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* Add bucketing test * Skip pylint * Use cpu to train
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# pylint: skip-file | ||
import numpy as np | ||
import mxnet as mx | ||
import random | ||
from random import randint | ||
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def test_bucket_module(): | ||
import logging | ||
head = '%(asctime)-15s %(message)s' | ||
logging.basicConfig(level=logging.DEBUG, format=head) | ||
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class DummySentenceIter(mx.rnn.BucketSentenceIter): | ||
"""Dummy sentence iterator to output sentences the same as input. | ||
""" | ||
def __init__(self, sentences, batch_size, buckets=None, invalid_label=-1, | ||
data_name='data', label_name='l2_label', dtype='float32', | ||
layout='NTC'): | ||
super(DummySentenceIter, self).__init__(sentences, batch_size, | ||
buckets=buckets, invalid_label=invalid_label, | ||
data_name=data_name, label_name=label_name, | ||
dtype=dtype, layout=layout) | ||
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def reset(self): | ||
"""Resets the iterator to the beginning of the data.""" | ||
self.curr_idx = 0 | ||
random.shuffle(self.idx) | ||
for buck in self.data: | ||
np.random.shuffle(buck) | ||
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self.nddata = [] | ||
self.ndlabel = [] | ||
for buck in self.data: | ||
self.nddata.append(mx.nd.array(buck, dtype=self.dtype)) | ||
self.ndlabel.append(mx.nd.array(buck, dtype=self.dtype)) | ||
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batch_size = 128 | ||
num_epochs = 20 | ||
num_hidden = 50 | ||
num_embed = 50 | ||
num_layers = 2 | ||
len_vocab = 100 | ||
buckets = [10, 20, 30, 40, 50, 60] | ||
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invalid_label = 0 | ||
num_sentence = 2500 | ||
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train_sent = [] | ||
val_sent = [] | ||
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for _ in range(num_sentence): | ||
len_sentence = randint(1, max(buckets) + 10) | ||
train_sentence = [] | ||
val_sentence = [] | ||
for _ in range(len_sentence): | ||
train_sentence.append(randint(1, len_vocab)) | ||
val_sentence.append(randint(1, len_vocab)) | ||
train_sent.append(train_sentence) | ||
val_sent.append(val_sentence) | ||
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data_train = DummySentenceIter(train_sent, batch_size, buckets=buckets, | ||
invalid_label=invalid_label) | ||
data_val = DummySentenceIter(val_sent, batch_size, buckets=buckets, | ||
invalid_label=invalid_label) | ||
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stack = mx.rnn.SequentialRNNCell() | ||
for i in range(num_layers): | ||
stack.add(mx.rnn.LSTMCell(num_hidden=num_hidden, prefix='lstm_l%d_'%i)) | ||
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def sym_gen(seq_len): | ||
data = mx.sym.Variable('data') | ||
label = mx.sym.Variable('l2_label') | ||
embed = mx.sym.Embedding(data=data, input_dim=len_vocab, | ||
output_dim=num_embed, name='embed') | ||
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stack.reset() | ||
outputs, states = stack.unroll(seq_len, inputs=embed, merge_outputs=True) | ||
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pred = mx.sym.Reshape(outputs, shape=(-1, num_hidden)) | ||
pred = mx.sym.FullyConnected(data=pred, num_hidden=1, name='pred') | ||
pred = mx.sym.reshape(pred, shape= (batch_size, -1)) | ||
loss = mx.sym.LinearRegressionOutput(pred, label, name='l2_loss') | ||
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return loss, ('data',), ('l2_label',) | ||
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contexts = mx.cpu(0) | ||
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model = mx.mod.BucketingModule( | ||
sym_gen = sym_gen, | ||
default_bucket_key = data_train.default_bucket_key, | ||
context = contexts) | ||
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model.fit( | ||
train_data = data_train, | ||
eval_data = data_val, | ||
eval_metric = mx.metric.MSE(), | ||
kvstore = 'device', | ||
optimizer = 'sgd', | ||
optimizer_params = { 'learning_rate': 0.01, | ||
'momentum': 0, | ||
'wd': 0.00001 }, | ||
initializer = mx.init.Xavier(factor_type="in", magnitude=2.34), | ||
num_epoch = num_epochs, | ||
batch_end_callback = mx.callback.Speedometer(batch_size, 50)) | ||
assert model.score(data_val, mx.metric.MSE())[0][1] < 15, "High mean square error." | ||
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if __name__ == "__main__": | ||
test_bucket_module() |