-
Notifications
You must be signed in to change notification settings - Fork 600
/
rnn_cell.py
183 lines (154 loc) · 7.11 KB
/
rnn_cell.py
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
from collections import namedtuple
import tensorflow as tf
import tensorflow.contrib.distributions as tfd
import numpy as np
from tf_utils import dense_layer, shape
LSTMAttentionCellState = namedtuple(
'LSTMAttentionCellState',
['h1', 'c1', 'h2', 'c2', 'h3', 'c3', 'alpha', 'beta', 'kappa', 'w', 'phi']
)
class LSTMAttentionCell(tf.nn.rnn_cell.RNNCell):
def __init__(
self,
lstm_size,
num_attn_mixture_components,
attention_values,
attention_values_lengths,
num_output_mixture_components,
bias,
reuse=None,
):
self.reuse = reuse
self.lstm_size = lstm_size
self.num_attn_mixture_components = num_attn_mixture_components
self.attention_values = attention_values
self.attention_values_lengths = attention_values_lengths
self.window_size = shape(self.attention_values, 2)
self.char_len = tf.shape(attention_values)[1]
self.batch_size = tf.shape(attention_values)[0]
self.num_output_mixture_components = num_output_mixture_components
self.output_units = 6*self.num_output_mixture_components + 1
self.bias = bias
@property
def state_size(self):
return LSTMAttentionCellState(
self.lstm_size,
self.lstm_size,
self.lstm_size,
self.lstm_size,
self.lstm_size,
self.lstm_size,
self.num_attn_mixture_components,
self.num_attn_mixture_components,
self.num_attn_mixture_components,
self.window_size,
self.char_len,
)
@property
def output_size(self):
return self.lstm_size
def zero_state(self, batch_size, dtype):
return LSTMAttentionCellState(
tf.zeros([batch_size, self.lstm_size]),
tf.zeros([batch_size, self.lstm_size]),
tf.zeros([batch_size, self.lstm_size]),
tf.zeros([batch_size, self.lstm_size]),
tf.zeros([batch_size, self.lstm_size]),
tf.zeros([batch_size, self.lstm_size]),
tf.zeros([batch_size, self.num_attn_mixture_components]),
tf.zeros([batch_size, self.num_attn_mixture_components]),
tf.zeros([batch_size, self.num_attn_mixture_components]),
tf.zeros([batch_size, self.window_size]),
tf.zeros([batch_size, self.char_len]),
)
def __call__(self, inputs, state, scope=None):
with tf.variable_scope(scope or type(self).__name__, reuse=tf.AUTO_REUSE):
# lstm 1
s1_in = tf.concat([state.w, inputs], axis=1)
cell1 = tf.contrib.rnn.LSTMCell(self.lstm_size)
s1_out, s1_state = cell1(s1_in, state=(state.c1, state.h1))
# attention
attention_inputs = tf.concat([state.w, inputs, s1_out], axis=1)
attention_params = dense_layer(attention_inputs, 3*self.num_attn_mixture_components, scope='attention')
alpha, beta, kappa = tf.split(tf.nn.softplus(attention_params), 3, axis=1)
kappa = state.kappa + kappa / 25.0
beta = tf.clip_by_value(beta, .01, np.inf)
kappa_flat, alpha_flat, beta_flat = kappa, alpha, beta
kappa, alpha, beta = tf.expand_dims(kappa, 2), tf.expand_dims(alpha, 2), tf.expand_dims(beta, 2)
enum = tf.reshape(tf.range(self.char_len), (1, 1, self.char_len))
u = tf.cast(tf.tile(enum, (self.batch_size, self.num_attn_mixture_components, 1)), tf.float32)
phi_flat = tf.reduce_sum(alpha*tf.exp(-tf.square(kappa - u) / beta), axis=1)
phi = tf.expand_dims(phi_flat, 2)
sequence_mask = tf.cast(tf.sequence_mask(self.attention_values_lengths, maxlen=self.char_len), tf.float32)
sequence_mask = tf.expand_dims(sequence_mask, 2)
w = tf.reduce_sum(phi*self.attention_values*sequence_mask, axis=1)
# lstm 2
s2_in = tf.concat([inputs, s1_out, w], axis=1)
cell2 = tf.contrib.rnn.LSTMCell(self.lstm_size)
s2_out, s2_state = cell2(s2_in, state=(state.c2, state.h2))
# lstm 3
s3_in = tf.concat([inputs, s2_out, w], axis=1)
cell3 = tf.contrib.rnn.LSTMCell(self.lstm_size)
s3_out, s3_state = cell3(s3_in, state=(state.c3, state.h3))
new_state = LSTMAttentionCellState(
s1_state.h,
s1_state.c,
s2_state.h,
s2_state.c,
s3_state.h,
s3_state.c,
alpha_flat,
beta_flat,
kappa_flat,
w,
phi_flat,
)
return s3_out, new_state
def output_function(self, state):
params = dense_layer(state.h3, self.output_units, scope='gmm', reuse=tf.AUTO_REUSE)
pis, mus, sigmas, rhos, es = self._parse_parameters(params)
mu1, mu2 = tf.split(mus, 2, axis=1)
mus = tf.stack([mu1, mu2], axis=2)
sigma1, sigma2 = tf.split(sigmas, 2, axis=1)
covar_matrix = [tf.square(sigma1), rhos*sigma1*sigma2,
rhos*sigma1*sigma2, tf.square(sigma2)]
covar_matrix = tf.stack(covar_matrix, axis=2)
covar_matrix = tf.reshape(covar_matrix, (self.batch_size, self.num_output_mixture_components, 2, 2))
mvn = tfd.MultivariateNormalFullCovariance(loc=mus, covariance_matrix=covar_matrix)
b = tfd.Bernoulli(probs=es)
c = tfd.Categorical(probs=pis)
sampled_e = b.sample()
sampled_coords = mvn.sample()
sampled_idx = c.sample()
idx = tf.stack([tf.range(self.batch_size), sampled_idx], axis=1)
coords = tf.gather_nd(sampled_coords, idx)
return tf.concat([coords, tf.cast(sampled_e, tf.float32)], axis=1)
def termination_condition(self, state):
char_idx = tf.cast(tf.argmax(state.phi, axis=1), tf.int32)
final_char = char_idx >= self.attention_values_lengths - 1
past_final_char = char_idx >= self.attention_values_lengths
output = self.output_function(state)
es = tf.cast(output[:, 2], tf.int32)
is_eos = tf.equal(es, np.ones_like(es))
return tf.logical_or(tf.logical_and(final_char, is_eos), past_final_char)
def _parse_parameters(self, gmm_params, eps=1e-8, sigma_eps=1e-4):
pis, sigmas, rhos, mus, es = tf.split(
gmm_params,
[
1*self.num_output_mixture_components,
2*self.num_output_mixture_components,
1*self.num_output_mixture_components,
2*self.num_output_mixture_components,
1
],
axis=-1
)
pis = pis*(1 + tf.expand_dims(self.bias, 1))
sigmas = sigmas - tf.expand_dims(self.bias, 1)
pis = tf.nn.softmax(pis, axis=-1)
pis = tf.where(pis < .01, tf.zeros_like(pis), pis)
sigmas = tf.clip_by_value(tf.exp(sigmas), sigma_eps, np.inf)
rhos = tf.clip_by_value(tf.tanh(rhos), eps - 1.0, 1.0 - eps)
es = tf.clip_by_value(tf.nn.sigmoid(es), eps, 1.0 - eps)
es = tf.where(es < .01, tf.zeros_like(es), es)
return pis, mus, sigmas, rhos, es