-
Notifications
You must be signed in to change notification settings - Fork 9
/
Copy pathmain.py
279 lines (226 loc) · 10.1 KB
/
main.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
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
import tensorflow as tf
import random
import time
import numpy as np
import sys
import pprint
import optimizee
import nn_opt
import math
import os
import task_list
import test_list
def log(s, filename):
print s
with open(filename, 'a') as fp:
print >>fp, s
def randid():
return ''.join([chr(random.randint(0, 25) + ord('a')) for i in range(6)])
def task_id():
flags = tf.app.flags.FLAGS
return flags.task + "-" + flags.id
def train_optimizer(task):
'''Train an RNN optimizer.
Args:
task: A dictionary in task_list.py which specifies the model of the optimizer and
the tricks and the optimizee to use when training the optimizer.
'''
flags = tf.app.flags.FLAGS
session = tf.get_default_session()
task['optimizee']['train'].build()
n_steps = task['n_steps'] if 'n_steps' in task else flags.n_steps
n_bptt_steps = task['n_bptt_steps'] if 'n_bptt_steps' in task else flags.n_bptt_steps
use_avg_loss = task['use_avg_loss'] if 'use_avg_loss' in task else False
options = task['options'] if 'options' in task else {}
assert n_steps % n_bptt_steps == 0
model = task['model'](
name=task_id(),
optimizee=task['optimizee']['train'],
n_bptt_steps=n_bptt_steps,
use_avg_loss=use_avg_loss,
**options)
model.prepare_train_optimizee(task['optimizee']['tests'])
log_filename = model.name + "_data/log.txt"
session.run(tf.global_variables_initializer())
if flags.eid != 0:
model.restore(flags.eid)
model.bid = flags.eid * flags.n_batches
avg_loss_value = 0.0
avg_final_loss_value = 0.0
eid = flags.eid
while flags.n_epochs == 0 or eid < flags.n_epochs:
eid += 1
loss_values = []
for i in range(flags.n_batches):
ret = model.train_one_iteration(n_steps)
loss_value = ret['loss']
loss_values.append(loss_value)
sys.stdout.write("\r\033[K")
msg = "iteration #%d" % i
msg += ": loss = %.5f avg loss = %.5f" % (loss_value, np.mean(loss_values))
sys.stdout.write(msg)
sys.stdout.flush()
sys.stdout.write("\r\033[K")
msg = "epoch #%d" % eid
msg += ": loss = %.5f" % np.mean(loss_values)
log(msg, log_filename)
log(str(loss_values), log_filename)
if eid % 10 == 0:
model.save(eid)
test_loss_values = {}
for i in range(flags.n_tests):
for name, avg_loss_value, gd_avg_loss_value in model.test(eid):
if name not in test_loss_values:
test_loss_values[name] = {'nn': [], 'gd': []}
test_loss_values[name]['nn'].append(avg_loss_value)
test_loss_values[name]['gd'].append(gd_avg_loss_value)
for name in test_loss_values:
log("epoch #%d test %s: loss = %.5f gd_loss = %.5f" % (eid, name,
np.mean(test_loss_values[name]['nn']), np.mean(test_loss_values[name]['gd'])), log_filename)
def train_optimizee(task):
'''Use traditional optimization algorithm to train an optimizee and get more
information about the gradient each step and the final optimizee parameters.
Args:
task: A dictionary in test_list.py which specifies the optimizee to train,
the optimization algorithm to use and how many steps to train the optimizee.
'''
flags = tf.app.flags.FLAGS
session = tf.get_default_session()
if task['frequency'] == 0:
task['frequency'] = 1
opt = task['optimizee']
opt.build()
all_val_final_loss = []
x = tf.Variable(np.zeros([opt.x_dim]), dtype=tf.float32)
loss = opt.loss(0, x)
gd = task['gd']()
grad = gd.compute_gradients(loss)
train_step = gd.apply_gradients(grad)
for it in range(100):
internal_feed_dict = opt.next_internal_feed_dict()
session.run(tf.global_variables_initializer())
session.run(x.assign(opt.get_initial_x()))
eid = 0
while flags.n_epochs == 0 or eid < flags.n_epochs:
eid += 1
data_dicts = []
for i in range(task['n_steps']):
data_dicts.append(opt.next_feed_dict(1))
loss_values = []
avg_grad_rms = 0
for i in range(task['n_steps']):
feed_dict = internal_feed_dict
feed_dict.update(data_dicts[i])
_, loss_value, grad_value = session.run([train_step, loss, grad[0][0]], feed_dict=feed_dict)
loss_values.append(loss_value)
sys.stdout.write("\r\033[K")
sys.stdout.write("iteration #%d: loss = %.5f grad rms: %.5f grad mean: %.5f grad var: %.5f grad min: %.5f grad max: %.5f" % (i, loss_value, math.sqrt(np.mean(grad_value ** 2)), np.mean(grad_value), math.sqrt(np.var(grad_value)), np.min(grad_value), np.max(grad_value)))
avg_grad_rms += math.sqrt(np.mean(grad_value ** 2))
sys.stdout.flush()
avg_grad_rms /= task['n_steps']
sys.stdout.write("\r\033[K")
val_final_loss = 0.0
for i in range(task['n_steps']):
feed_dict = internal_feed_dict
feed_dict.update(data_dicts[i])
val_final_loss += session.run(loss, feed_dict=feed_dict)
val_final_loss /= task['n_steps']
val_x = x.eval()
print "epoch #%d: loss = %.5f" % (eid, val_final_loss)
print "x mean: %.5f x std_var: %.5f x min: %.5f x max: %.5f grad rms: %.5f" % (np.mean(val_x), math.sqrt(np.var(val_x)), np.min(val_x), np.max(val_x), avg_grad_rms)
all_val_final_loss.append(val_final_loss)
print 'mean final loss = %.5f' % np.mean(all_val_final_loss)
def optimizer_train_optimizee(task):
'''Test a trained RNN optimizer on different optimizees.
Args:
task: A dictionary in task_list.py which specifies the RNN optimizer.
'''
flags = tf.app.flags.FLAGS
session = tf.get_default_session()
test_names = [
'mnist-nn-sigmoid-100',
'mnist-nn-relu-100',
'mnist-nn-sigmoid-2000',
'mnist-nn-sigmoid-10000',
'vgg-mnist-fc1-conv2-pool1-100',
'vgg-cifar-fc1-conv2-pool1-100',
'vgg-mnist-fc2-conv4-pool2-100',
'vgg-cifar-fc2-conv4-pool2-100',
'mnist-nn-elu-100',
'mnist-nn-tanh-100',
'mnist-nn-l2-sigmoid-100',
'mnist-nn-l3-sigmoid-100',
'mnist-nn-l4-sigmoid-100',
'mnist-nn-l5-sigmoid-100',
'mnist-nn-l6-sigmoid-100',
'mnist-nn-l7-sigmoid-100',
'mnist-nn-l8-sigmoid-100',
'mnist-nn-l9-sigmoid-100',
'mnist-nn-l10-sigmoid-100',
'sin_lstm',
'sin_lstm-x2',
'sin_lstm-no001',
]
tests = {}
for name in test_names:
tests[name] = task['optimizee']['tests'][name]
tests[name]['frequency'] = 1
task['optimizee']['train'].build()
n_bptt_steps = task['n_bptt_steps'] if 'n_bptt_steps' in task else flags.n_bptt_steps
use_avg_loss = task['use_avg_loss'] if 'use_avg_loss' in task else False
options = task['options'] if 'options' in task else {}
model = task['model'](
name=task_id(),
optimizee=task['optimizee']['train'],
n_bptt_steps=n_bptt_steps,
use_avg_loss=use_avg_loss,
**options)
model.prepare_train_optimizee(tests)
log_filename = model.name + "_data/log_test.txt"
session.run(tf.global_variables_initializer())
assert flags.eid != 0
model.restore(flags.eid)
log("model %s after epoch #%d" % (task_id(), flags.eid ), log_filename)
eid = 0
test_loss_values = {}
while flags.n_epochs == 0 or eid < flags.n_epochs:
eid += 1
for i in range(flags.n_tests):
for name, avg_loss_value, gd_avg_loss_value in model.test(eid):
if name not in test_loss_values:
test_loss_values[name] = {'nn': [], 'gd': []}
test_loss_values[name]['nn'].append(avg_loss_value)
test_loss_values[name]['gd'].append(gd_avg_loss_value)
for name in sorted(test_loss_values):
log("epoch #%d: test %s: loss = %.5f gd_loss = %.5f" % (eid, name,
np.mean(test_loss_values[name]['nn']), np.mean(test_loss_values[name]['gd'])), log_filename)
def main(argv):
pprint.pprint(tf.app.flags.FLAGS.__flags)
flags = tf.app.flags.FLAGS
graph = tf.Graph()
os.environ["CUDA_VISIBLE_DEVICES"]=str(flags.gpu)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.22, allow_growth=True)
with graph.as_default():
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options),graph=graph) as session:
all_tests = test_list.tests
tasks = task_list.tasks
if flags.train == 'optimizer':
train_optimizer(tasks[flags.task])
elif flags.train == 'optimizee':
train_optimizee(all_tests[flags.task])
elif flags.train == 'optimizer_train_optimizee':
optimizer_train_optimizee(tasks[flags.task])
elif flags.train == 'test':
test(tasks[flags.task])
if __name__ == '__main__':
tf.app.flags.DEFINE_string("task", "temp", "the name of the task")
tf.app.flags.DEFINE_string("id", randid(), "id")
tf.app.flags.DEFINE_integer("gpu", 0, "gpu id")
tf.app.flags.DEFINE_string("train", "optimizer", "optimizer, optimizee, or optimizer_train_optimizee")
tf.app.flags.DEFINE_integer("eid", 0, "the epoch id to continue training")
tf.app.flags.DEFINE_integer("n_steps", 100, "the number of iterations in training RNN optimizer")
tf.app.flags.DEFINE_integer("n_bptt_steps", 20, "the number of iterations in training RNN optimizer")
tf.app.flags.DEFINE_integer("n_batches", 100, "#batches per epoch")
tf.app.flags.DEFINE_integer("n_tests", 1, "#batches per epoch at the test stage")
tf.app.flags.DEFINE_integer("n_epochs", 0, "#epochs")
tf.app.run()