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