|
elif isinstance(inputs, (tuple, list)):
|
||
|
for inp, layer in zip(inputs, self.dense_layers):
|
||
|
output = layer(inp)
|
||
|
if result is None:
|
||
|
result = output
|
||
|
else:
|
||
|
result += output
|
||
|
else:
|
||
|
result = self.dense_layers[0](inputs)
|
||
|
|
||
|
if self.use_bias:
|
||
|
result = tf.nn.bias_add(result, self.bias)
|
||
|
if self.activation is not None:
|
||
|
return self.activation(result)
|
||
|
return result
|
||
|
|
||
|
def get_config(self):
|
||
|
config = {
|
||
|
"units": self.units,
|
||
|
"activation": activations.serialize(self.activation),
|
||
|
"use_bias": self.use_bias,
|
||
|
"kernel_initializer": initializers.serialize(
|
||
|
self.kernel_initializer
|
||
|
),
|
||
|
"bias_initializer": initializers.serialize(self.bias_initializer),
|
||
|
"kernel_regularizer": regularizers.serialize(
|
||
|
self.kernel_regularizer
|
||
|
),
|
||
|
"bias_regularizer": regularizers.serialize(self.bias_regularizer),
|
||
|
}
|
||
|
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):
|
||
|
del custom_objects
|
||
|
return cls(**config)
|