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dcn.py
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dcn.py
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# Copyright 2024 The TensorFlow Recommenders Authors.
#
# 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.
"""Implements `Cross` Layer, the cross layer in Deep & Cross Network (DCN)."""
from typing import Union, Text, Optional
import tensorflow as tf
@tf.keras.utils.register_keras_serializable()
class Cross(tf.keras.layers.Layer):
"""Cross Layer in Deep & Cross Network to learn explicit feature interactions.
A layer that creates explicit and bounded-degree feature interactions
efficiently. The `call` method accepts `inputs` as a tuple of size 2
tensors. The first input `x0` is the base layer that contains the original
features (usually the embedding layer); the second input `xi` is the output
of the previous `Cross` layer in the stack, i.e., the i-th `Cross`
layer. For the first `Cross` layer in the stack, x0 = xi.
The output is x_{i+1} = x0 .* (W * xi + bias + diag_scale * xi) + xi,
where .* designates elementwise multiplication, W could be a full-rank
matrix, or a low-rank matrix U*V to reduce the computational cost, and
diag_scale increases the diagonal of W to improve training stability (
especially for the low-rank case).
References:
1. [R. Wang et al.](https://arxiv.org/pdf/2008.13535.pdf)
See Eq. (1) for full-rank and Eq. (2) for low-rank version.
2. [R. Wang et al.](https://arxiv.org/pdf/1708.05123.pdf)
Example:
```python
# after embedding layer in a functional model:
input = tf.keras.Input(shape=(None,), name='index', dtype=tf.int64)
x0 = tf.keras.layers.Embedding(input_dim=32, output_dim=6)
x1 = Cross()(x0, x0)
x2 = Cross()(x0, x1)
logits = tf.keras.layers.Dense(units=10)(x2)
model = tf.keras.Model(input, logits)
```
Args:
projection_dim: project dimension to reduce the computational cost.
Default is `None` such that a full (`input_dim` by `input_dim`) matrix
W is used. If enabled, a low-rank matrix W = U*V will be used, where U
is of size `input_dim` by `projection_dim` and V is of size
`projection_dim` by `input_dim`. `projection_dim` need to be smaller
than `input_dim`/2 to improve the model efficiency. In practice, we've
observed that `projection_dim` = d/4 consistently preserved the
accuracy of a full-rank version.
diag_scale: a non-negative float used to increase the diagonal of the
kernel W by `diag_scale`, that is, W + diag_scale * I, where I is an
identity matrix.
use_bias: whether to add a bias term for this layer. If set to False,
no bias term will be used.
preactivation: Activation applied to output matrix of the layer, before
multiplication with the input. Can be used to control the scale of the
layer's outputs and improve stability.
kernel_initializer: Initializer to use on the kernel matrix.
bias_initializer: Initializer to use on the bias vector.
kernel_regularizer: Regularizer to use on the kernel matrix.
bias_regularizer: Regularizer to use on bias vector.
Input shape: A tuple of 2 (batch_size, `input_dim`) dimensional inputs.
Output shape: A single (batch_size, `input_dim`) dimensional output.
"""
def __init__(
self,
projection_dim: Optional[int] = None,
diag_scale: Optional[float] = 0.0,
use_bias: bool = True,
preactivation: Optional[Union[str, tf.keras.layers.Activation]] = None,
kernel_initializer: Union[
Text, tf.keras.initializers.Initializer] = "truncated_normal",
bias_initializer: Union[Text,
tf.keras.initializers.Initializer] = "zeros",
kernel_regularizer: Union[Text, None,
tf.keras.regularizers.Regularizer] = None,
bias_regularizer: Union[Text, None,
tf.keras.regularizers.Regularizer] = None,
**kwargs):
super(Cross, self).__init__(**kwargs)
self._projection_dim = projection_dim
self._diag_scale = diag_scale
self._use_bias = use_bias
self._preactivation = tf.keras.activations.get(preactivation)
self._kernel_initializer = tf.keras.initializers.get(kernel_initializer)
self._bias_initializer = tf.keras.initializers.get(bias_initializer)
self._kernel_regularizer = tf.keras.regularizers.get(kernel_regularizer)
self._bias_regularizer = tf.keras.regularizers.get(bias_regularizer)
self._input_dim = None
self._supports_masking = True
if self._diag_scale < 0: # pytype: disable=unsupported-operands
raise ValueError(
"`diag_scale` should be non-negative. Got `diag_scale` = {}".format(
self._diag_scale))
def build(self, input_shape):
last_dim = input_shape[-1]
if self._projection_dim is None:
self._dense = tf.keras.layers.Dense(
last_dim,
kernel_initializer=_clone_initializer(self._kernel_initializer),
bias_initializer=self._bias_initializer,
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer,
use_bias=self._use_bias,
dtype=self.dtype,
activation=self._preactivation,
)
else:
self._dense_u = tf.keras.layers.Dense(
self._projection_dim,
kernel_initializer=_clone_initializer(self._kernel_initializer),
kernel_regularizer=self._kernel_regularizer,
use_bias=False,
dtype=self.dtype,
)
self._dense_v = tf.keras.layers.Dense(
last_dim,
kernel_initializer=_clone_initializer(self._kernel_initializer),
bias_initializer=self._bias_initializer,
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer,
use_bias=self._use_bias,
dtype=self.dtype,
activation=self._preactivation,
)
self.built = True
def call(self, x0: tf.Tensor, x: Optional[tf.Tensor] = None) -> tf.Tensor:
"""Computes the feature cross.
Args:
x0: The input tensor
x: Optional second input tensor. If provided, the layer will compute
crosses between x0 and x; if not provided, the layer will compute
crosses between x0 and itself.
Returns:
Tensor of crosses.
"""
if not self.built:
self.build(x0.shape)
if x is None:
x = x0
if x0.shape[-1] != x.shape[-1]:
raise ValueError(
"`x0` and `x` dimension mismatch! Got `x0` dimension {}, and x "
"dimension {}. This case is not supported yet.".format(
x0.shape[-1], x.shape[-1]))
if self._projection_dim is None:
prod_output = self._dense(x)
else:
prod_output = self._dense_v(self._dense_u(x))
prod_output = tf.cast(prod_output, self.compute_dtype)
if self._diag_scale:
prod_output = prod_output + self._diag_scale * x
return x0 * prod_output + x
def get_config(self):
config = {
"projection_dim":
self._projection_dim,
"diag_scale":
self._diag_scale,
"use_bias":
self._use_bias,
"preactivation":
tf.keras.activations.serialize(self._preactivation),
"kernel_initializer":
tf.keras.initializers.serialize(self._kernel_initializer),
"bias_initializer":
tf.keras.initializers.serialize(self._bias_initializer),
"kernel_regularizer":
tf.keras.regularizers.serialize(self._kernel_regularizer),
"bias_regularizer":
tf.keras.regularizers.serialize(self._bias_regularizer),
}
base_config = super().get_config()
return dict(list(base_config.items()) + list(config.items()))
def _clone_initializer(initializer):
return initializer.__class__.from_config(initializer.get_config())