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multihead.py
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multihead.py
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from keras import backend as K
from keras.engine.topology import Layer
from keras.layers import activations, initializers, regularizers, constraints, InputSpec
import numpy as np
import math
class MultiHead(Layer):
"""Just your regular densely-connected NN layer.
# Arguments
units: Positive integer, dimensionality of the output space.
activation: Activation function to use
(see [activations](../activations.md)).
If you don't specify anything, no activation is applied
(ie. "linear" activation: `a(x) = x`).
use_bias: Boolean, whether the layer uses a bias vector.
kernel_initializer: Initializer for the `kernel` weights matrix
(see [initializers](../initializers.md)).
bias_initializer: Initializer for the bias vector
(see [initializers](../initializers.md)).
kernel_regularizer: Regularizer function applied to
the `kernel` weights matrix
(see [regularizer](../regularizers.md)).
bias_regularizer: Regularizer function applied to the bias vector
(see [regularizer](../regularizers.md)).
activity_regularizer: Regularizer function applied to
the output of the layer (its "activation").
(see [regularizer](../regularizers.md)).
kernel_constraint: Constraint function applied to
the `kernel` weights matrix
(see [constraints](../constraints.md)).
bias_constraint: Constraint function applied to the bias vector
(see [constraints](../constraints.md)).
# Input shape
nD tensor with shape: `(batch_size, ..., input_dim)`.
The most common situation would be
a 2D input with shape `(batch_size, input_dim)`.
# Output shape
nD tensor with shape: `(batch_size, ..., units)`.
For instance, for a 2D input with shape `(batch_size, input_dim)`,
the output would have shape `(batch_size, units)`.
"""
def __init__(self, units,
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
if 'input_shape' not in kwargs and 'input_dim' in kwargs:
kwargs['input_shape'] = (kwargs.pop('input_dim'),)
super(MultiHead, self).__init__(**kwargs)
self.units = units
self.heads = 8
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)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.input_spec = InputSpec(min_ndim=2)
self.supports_masking = True
self.input_spec = [InputSpec(min_ndim=3), InputSpec(min_ndim=3), InputSpec(min_ndim=3)]
def build(self, input_shape):
self.heads = input_shape[2][-1]//self.units
self.scaling = 1/math.sqrt(self.units)
assert len(input_shape) >= 2
query_dim = input_shape[0][-1]
key_dim = input_shape[1][-1]
value_dim = input_shape[2][-1]
self.query_kernel = self.add_weight(shape=(self.heads, query_dim, self.units),
initializer=self.kernel_initializer,
name='query_kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
self.key_kernel = self.add_weight(shape=(self.heads, key_dim, self.units),
initializer=self.kernel_initializer,
name='key_kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
self.value_kernel = self.add_weight(shape=(self.heads, key_dim, self.units),
initializer=self.kernel_initializer,
name='value_kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
if self.use_bias:
self.query_bias = self.add_weight(shape=(self.heads, self.units),
initializer=self.bias_initializer,
name='query_bias',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
self.key_bias = self.add_weight(shape=(self.heads, self.units),
initializer=self.bias_initializer,
name='key_bias',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else:
self.query_bias = None
self.key_bias = None
super(MultiHead, self).build(input_shape)
def call(self, inputs):
queries, keys, values = inputs
out_list = []
query = K.dot(queries, self.query_kernel)
key = K.dot(keys, self.key_kernel)
value = K.dot(values, self.value_kernel)
if self.use_bias:
query = query + self.query_bias
key = key + self.key_bias
if self.activation is not None:
query = self.scaling*self.activation(query)
for i in range(self.heads):
weights = K.softmax(K.batch_dot(query[:, :, i, :], key[:, :, i, :], axes=[2,2]))
out = K.batch_dot(weights, value[:, :, i, :])
out_list.append(out)
output = K.concatenate(out_list, axis=-1)
return output
def compute_output_shape(self, input_shape):
assert input_shape and len(input_shape) >= 2
assert input_shape[-1]
output_shape = list(input_shape[0])
output_shape[-1] = input_shape[2][-1]
return tuple(output_shape)
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),
'activity_regularizer': regularizers.serialize(self.activity_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint)
}
base_config = super(MultiHead, self).get_config()
return dict(list(base_config.items()) + list(config.items()))