-
Notifications
You must be signed in to change notification settings - Fork 4
Expand file tree
/
Copy pathmyclass.py
More file actions
221 lines (184 loc) · 8.18 KB
/
myclass.py
File metadata and controls
221 lines (184 loc) · 8.18 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
#!-*- encoding=utf-8 -*-
from keras.models import Sequential, Model
from keras.layers import *
from keras.optimizers import Adam
from keras.preprocessing.sequence import pad_sequences
from keras.utils import Sequence
from keras.preprocessing.text import Tokenizer
from keras import regularizers
from keras.callbacks import ReduceLROnPlateau, ModelCheckpoint, Callback
from keras.initializers import Ones, Zeros
import numpy as np
import random
import sys
import os
import json
import keras.backend.tensorflow_backend as KTF
import tensorflow as tf
import keras.backend as K
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
sess = tf.Session(config=config)
class Position_Embedding(Layer):
def __init__(self, size=None, mode='sum', **kwargs):
self.size = size
self.mode = mode
super(Position_Embedding, self).__init__(**kwargs)
def call(self, x):
if (self.size == None) or (self.mode == 'sum'):
self.size = int(x.shape[-1])
batch_size,seq_len = K.shape(x)[0],K.shape(x)[1]
position_j = 1. / K.pow(10000., \
2 * K.arange(self.size / 2, dtype='float32' \
) / self.size)
position_j = K.expand_dims(position_j, 0)
position_i = K.cumsum(K.ones_like(x[:,:,0]), 1)-1 #K.arange不支持变长，只好用这种方法生成
position_i = K.expand_dims(position_i, 2)
position_ij = K.dot(position_i, position_j)
position_ij = K.concatenate([K.cos(position_ij), K.sin(position_ij)], 2)
if self.mode == 'sum':
return position_ij + x
elif self.mode == 'concat':
return K.concatenate([position_ij, x], 2)
def compute_output_shape(self, input_shape):
if self.mode == 'sum':
return input_shape
elif self.mode == 'concat':
return (input_shape[0], input_shape[1], input_shape[2]+self.size)
class TiedEmbeddingsTransposed(Layer):
"""Layer for tying embeddings in an output layer.
A regular embedding layer has the shape: V x H (V: size of the vocabulary. H: size of the projected space).
In this layer, we'll go: H x V.
With the same weights than the regular embedding.
In addition, it may have an activation.
# References
- [ Using the Output Embedding to Improve Language Models](https://arxiv.org/abs/1608.05859)
"""
def __init__(self, tied_to=None,
activation=None,
**kwargs):
super(TiedEmbeddingsTransposed, self).__init__(**kwargs)
self.tied_to = tied_to
self.activation = activations.get(activation)
def build(self, input_shape):
self.transposed_weights = K.transpose(self.tied_to.weights[0])
self.built = True
def compute_output_shape(self, input_shape):
return input_shape[0], input_shape[1], K.int_shape(self.tied_to.weights[0])[0]
def call(self, inputs, mask=None):
output = K.dot(inputs, self.transposed_weights)
if self.activation is not None:
output = self.activation(output)
return output
def get_config(self):
config = {'activation': activations.serialize(self.activation)
}
base_config = super(TiedEmbeddingsTransposed, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class Attention(Layer):
#uni-directional self attention for long-range text generation
def __init__(self, nb_head, size_per_head, max_len, **kwargs):
self.nb_head = nb_head
self.size_per_head = size_per_head
self.output_dim = nb_head*size_per_head
self.max_len = max_len
super(Attention, self).__init__(**kwargs)
def build(self, input_shape):
self.WQ = self.add_weight(name='WQ',
shape=(input_shape[0][-1], self.output_dim),
initializer='glorot_uniform',
trainable=True)
self.WK = self.add_weight(name='WK',
shape=(input_shape[1][-1], self.output_dim),
initializer='glorot_uniform',
trainable=True)
self.WV = self.add_weight(name='WV',
shape=(input_shape[2][-1], self.output_dim),
initializer='glorot_uniform',
trainable=True)
super(Attention, self).build(input_shape)
def Mask(self, inputs):
mask = K.eye(self.max_len) #[ml, ml]
mask = K.cumsum(mask, 1) #[ml,ml]
mask = K.expand_dims(mask, axis=0) #[bs, ml, ml]
eye = K.eye(self.max_len)
eye = K.expand_dims(eye, axis=0)
mask = mask - eye
mask = K.expand_dims(mask, axis=1) #[1,1, ml,ml]
mask = K.permute_dimensions(mask, (0,3,2,1))
return inputs - mask * 1e12
def call(self, x):
if len(x) == 3:
Q_seq,K_seq,V_seq = x
Q_len,V_len = None,None
elif len(x) == 5:
Q_seq,K_seq,V_seq,Q_len,V_len = x
Q_seq = K.dot(Q_seq, self.WQ) #[bs, ml, output_dim]
Q_seq = K.reshape(Q_seq, (-1, K.shape(Q_seq)[1], self.nb_head, self.size_per_head)) #[bs, ml, nb_head, size_per_head]
Q_seq = K.permute_dimensions(Q_seq, (0,2,1,3)) #[bs, nb_head, ml, size_per_head]
K_seq = K.dot(K_seq, self.WK) #[bs, ml, output_dim]
K_seq = K.reshape(K_seq, (-1, K.shape(K_seq)[1], self.nb_head, self.size_per_head))
K_seq = K.permute_dimensions(K_seq, (0,2,1,3)) #[bs, nb_head, ml, size_per_head]
V_seq = K.dot(V_seq, self.WV)
V_seq = K.reshape(V_seq, (-1, K.shape(V_seq)[1], self.nb_head, self.size_per_head))
V_seq = K.permute_dimensions(V_seq, (0,2,1,3)) #[bs, nb_head, ml, size_per_head]
A = K.batch_dot(Q_seq, K_seq, axes=[3,3]) / self.size_per_head**0.5 #[bs, nb_head, ml, ml]
A = K.permute_dimensions(A, (0,3,2,1)) #[bs, ml, ml, nb_head]
A = self.Mask(A)
A = K.permute_dimensions(A, (0,3,2,1)) #[bs, nb_head, ml, ml]
A = K.softmax(A)
O_seq = K.batch_dot(A, V_seq, axes=[3,2]) #[bs, nb_head, ml, size_per_head]
# print(O_seq.shape)
O_seq = K.permute_dimensions(O_seq, (0,2,1,3)) #[bs, ml, nb_head, size_per_head]
O_seq = K.reshape(O_seq, (-1, K.shape(O_seq)[1], self.output_dim))
# O_seq = self.Mask(O_seq, Q_len, 'mul')
return O_seq
def compute_output_shape(self, input_shape):
return (input_shape[0][0], input_shape[0][1], self.output_dim)
class LayerNormalization(Layer):
"""
Implementation according to:
"Layer Normalization" by JL Ba, JR Kiros, GE Hinton (2016)
"""
def __init__(self, epsilon=1e-8, **kwargs):
self._epsilon = epsilon
super(LayerNormalization, self).__init__(**kwargs)
def compute_output_shape(self, input_shape):
return input_shape
def build(self, input_shape):
self._g = self.add_weight(
name='gain',
shape=(input_shape[-1],),
initializer=Ones(),
trainable=True
)
self._b = self.add_weight(
name='bias',
shape=(input_shape[-1],),
initializer=Zeros(),
trainable=True
)
super(LayerNormalization, self).build(input_shape)
def call(self, x):
mean = K.mean(x, axis=-1)
std = K.std(x, axis=-1)
if len(x.shape) == 3:
mean = K.permute_dimensions(
K.repeat(mean, x.shape.as_list()[-1]),
[0,2,1]
)
std = K.permute_dimensions(
K.repeat(std, x.shape.as_list()[-1]),
[0,2,1]
)
elif len(x.shape) == 2:
mean = K.reshape(
K.repeat_elements(mean, x.shape.as_list()[-1], 0),
(-1, x.shape.as_list()[-1])
)
std = K.reshape(
K.repeat_elements(mean, x.shape.as_list()[-1], 0),
(-1, x.shape.as_list()[-1])
)
return self._g * (x - mean) / (std + self._epsilon) + self._b