-
Notifications
You must be signed in to change notification settings - Fork 588
/
rnn.py
235 lines (205 loc) · 8.19 KB
/
rnn.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
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
222
223
224
225
226
227
228
229
230
231
232
233
234
235
from __future__ import print_function
import os
import numpy as np
import tensorflow as tf
import drawing
from data_frame import DataFrame
from rnn_cell import LSTMAttentionCell
from rnn_ops import rnn_free_run
from tf_base_model import TFBaseModel
from tf_utils import time_distributed_dense_layer
class DataReader(object):
def __init__(self, data_dir):
data_cols = ['x', 'x_len', 'c', 'c_len']
data = [np.load(os.path.join(data_dir, '{}.npy'.format(i))) for i in data_cols]
self.test_df = DataFrame(columns=data_cols, data=data)
self.train_df, self.val_df = self.test_df.train_test_split(train_size=0.95, random_state=2018)
print('train size', len(self.train_df))
print('val size', len(self.val_df))
print('test size', len(self.test_df))
def train_batch_generator(self, batch_size):
return self.batch_generator(
batch_size=batch_size,
df=self.train_df,
shuffle=True,
num_epochs=10000,
mode='train'
)
def val_batch_generator(self, batch_size):
return self.batch_generator(
batch_size=batch_size,
df=self.val_df,
shuffle=True,
num_epochs=10000,
mode='val'
)
def test_batch_generator(self, batch_size):
return self.batch_generator(
batch_size=batch_size,
df=self.test_df,
shuffle=False,
num_epochs=1,
mode='test'
)
def batch_generator(self, batch_size, df, shuffle=True, num_epochs=10000, mode='train'):
gen = df.batch_generator(
batch_size=batch_size,
shuffle=shuffle,
num_epochs=num_epochs,
allow_smaller_final_batch=(mode == 'test')
)
for batch in gen:
batch['x_len'] = batch['x_len'] - 1
max_x_len = np.max(batch['x_len'])
max_c_len = np.max(batch['c_len'])
batch['y'] = batch['x'][:, 1:max_x_len + 1, :]
batch['x'] = batch['x'][:, :max_x_len, :]
batch['c'] = batch['c'][:, :max_c_len]
yield batch
class rnn(TFBaseModel):
def __init__(
self,
lstm_size,
output_mixture_components,
attention_mixture_components,
**kwargs
):
self.lstm_size = lstm_size
self.output_mixture_components = output_mixture_components
self.output_units = self.output_mixture_components*6 + 1
self.attention_mixture_components = attention_mixture_components
super(rnn, self).__init__(**kwargs)
def parse_parameters(self, z, eps=1e-8, sigma_eps=1e-4):
pis, sigmas, rhos, mus, es = tf.split(
z,
[
1*self.output_mixture_components,
2*self.output_mixture_components,
1*self.output_mixture_components,
2*self.output_mixture_components,
1
],
axis=-1
)
pis = tf.nn.softmax(pis, axis=-1)
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)
return pis, mus, sigmas, rhos, es
def NLL(self, y, lengths, pis, mus, sigmas, rho, es, eps=1e-8):
sigma_1, sigma_2 = tf.split(sigmas, 2, axis=2)
y_1, y_2, y_3 = tf.split(y, 3, axis=2)
mu_1, mu_2 = tf.split(mus, 2, axis=2)
norm = 1.0 / (2*np.pi*sigma_1*sigma_2 * tf.sqrt(1 - tf.square(rho)))
Z = tf.square((y_1 - mu_1) / (sigma_1)) + \
tf.square((y_2 - mu_2) / (sigma_2)) - \
2*rho*(y_1 - mu_1)*(y_2 - mu_2) / (sigma_1*sigma_2)
exp = -1.0*Z / (2*(1 - tf.square(rho)))
gaussian_likelihoods = tf.exp(exp) * norm
gmm_likelihood = tf.reduce_sum(pis * gaussian_likelihoods, 2)
gmm_likelihood = tf.clip_by_value(gmm_likelihood, eps, np.inf)
bernoulli_likelihood = tf.squeeze(tf.where(tf.equal(tf.ones_like(y_3), y_3), es, 1 - es))
nll = -(tf.log(gmm_likelihood) + tf.log(bernoulli_likelihood))
sequence_mask = tf.logical_and(
tf.sequence_mask(lengths, maxlen=tf.shape(y)[1]),
tf.logical_not(tf.is_nan(nll)),
)
nll = tf.where(sequence_mask, nll, tf.zeros_like(nll))
num_valid = tf.reduce_sum(tf.cast(sequence_mask, tf.float32), axis=1)
sequence_loss = tf.reduce_sum(nll, axis=1) / tf.maximum(num_valid, 1.0)
element_loss = tf.reduce_sum(nll) / tf.maximum(tf.reduce_sum(num_valid), 1.0)
return sequence_loss, element_loss
def sample(self, cell):
initial_state = cell.zero_state(self.num_samples, dtype=tf.float32)
initial_input = tf.concat([
tf.zeros([self.num_samples, 2]),
tf.ones([self.num_samples, 1]),
], axis=1)
return rnn_free_run(
cell=cell,
sequence_length=self.sample_tsteps,
initial_state=initial_state,
initial_input=initial_input,
scope='rnn'
)[1]
def primed_sample(self, cell):
initial_state = cell.zero_state(self.num_samples, dtype=tf.float32)
primed_state = tf.nn.dynamic_rnn(
inputs=self.x_prime,
cell=cell,
sequence_length=self.x_prime_len,
dtype=tf.float32,
initial_state=initial_state,
scope='rnn'
)[1]
return rnn_free_run(
cell=cell,
sequence_length=self.sample_tsteps,
initial_state=primed_state,
scope='rnn'
)[1]
def calculate_loss(self):
self.x = tf.placeholder(tf.float32, [None, None, 3])
self.y = tf.placeholder(tf.float32, [None, None, 3])
self.x_len = tf.placeholder(tf.int32, [None])
self.c = tf.placeholder(tf.int32, [None, None])
self.c_len = tf.placeholder(tf.int32, [None])
self.sample_tsteps = tf.placeholder(tf.int32, [])
self.num_samples = tf.placeholder(tf.int32, [])
self.prime = tf.placeholder(tf.bool, [])
self.x_prime = tf.placeholder(tf.float32, [None, None, 3])
self.x_prime_len = tf.placeholder(tf.int32, [None])
self.bias = tf.placeholder_with_default(
tf.zeros([self.num_samples], dtype=tf.float32), [None])
cell = LSTMAttentionCell(
lstm_size=self.lstm_size,
num_attn_mixture_components=self.attention_mixture_components,
attention_values=tf.one_hot(self.c, len(drawing.alphabet)),
attention_values_lengths=self.c_len,
num_output_mixture_components=self.output_mixture_components,
bias=self.bias
)
self.initial_state = cell.zero_state(tf.shape(self.x)[0], dtype=tf.float32)
outputs, self.final_state = tf.nn.dynamic_rnn(
inputs=self.x,
cell=cell,
sequence_length=self.x_len,
dtype=tf.float32,
initial_state=self.initial_state,
scope='rnn'
)
params = time_distributed_dense_layer(outputs, self.output_units, scope='rnn/gmm')
pis, mus, sigmas, rhos, es = self.parse_parameters(params)
sequence_loss, self.loss = self.NLL(self.y, self.x_len, pis, mus, sigmas, rhos, es)
self.sampled_sequence = tf.cond(
self.prime,
lambda: self.primed_sample(cell),
lambda: self.sample(cell)
)
return self.loss
if __name__ == '__main__':
dr = DataReader(data_dir='data/processed/')
nn = rnn(
reader=dr,
log_dir='logs',
checkpoint_dir='checkpoints',
prediction_dir='predictions',
learning_rates=[.0001, .00005, .00002],
batch_sizes=[32, 64, 64],
patiences=[1500, 1000, 500],
beta1_decays=[.9, .9, .9],
validation_batch_size=32,
optimizer='rms',
num_training_steps=100000,
warm_start_init_step=0,
regularization_constant=0.0,
keep_prob=1.0,
enable_parameter_averaging=False,
min_steps_to_checkpoint=2000,
log_interval=20,
grad_clip=10,
lstm_size=400,
output_mixture_components=20,
attention_mixture_components=10
)
nn.fit()