-
Notifications
You must be signed in to change notification settings - Fork 2
/
layer.py
473 lines (431 loc) · 20.1 KB
/
layer.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
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
#! /usr/bin/python
# -*- coding: utf8 -*-
import tensorflow as tf
import time
import numpy as np
from six.moves import xrange
import random
import warnings
from gensim.models import KeyedVectors
set_keep = globals()
set_keep['_layers_name_list'] =[]
set_keep['name_reuse'] = False
try: # For TF12 and later
TF_GRAPHKEYS_VARIABLES = tf.GraphKeys.GLOBAL_VARIABLES
except: # For TF11 and before
TF_GRAPHKEYS_VARIABLES = tf.GraphKeys.VARIABLES
def print_all_variables(train_only=False):
"""Print all trainable and non-trainable variables
without tl.layers.initialize_global_variables(sess)
Parameters
----------
train_only : boolean
If True, only print the trainable variables, otherwise, print all variables.
"""
if train_only:
t_vars = tf.trainable_variables()
print(" [*] printing trainable variables")
else:
try: # TF1.0
t_vars = tf.global_variables()
except: # TF0.12
t_vars = tf.all_variables()
print(" [*] printing global variables")
for idx, v in enumerate(t_vars):
print(" var {:3}: {:15} {}".format(idx, str(v.get_shape()), v.name))
def get_variables_with_name(name, train_only=True, printable=False):
"""Get variable list by a given name scope.
>>> dense_vars = tl.layers.get_variable_with_name('dense', True, True)
"""
print(" [*] geting variables with %s" % name)
# tvar = tf.trainable_variables() if train_only else tf.all_variables()
if train_only:
t_vars = tf.trainable_variables()
else:
try: # TF1.0
t_vars = tf.global_variables()
except: # TF0.12
t_vars = tf.all_variables()
d_vars = [var for var in t_vars if name in var.name]
if printable:
for idx, v in enumerate(d_vars):
print(" got {:3}: {:15} {}".format(idx, v.name, str(v.get_shape())))
return d_vars
def get_layers_with_name(network=None, name="", printable=False):
"""Get layer list in a network by a given name scope.
>>> layers = tl.layers.get_layers_with_name(network, "CNN", True)
"""
assert network is not None
print(" [*] geting layers with %s" % name)
layers = []
i = 0
for layer in network.all_layers:
# print(type(layer.name))
if name in layer.name:
layers.append(layer)
if printable:
# print(layer.name)
print(" got {:3}: {:15} {}".format(i, layer.name, str(layer.get_shape())))
i = i + 1
return layers
def initialize_global_variables(sess=None):
"""Excute ``sess.run(tf.global_variables_initializer())`` for TF12+ or
sess.run(tf.initialize_all_variables()) for TF11.
Parameters
----------
sess : a Session
"""
assert sess is not None
try: # TF12
sess.run(tf.global_variables_initializer())
except: # TF11
sess.run(tf.initialize_all_variables())
## Basic layer
class Layer(object):
"""
The :class:`Layer` class represents a single layer of a neural network. It
should be subclassed when implementing new types of layers.
Because each layer can keep track of the layer(s) feeding into it, a
network's output :class:`Layer` instance can double as a handle to the full
network.
"""
def __init__(
self,
inputs = None,
name ='layer'
):
self.inputs = inputs
if (name in set_keep['_layers_name_list']) and name_reuse == False:
raise Exception("Layer '%s' already exists, please choice other 'name' or reuse this layer\
\nHint : Use different name for different 'Layer' (The name is used to control parameter sharing)" % name)
else:
self.name = name
if name not in ['', None, False]:
set_keep['_layers_name_list'].append(name)
def print_params(self, details=True):
''' Print all info of parameters in the network'''
for i, p in enumerate(self.all_params):
if details:
try:
print(" param {:3}: {:15} (mean: {:<18}, median: {:<18}, std: {:<18}) {}".format(i, str(p.eval().shape), p.eval().mean(), np.median(p.eval()), p.eval().std(), p.name))
except Exception as e:
print(str(e))
raise Exception("Hint: print params details after tl.layers.initialize_global_variables(sess) or use network.print_params(False).")
else:
print(" param {:3}: {:15} {}".format(i, str(p.get_shape()), p.name))
print(" num of params: %d" % self.count_params())
def print_layers(self):
''' Print all info of layers in the network '''
for i, p in enumerate(self.all_layers):
print(" layer %d: %s" % (i, str(p)))
def count_params(self):
''' Return the number of parameters in the network '''
n_params = 0
for i, p in enumerate(self.all_params):
n = 1
# for s in p.eval().shape:
for s in p.get_shape():
try:
s = int(s)
except:
s = 1
if s:
n = n * s
n_params = n_params + n
return n_params
def __str__(self):
print("\nIt is a Layer class")
self.print_params(False)
self.print_layers()
return " Last layer is: %s" % self.__class__.__name__
class Seq2seqWrapper(Layer):
"""Sequence-to-sequence model with attention and for multiple buckets.
Parameters
----------
source_vocab_size : size of the source vocabulary.
target_vocab_size : size of the target vocabulary.
buckets : a list of pairs (I, O), where I specifies maximum input length
that will be processed in that bucket, and O specifies maximum output
length. Training instances that have inputs longer than I or outputs
longer than O will be pushed to the next bucket and padded accordingly.
We assume that the list is sorted, e.g., [(2, 4), (8, 16)].
size : number of units in each layer of the model.
num_layers : number of layers in the model.
max_gradient_norm : gradients will be clipped to maximally this norm.
batch_size : the size of the batches used during training;
the model construction is independent of batch_size, so it can be
changed after initialization if this is convenient, e.g., for decoding.
learning_rate : learning rate to start with.
learning_rate_decay_factor : decay learning rate by this much when needed.
use_lstm : if true, we use LSTM cells instead of GRU cells.
num_samples : number of samples for sampled softmax.
forward_only : if set, we do not construct the backward pass in the model.
name : a string or None
An optional name to attach to this layer.
"""
def __init__(self,
buckets,
size,
num_layers,
max_gradient_norm,
batch_size,
learning_rate,
learning_rate_decay_factor,
vec_file,
use_lstm=False,
num_samples=512,
forward_only=False,
name='wrapper'):
Layer.__init__(self)#, name=name)
self.buckets = buckets
self.batch_size = batch_size
self.learning_rate = tf.Variable(float(learning_rate), trainable=False, name='learning_rate')
self.learning_rate_decay_op = self.learning_rate.assign(
self.learning_rate * learning_rate_decay_factor)
self.global_step = tf.Variable(0, trainable=False, name='global_step')
self.size = size
# =========== Load Vector File ======
self.vec_model = KeyedVectors.load_word2vec_format(vec_file, binary=True)
# =========== Fake output Layer for compute cost ======
# If we use sampled softmax, we need an output projection.
with tf.variable_scope(name) as vs:
output_projection = None
'''
softmax_loss_function = None
# Sampled softmax only makes sense if we sample less than vocabulary size.
if num_samples > 0 and num_samples < self.target_vocab_size:
w = tf.get_variable("proj_w", [size, self.target_vocab_size])
w_t = tf.transpose(w)
b = tf.get_variable("proj_b", [self.target_vocab_size])
output_projection = (w, b)
def sampled_loss(inputs, labels):
labels = tf.reshape(labels, [-1, 1])
return tf.nn.sampled_softmax_loss(w_t, b, inputs, labels, num_samples,
self.target_vocab_size)
#return tf.nn.sampled_softmax_loss(w_t, b, labels, num_samples,
# self.target_vocab_size)
softmax_loss_function = sampled_loss
'''
# ============ Seq Encode Layer =============
# Create the internal multi-layer cell for our RNN.
try: # TF1.0
single_cell = tf.contrib.rnn.GRUCell(size)
except:
single_cell = tf.nn.rnn_cell.GRUCell(size)
if use_lstm:
try: # TF1.0
single_cell = tf.contrib.rnn.BasicLSTMCell(size)
except:
single_cell = tf.nn.rnn_cell.BasicLSTMCell(size)
cell = single_cell
if num_layers > 1:
try: # TF1.0
cell = tf.contrib.rnn.MultiRNNCell([single_cell] * num_layers)
except:
cell = tf.nn.rnn_cell.MultiRNNCell([single_cell] * num_layers)
def seq2seq_f(encoder_inputs, decoder_inputs, do_decode):
#loop_function:
#If not None, this function will be applied to i-th output in order to generate i+1-th input,
#and decoder_inputs will be ignored
if(do_decode==True):
loop_function = lambda prev,i: prev
else:
loop_function = None
'''
return tf.contrib.legacy_seq2seq.tied_rnn_seq2seq(
encoder_inputs, decoder_inputs, cell,
loop_function=loop_function, dtype=tf.float32, scope=None)
'''
return tf.contrib.legacy_seq2seq.basic_rnn_seq2seq(encoder_inputs, decoder_inputs, cell, dtype=tf.float32, scope=None)
#=============================================================
# Feeds for inputs.
self.encoder_inputs = []
self.decoder_inputs = []
self.target_weights = []
#each step for an loop
for i in xrange(buckets[-1][0]): # Last bucket is the biggest one.
self.encoder_inputs.append(tf.placeholder(tf.float32, shape=[batch_size,size],
name="encoder{0}".format(i)))
for i in xrange(buckets[-1][1] + 1):
self.decoder_inputs.append(tf.placeholder(tf.float32, shape=[batch_size,size],
name="decoder{0}".format(i)))
#[decoder_size*batch_size]
self.target_weights.append(tf.placeholder(tf.float32, shape=[batch_size],
name="weight{0}".format(i)))
# Our targets are decoder inputs shifted by one.
targets = [self.decoder_inputs[i + 1]
for i in xrange(len(self.decoder_inputs) - 1)]
self.targets = targets # DH add for debug
# Training outputs and losses.
#x y: (64,100)
if forward_only:
self.outputs, self.losses = tf.contrib.legacy_seq2seq.model_with_buckets(
self.encoder_inputs, self.decoder_inputs, targets,
self.target_weights, buckets, lambda x, y: seq2seq_f(x, y, True),
softmax_loss_function=lambda x,y:tf.losses.cosine_distance(tf.nn.l2_normalize(x,1), tf.nn.l2_normalize(y,1),dim=1))
#softmax_loss_function=lambda x,y:tf.losses.mean_squared_error(tf.nn.l2_normalize(x,0), tf.nn.l2_normalize(y,0)))
#softmax_loss_function=softmax_loss_function)
else:
self.outputs, self.losses = tf.contrib.legacy_seq2seq.model_with_buckets(
self.encoder_inputs, self.decoder_inputs, targets,
self.target_weights, buckets,
lambda x, y: seq2seq_f(x, y, False),
softmax_loss_function=lambda x,y:tf.losses.cosine_distance(tf.nn.l2_normalize(x,1), tf.nn.l2_normalize(y,1),dim=1))
#softmax_loss_function=lambda x,y:tf.losses.mean_squared_error(tf.nn.l2_normalize(x,0), tf.nn.l2_normalize(y,0)))
#softmax_loss_function=softmax_loss_function)
# Gradients and SGD update operation for training the model.
params = tf.trainable_variables()
if not forward_only:
self.gradient_norms = []
self.updates = []
opt = tf.train.GradientDescentOptimizer(self.learning_rate)
for b in xrange(len(buckets)):
gradients = tf.gradients(self.losses[b], params)
clipped_gradients, norm = tf.clip_by_global_norm(gradients,
max_gradient_norm)
self.gradient_norms.append(norm)
self.updates.append(opt.apply_gradients(
zip(clipped_gradients, params), global_step=self.global_step))
'''
params = tf.trainable_variables()
if not forward_only:
self.gradient_norms = []
self.updates = []
opt = tf.train.AdamOptimizer(learning_rate=self.learning_rate)
for b in xrange(len(buckets)):
gradients = tf.gradients(self.losses[b], params)
clipped_gradients, norm = tf.clip_by_global_norm(gradients,max_gradient_norm)
self.gradient_norms.append(norm)
#self.updates.append(opt.apply_gradients(zip(clipped_gradients, params), global_step=self.global_step))
self.updates.append(opt.apply_gradients(zip(gradients, params), global_step=self.global_step))
'''
self.all_params = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)
def step(self, session, encoder_inputs, decoder_inputs, target_weights,
bucket_id, forward_only):
"""Run a step of the model feeding the given inputs.
Parameters
----------
session : tensorflow session to use.
encoder_inputs : list of numpy int vectors to feed as encoder inputs.
decoder_inputs : list of numpy int vectors to feed as decoder inputs.
target_weights : list of numpy float vectors to feed as target weights.
bucket_id : which bucket of the model to use.
forward_only : whether to do the backward step or only forward.
Returns
--------
A triple consisting of gradient norm (or None if we did not do backward),
average perplexity, and the outputs.
Raises
--------
ValueError : if length of encoder_inputs, decoder_inputs, or
target_weights disagrees with bucket size for the specified bucket_id.
"""
# Check if the sizes match.
encoder_size, decoder_size = self.buckets[bucket_id]
if len(encoder_inputs) != encoder_size:
raise ValueError("Encoder length must be equal to the one in bucket,"
" %d != %d." % (len(encoder_inputs), encoder_size))
if len(decoder_inputs) != decoder_size:
raise ValueError("Decoder length must be equal to the one in bucket,"
" %d != %d." % (len(decoder_inputs), decoder_size))
if len(target_weights) != decoder_size:
raise ValueError("Weights length must be equal to the one in bucket,"
" %d != %d." % (len(target_weights), decoder_size))
# Input feed: encoder inputs, decoder inputs, target_weights, as provided.
input_feed = {}
for l in xrange(encoder_size):
#[encoder_size*batch_size]
input_feed[self.encoder_inputs[l].name] = encoder_inputs[l]
for l in xrange(decoder_size):
input_feed[self.decoder_inputs[l].name] = decoder_inputs[l]
#[decoder_size*batch_size]
input_feed[self.target_weights[l].name] = target_weights[l]
# Since our targets are decoder inputs shifted by one, we need one more.
last_target = self.decoder_inputs[decoder_size].name
input_feed[last_target] = decoder_inputs[decoder_size-1]#should be padding,and I hope it is
# Output feed: depends on whether we do a backward step or not.
if not forward_only:
output_feed = [self.updates[bucket_id], # Update Op that does SGD.
self.gradient_norms[bucket_id], # Gradient norm.
self.losses[bucket_id]
] # Loss for this batch.
else:
output_feed = [self.losses[bucket_id]] # Loss for this batch.
for l in xrange(decoder_size): # Output logits.
output_feed.append(self.outputs[bucket_id][l])
outputs = session.run(output_feed, input_feed)
if not forward_only:
return outputs[1], outputs[2], None # Gradient norm, loss, no outputs.
else:
return None, outputs[0], outputs[1:] # No gradient norm, loss, outputs.
def id2vec(self,id):
'''should return the word embedding(shape:a list of length [size])
Parameters
------------
id:the token id should be trans
'''
#print("id2vec")
try:
ret_vec = self.vec_model[str(id)]
except KeyError:
ret_vec = np.asarray([0.0] * self.size)
return ret_vec
def vec2id(self, vec):
"""Return the id whose vector is nearest to the given vec."""
#print("vec2id")
#print(vec[0])
nearest_vecs = self.vec_model.most_similar(positive=[vec[0]], topn=1) # A list of tuples, which have the format of (id, similarity)
#print("nearest")
#print(int(nearest_vecs[0][0]))
return int(nearest_vecs[0][0])
def get_batch(self, data, bucket_id, PAD_ID=0, GO_ID=1, EOS_ID=2, UNK_ID=3):
"""Get a random batch of data from the specified bucket, prepare for step.
Parameters
----------
data : a tuple of size len(self.buckets) in which each element contains
lists of pairs of input and output data that we use to create a batch.
bucket_id : integer, which bucket to get the batch for.
Returns
-------
The triple (encoder_inputs, decoder_inputs, target_weights) for
the constructed batch that has the proper format to call step(...) later.
"""
#print ("get batch")
encoder_size, decoder_size = self.buckets[bucket_id]
encoder_inputs, decoder_inputs = [], []
# Get a random batch of encoder and decoder inputs from data,
# pad them if needed, reverse encoder inputs and add GO to decoder.
for _ in xrange(self.batch_size):
#encoder_input and decoder_input is a single sentence
encoder_input, decoder_input = random.choice(data[bucket_id])
#print ("encoder input")
#print(encoder_input)
encoder_pad = [PAD_ID] * (encoder_size - len(encoder_input))
#encoder_inputs is a list(batch_size elements) of ask sentence(which is list)
#reversed the order of input
encoder_inputs.append(list(reversed(encoder_input + encoder_pad)))
decoder_pad_size = decoder_size - len(decoder_input) - 1
decoder_inputs.append([GO_ID] + decoder_input +
[PAD_ID] * decoder_pad_size)
batch_encoder_inputs, batch_decoder_inputs, batch_weights = [], [], []
#batch_encoder_inputs:shape[encoder_size*batch_size],each element is a size
for length_idx in xrange(encoder_size):
batch_encoder_inputs.append(
#[encoder_inputs[batch_idx][length_idx]
[ self.id2vec(encoder_inputs[batch_idx][length_idx])
for batch_idx in xrange(self.batch_size)])
# Batch decoder inputs are re-indexed decoder_inputs, we create weights.
for length_idx in xrange(decoder_size):
batch_decoder_inputs.append(
#[decoder_inputs[batch_idx][length_idx]
[ self.id2vec(decoder_inputs[batch_idx][length_idx]) #[2.0]*self.size stand for word_vec of word decoder_inputs[batch_idx][length_idx]
for batch_idx in xrange(self.batch_size)])
batch_weight = self.batch_size*[1.0]
for batch_idx in xrange(self.batch_size):
if length_idx < decoder_size - 1:
target = decoder_inputs[batch_idx][length_idx + 1]
if length_idx == decoder_size - 1 or target == PAD_ID:
batch_weight[batch_idx] = 0.0
batch_weights.append(batch_weight)
#batch_weight:[decoder_size*batch_size]
return batch_encoder_inputs, batch_decoder_inputs, batch_weights