-
Notifications
You must be signed in to change notification settings - Fork 6
/
run_exp.py
411 lines (373 loc) · 15.9 KB
/
run_exp.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
"""Runs deep learning experiments on speech dataset.
Usage:
run_exp.py [--dataset-path=path] [--dataset-name=timit]
[--iterator-type=sentences] [--batch-size=100] [--nframes=13]
[--features=fbank] [--init-lr=0.001] [--epochs=500]
[--network-type=dropout_net] [--trainer-type=adadelta]
[--prefix-output-fname=my_prefix_42] [--debug-test] [--debug-print=lvl]
[--debug-time] [--debug-plot=0]
Options:
-h --help Show this screen
--version Show version
--dataset-path=str A valid path to the dataset
default is timit
--dataset-name=str Name of the dataset (for outputs/saves)
default is "timit"
--iterator-type=str "sentences" | "batch" | "dtw"
default is "sentences"
--batch-size=int Batch size, used only by the batch iterator
default is 100 (unused for "sentences" iterator type)
--nframes=int Number of frames to base the first layer on
default is 13
--features=str "fbank" | "MFCC" (some others are not tested)
default is "fbank"
--init-lr=float Initial learning rate for SGD
default is 0.001 (that is very low intentionally)
--epochs=int Max number of epochs (always early stopping)
default is 500
--network-type=str "dropout*" | "*"
default is "dropout_net"
--trainer-type=str "SGD" | "adagrad" | "adadelta"
default is "adadelta"
--prefix-output-fname=str An additional prefix to the output file name
default is "" (empty string)
--debug-test Flag that activates training on the test set
default is False, using it makes it True
--debug-print=int Level of debug printing. 0: nothing, 1: network
default is 0 2: epochs/iters related
default is False, using it makes it True
--debug-time Flag that activates timing epoch duration
default is False, using it makes it True
--debug-plot=int Level of debug plotting, 1: costs
default is 0 >= 2: gradients & updates
"""
import socket, docopt, cPickle, time, sys, os
import numpy
import prettyplotlib as ppl
import matplotlib.pyplot as plt
import random
from random import shuffle
from prep_timit import load_data
from dataset_iterators import DatasetSentencesIterator, DatasetBatchIterator
from dataset_iterators import DatasetDTWIterator, DatasetDTReWIterator
from layers import Linear, ReLU, SigmoidLayer
from classifiers import LogisticRegression
from nnet_archs import NeuralNet, DropoutNet
DEFAULT_DATASET = '/fhgfs/bootphon/scratch/gsynnaeve/TIMIT/train_dev_test_split'
if socket.gethostname() == "syhws-MacBook-Pro.local":
DEFAULT_DATASET = '/Users/gabrielsynnaeve/postdoc/datasets/TIMIT_train_dev_test'
elif socket.gethostname() == "TODO": # TODO
DEFAULT_DATASET = '/media/bigdata/TIMIT_train_dev_test'
DEBUG = False
def print_mean_weights_biases(params):
for layer_ind, param in enumerate(params):
filler = "weight"
if layer_ind % 2:
filler = "bias"
print("layer %i mean %s values %f and std devs %f" % (layer_ind/2,
filler, numpy.mean(param.eval()), numpy.std(param.eval())))
def plot_costs(cost):
# TODO
pass
def rolling_avg_pgu(iteration, pgu, l):
# (iteration * pgu + l) / (iteration + 1)
assert len(l) == len(pgu)
ll = len(l)/3
params, gparams, updates = l[:ll], l[ll:-ll], l[-ll:]
mpars, mgpars, mupds = pgu[:ll], pgu[ll:-ll], pgu[-ll:]
ii = iteration + 1
return [(iteration * mpars[k] + p) / ii for k, p in enumerate(params)] +\
[(iteration * mgpars[k] + g) / ii for k, g in enumerate(gparams)] +\
[(iteration * mupds[k] + u) / ii for k, u in enumerate(updates)]
def plot_params_gradients_updates(n, l):
# TODO currently works only with THEANO_FLAGS="device=cpu" (not working on
#CudaNDArrays)
def plot_helper(li, ti, p):
fig, ax = plt.subplots(1)
if li % 2:
title = "biases" + ti
ppl.bar(ax, numpy.arange(p.shape[0]), p)
else:
title = "weights" + ti
ppl.pcolormesh(fig, ax, p)
plt.title(title)
plt.savefig(title + ".png")
#ppl.show()
plt.close()
ll = len(l)/3
params, gparams, updates = l[:ll], l[ll:-ll], l[-ll:]
if DEBUG:
print "params"
print params
print "===================="
print "gparams" # TODO find out why not CudaNDArray here
print gparams
print "===================="
print "updates" # TODO find out why not CudaNDArray here
print updates
title_iter = "_%04i" % n
for layer_ind, param in enumerate(params):
title = "_for_layer_" + str(layer_ind/3) + title_iter
plot_helper(layer_ind, title, param)
for layer_ind, gparam in enumerate(gparams):
title = "_gradients_for_layer_" + str(layer_ind/3) + title_iter
plot_helper(layer_ind, title, gparam)
for layer_ind, update in enumerate(updates):
title = "_updates_for_layer_" + str(layer_ind/3) + title_iter
plot_helper(layer_ind, title, update)
def run(dataset_path=DEFAULT_DATASET, dataset_name='timit',
iterator_type=DatasetSentencesIterator, batch_size=100,
nframes=13, features="fbank",
init_lr=0.001, max_epochs=500,
network_type="dropout_net", trainer_type="adadelta",
layers_types=[Linear, ReLU, ReLU, ReLU, LogisticRegression],
layers_sizes=[2400, 2400, 2400, 2400],
dropout_rates=[0.2, 0.5, 0.5, 0.5, 0.5],
recurrent_connections=[],
prefix_fname='',
debug_on_test_only=False,
debug_print=0,
debug_time=False,
debug_plot=0):
"""
FIXME TODO
"""
output_file_name = dataset_name
if prefix_fname != "":
output_file_name = prefix_fname + "_" + dataset_name
output_file_name += "_" + features + str(nframes)
output_file_name += "_" + network_type + "_" + trainer_type
print "output file name:", output_file_name
n_ins = None
n_outs = None
print "loading dataset from", dataset_path
data = load_data(dataset_path, nframes=1, features=features, scaling='normalize', cv_frac='fixed', speakers=False, numpy_array_only=True)
train_set_x, train_set_y = data[0]
valid_set_x, valid_set_y = data[1]
test_set_x, test_set_y = data[2]
assert train_set_x.shape[1] == valid_set_x.shape[1]
assert test_set_x.shape[1] == valid_set_x.shape[1]
print "dataset loaded!"
print "train set size", train_set_x.shape[0]
print "validation set size", valid_set_x.shape[0]
print "test set size", test_set_x.shape[0]
print "phones in train", len(set(train_set_y))
print "phones in valid", len(set(valid_set_y))
print "phones in test", len(set(test_set_y))
n_outs = len(set(train_set_y))
to_int = {}
with open(dataset_name + '_to_int_and_to_state_dicts_tuple.pickle') as f:
to_int, _ = cPickle.load(f)
print "nframes:", nframes
train_set_iterator = iterator_type(train_set_x, train_set_y,
to_int, nframes=nframes, batch_size=batch_size)
valid_set_iterator = iterator_type(valid_set_x, valid_set_y,
to_int, nframes=nframes, batch_size=batch_size)
test_set_iterator = iterator_type(test_set_x, test_set_y,
to_int, nframes=nframes, batch_size=batch_size)
n_ins = test_set_x.shape[1]*nframes
assert n_ins != None
assert n_outs != None
# numpy random generator
numpy_rng = numpy.random.RandomState(123)
print '... building the model'
# TODO the proper network type other than just dropout or not
nnet = None
if "dropout" in network_type:
nnet = DropoutNet(numpy_rng=numpy_rng,
n_ins=n_ins,
layers_types=layers_types,
layers_sizes=layers_sizes,
dropout_rates=dropout_rates,
n_outs=n_outs,
debugprint=debug_print)
else:
nnet = NeuralNet(numpy_rng=numpy_rng,
n_ins=n_ins,
layers_types=layers_types,
layers_sizes=layers_sizes,
n_outs=n_outs,
debugprint=debug_print)
print "Created a neural net as:",
print str(nnet)
# get the training, validation and testing function for the model
print '... getting the training functions'
print trainer_type
train_fn = None
if debug_plot or debug_print:
if trainer_type == "adadelta":
train_fn = nnet.get_adadelta_trainer(debug=True)
elif trainer_type == "adagrad":
train_fn = nnet.get_adagrad_trainer(debug=True)
else:
train_fn = nnet.get_SGD_trainer(debug=True)
else:
if trainer_type == "adadelta":
train_fn = nnet.get_adadelta_trainer()
elif trainer_type == "adagrad":
train_fn = nnet.get_adagrad_trainer()
else:
train_fn = nnet.get_SGD_trainer()
train_scoref = nnet.score_classif(train_set_iterator)
valid_scoref = nnet.score_classif(valid_set_iterator)
test_scoref = nnet.score_classif(test_set_iterator)
data_iterator = train_set_iterator
if debug_on_test_only:
data_iterator = test_set_iterator
train_scoref = test_scoref
print '... training the model'
# early-stopping parameters
patience = 1000 # look as this many examples regardless TODO
patience_increase = 2. # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
best_validation_loss = numpy.inf
test_score = 0.
start_time = time.clock()
done_looping = False
epoch = 0
lr = init_lr
timer = None
if debug_plot:
print_mean_weights_biases(nnet.params)
#with open(output_file_name + 'epoch_0.pickle', 'wb') as f:
# cPickle.dump(nnet, f)
while (epoch < max_epochs) and (not done_looping):
epoch = epoch + 1
avg_costs = []
avg_params_gradients_updates = []
if debug_time:
timer = time.time()
for iteration, (x, y) in enumerate(data_iterator):
avg_cost = 0.
if "delta" in trainer_type: # TODO remove need for this if
avg_cost = train_fn(x, y)
else:
avg_cost = train_fn(x, y, lr)
if type(avg_cost) == list:
avg_costs.append(avg_cost[0])
else:
avg_costs.append(avg_cost)
if debug_print >= 2:
print_mean_weights_biases(nnet.params)
if debug_plot >= 2:
plot_params_gradients_updates(epoch, avg_params_gradients_updates)
if debug_time:
print(' epoch %i took %f seconds' % (epoch, time.time() - timer))
print(' epoch %i, avg costs %f' % \
(epoch, numpy.mean(avg_costs)))
print(' epoch %i, training error %f' % \
(epoch, numpy.mean(train_scoref())))
# TODO update lr(t) = lr(0) / (1 + lr(0) * lambda * t)
# or another scheme for learning rate decay
#with open(output_file_name + 'epoch_' +str(epoch) + '.pickle', 'wb') as f:
# cPickle.dump(nnet, f)
if debug_on_test_only:
continue
# we check the validation loss on every epoch
validation_losses = valid_scoref()
this_validation_loss = numpy.mean(validation_losses) # TODO this is a mean of means (with different lengths)
print(' epoch %i, validation error %f' % \
(epoch, this_validation_loss))
# if we got the best validation score until now
if this_validation_loss < best_validation_loss:
with open(output_file_name + '.pickle', 'wb') as f:
cPickle.dump(nnet, f)
# improve patience if loss improvement is good enough
if (this_validation_loss < best_validation_loss *
improvement_threshold):
patience = max(patience, iteration * patience_increase)
# save best validation score and iteration number
best_validation_loss = this_validation_loss
# test it on the test set
test_losses = test_scoref()
test_score = numpy.mean(test_losses) # TODO this is a mean of means (with different lengths)
print((' epoch %i, test error of best model %f') %
(epoch, test_score))
if patience <= iteration: # TODO correct that
done_looping = True
break
end_time = time.clock()
print(('Optimization complete with best validation score of %f, '
'with test performance %f') %
(best_validation_loss, test_score))
print >> sys.stderr, ('The fine tuning code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time)
/ 60.))
with open(output_file_name + '_final.pickle', 'wb') as f:
cPickle.dump(nnet, f)
if __name__=='__main__':
arguments = docopt.docopt(__doc__, version='run_exp version 0.1')
dataset_path=DEFAULT_DATASET
if arguments['--dataset-path'] != None:
dataset_path = arguments['--dataset-path']
dataset_name = 'timit'
if arguments['--dataset-name'] != None:
dataset_name = arguments['--dataset-name']
iterator_type = DatasetSentencesIterator
if arguments['--iterator-type'] != None:
if "sentences" in arguments['--iterator-type']:
iterator_type = DatasetSentencesIterator
elif "dtw" in arguments['--iterator-type']:
if REDTW:
iterator_type = DatasetDTReWIterator
else:
iterator_type = DatasetDTWIterator
else:
iterator_type = DatasetBatchIterator # TODO
batch_size = 100
if arguments['--batch-size'] != None:
batch_size = int(arguments['--batch-size'])
nframes = 13
if arguments['--nframes'] != None:
nframes = int(arguments['--nframes'])
features = 'fbank'
if arguments['--features'] != None:
features = arguments['--features']
init_lr = 0.001
if arguments['--init-lr'] != None:
init_lr = float(arguments['--init-lr'])
max_epochs = 500
if arguments['--epochs'] != None:
max_epochs = int(arguments['--epochs'])
network_type = 'dropout_net'
if arguments['--network-type'] != None:
network_type = arguments['--network-type']
trainer_type = 'adadelta'
if arguments['--trainer-type'] != None:
trainer_type = arguments['--trainer-type']
prefix_fname = ''
if arguments['--prefix-output-fname'] != None:
prefix_fname = arguments['--prefix-output-fname']
debug_on_test_only = False
if arguments['--debug-test']:
debug_on_test_only = True
debug_print = 0
if arguments['--debug-print']:
debug_print = int(arguments['--debug-print'])
debug_time = False
if arguments['--debug-time']:
debug_time = True
debug_plot = 0
if arguments['--debug-plot']:
debug_plot = int(arguments['--debug-plot'])
run(dataset_path=dataset_path, dataset_name=dataset_name,
iterator_type=iterator_type, batch_size=batch_size,
nframes=nframes, features=features,
init_lr=init_lr, max_epochs=max_epochs,
network_type=network_type, trainer_type=trainer_type,
layers_types=[ReLU, ReLU, ReLU, LogisticRegression],
layers_sizes=[1000, 1000, 1000], # TODO in opts
#dropout_rates=[0., 0.5, 0.5, 0.5, 0.5],
#layers_types=[ReLU, LogisticRegression],
#layers_sizes=[200], # TODO in opts
#layers_types=[LogisticRegression],
#layers_sizes=[], # TODO in opts
recurrent_connections=[], # TODO in opts
prefix_fname=prefix_fname,
debug_on_test_only=debug_on_test_only,
debug_print=debug_print,
debug_time=debug_time,
debug_plot=debug_plot)