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test_snli_match_lstm_current_acc.py
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test_snli_match_lstm_current_acc.py
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# coding: utf-8
# In[1]:
from __future__ import print_function
import pickle
import sys
import numpy
import numpy as np
import pandas as pd
import theano
import theano.tensor as T
import lasagne
import time
from custom_layers import CustomEmbedding, MatchLSTM, FakeFeatureDot2Layer
# In[2]:
def prepare(df):
seqs_premise = []
seqs_hypothesis = []
for cc in df['sentence1']:
seqs_premise.append(cc)
for cc in df['sentence2']:
seqs_hypothesis.append(cc)
seqs_p = seqs_premise
seqs_h = seqs_hypothesis
lengths_p = [len(s) for s in seqs_p]
lengths_h = [len(s) for s in seqs_h]
n_samples = len(seqs_p)
maxlen_p = numpy.max(lengths_p) + 1
maxlen_h = numpy.max(lengths_h) + 1
premise = numpy.zeros((n_samples, maxlen_p))
hypothesis = numpy.zeros((n_samples, maxlen_h))
premise_masks = numpy.zeros((n_samples, maxlen_p))
hypothesis_masks = numpy.zeros((n_samples, maxlen_h))
for idx, [s_t, s_h] in enumerate(zip(seqs_p, seqs_h)):
assert lengths_h[idx] == len(s_h)
premise[idx, :lengths_p[idx]] = s_t
premise_masks[idx, :lengths_p[idx]] = 1
hypothesis[idx, :lengths_h[idx]] = s_h
hypothesis_masks[idx, :lengths_h[idx]] = 1
labels = []
for gl in df['gold_label']:
if gl == 'entailment':
value = 2
elif gl == 'contradiction':
value = 1
elif gl == 'neutral':
value = 0
else:
raise ValueError('unknown gold_label {0}'.format(gl))
labels.append(value)
labels = np.array(labels)
return (premise.astype('int32'),
premise_masks.astype('int32'),
hypothesis.astype('int32'),
hypothesis_masks.astype('int32'),
labels.astype('int32'))
# In[3]:
print('Loading data ...')
dev_df, test_df = (None, None)
with open('./snli/converted_dev.pkl', 'rb') as f:
print('Loading dev ...')
dev_df = pickle.load(f)
print(len(dev_df))
filtered_s2 = dev_df.sentence2.apply(lambda s2: len(s2) != 0)
dev_df = dev_df[filtered_s2]
print(len(dev_df))
dev_df = dev_df[dev_df.gold_label != '-']
dev_df = dev_df.reset_index()
print(len(dev_df))
with open('./snli/converted_test.pkl', 'rb') as f:
print('Loading test ...')
test_df = pickle.load(f)
print(len(test_df))
filtered_s2 = test_df.sentence2.apply(lambda s2: len(s2) != 0)
test_df = test_df[filtered_s2]
print(len(test_df))
test_df = test_df[test_df.gold_label != '-']
test_df = test_df.reset_index()
print(len(test_df))
# In[7]:
premise_max = 82 + 1
hypothesis_max = 62 + 1
# In[8]:
def main(k=300, batch_size=30,
display_freq=100,
save_freq=1000,
load_previous=False):
print('k: {}'.format(k))
print('batch_size: {}'.format(batch_size))
print('display_frequency: {}'.format(display_freq))
print('save_frequency: {}'.format(save_freq))
print('load previous: {}'.format(load_previous))
save_filename = '/tmp/mlstm_model.npz'
print("Building network ...")
premise_var = T.imatrix('premise_var')
premise_mask = T.imatrix('premise_mask')
hypo_var = T.imatrix('hypo_var')
hypo_mask = T.imatrix('hypo_mask')
unchanged_W = pickle.load(open('./snli/unchanged_W.pkl', 'rb'))
unchanged_W = unchanged_W.astype('float32')
unchanged_W_shape = unchanged_W.shape
oov_in_train_W = pickle.load(open('./snli/oov_in_train_W.pkl', 'rb'))
oov_in_train_W = oov_in_train_W.astype('float32')
oov_in_train_W_shape = oov_in_train_W.shape
print('unchanged_W.shape: {0}'.format(unchanged_W_shape))
print('oov_in_train_W.shape: {0}'.format(oov_in_train_W_shape))
# best hypoparameters
p = 0.3
learning_rate = 0.001
# learning_rate = theano.shared(0.001)
# learning_rate = 0.003
# l2_weight = 0.0003
l2_weight = 0.
l_premise = lasagne.layers.InputLayer(shape=(None, premise_max), input_var=premise_var)
l_premise_mask = lasagne.layers.InputLayer(shape=(None, premise_max), input_var=premise_mask)
l_hypo = lasagne.layers.InputLayer(shape=(None, hypothesis_max), input_var=hypo_var)
l_hypo_mask = lasagne.layers.InputLayer(shape=(None, hypothesis_max), input_var=hypo_mask)
premise_embedding = CustomEmbedding(l_premise, unchanged_W, unchanged_W_shape,
oov_in_train_W, oov_in_train_W_shape,
p=p)
# weights shared with premise_embedding
hypo_embedding = CustomEmbedding(l_hypo, unchanged_W=premise_embedding.unchanged_W,
unchanged_W_shape=unchanged_W_shape,
oov_in_train_W=premise_embedding.oov_in_train_W,
oov_in_train_W_shape=oov_in_train_W_shape,
p=p,
dropout_mask=premise_embedding.dropout_mask)
hypo_embedding = FakeFeatureDot2Layer(hypo_embedding)
mlstm = MatchLSTM(hypo_embedding, k, peepholes=False, mask_input=l_hypo_mask,
encoder_input=premise_embedding, encoder_mask_input=l_premise_mask,
)
p = 0.
if p > 0.:
print('apply dropout rate {} to decoder'.format(p))
mlstm = lasagne.layers.DropoutLayer(mlstm, p)
l_softmax = lasagne.layers.DenseLayer(
mlstm, num_units=3,
nonlinearity=lasagne.nonlinearities.softmax)
if load_previous:
print('loading previous saved model ...')
# And load them again later on like this:
with np.load(save_filename) as f:
param_values = [f['arr_%d' % i] for i in range(len(f.files))]
lasagne.layers.set_all_param_values(l_softmax, param_values)
target_var = T.ivector('target_var')
test_prediction = lasagne.layers.get_output(l_softmax, deterministic=True)
test_loss = lasagne.objectives.categorical_crossentropy(test_prediction,
target_var)
test_loss = test_loss.mean()
# lasagne.objectives.categorical_accuracy()
# As a bonus, also create an expression for the classification accuracy:
test_predict_cls = T.argmax(test_prediction, axis=1)
test_acc = T.mean(T.eq(test_predict_cls, target_var),
dtype=theano.config.floatX)
# Theano functions for training and computing cost
print("Compiling functions ...")
val_fn = theano.function([premise_var, premise_mask, hypo_var, hypo_mask, target_var],
[test_loss, test_acc])
predict_fn = theano.function([premise_var, premise_mask, hypo_var, hypo_mask],
test_predict_cls)
print("Testing ...")
print('dev_df.shape: {0}'.format(dev_df.shape))
print('test_df.shape: {0}'.format(test_df.shape))
try:
print("Starting evaluating...")
# And a full pass over the validation data:
val_err = 0
val_acc = 0
val_batches = 0
for start_i in range(0, len(dev_df), batch_size):
batched_df = dev_df[start_i:start_i + batch_size]
ps, p_masks, hs, h_masks, labels = prepare(batched_df)
err, acc = val_fn(ps, p_masks, hs, h_masks, labels)
val_err += err
val_acc += acc
val_batches += 1
print(" validation loss:\t\t{:.6f}".format(val_err / val_batches))
print(" validation accuracy:\t\t{:.2f} %".format(
val_acc / val_batches * 100))
# After training, we compute and print the test error:
test_err = 0
test_acc = 0
test_batches = 0
for start_i in range(0, len(test_df), batch_size):
batched_df = test_df[start_i:start_i + batch_size]
ps, p_masks, hs, h_masks, labels = prepare(batched_df)
err, acc = val_fn(ps, p_masks, hs, h_masks, labels)
test_err += err
test_acc += acc
test_batches += 1
print(" test loss:\t\t\t{:.6f}".format(test_err / test_batches))
print(" test accuracy:\t\t{:.2f} %".format(
test_acc / test_batches * 100))
# Optionally, you could now dump the network weights to a file like this:
# np.savez('model.npz', *lasagne.layers.get_all_param_values(network))
#
# And load them again later on like this:
# with np.load('model.npz') as f:
# param_values = [f['arr_%d' % i] for i in range(len(f.files))]
# lasagne.layers.set_all_param_values(network, param_values)
except KeyboardInterrupt:
print('exit ...')
# In[9]:
if __name__ == '__main__':
main(batch_size=30, load_previous=True)