def wrapper_ngram(data=TREC, resplit=True, validate_ratio=0.2):
train_x, train_y, validate_x, validate_y, test_x, test_y, \
W, mask = prepare_datasets(data, resplit=resplit, validation_ratio=validate_ratio)
# get input shape
input_shape = (train_x[0].shape[0], W.shape[1])
print "input data shape", input_shape
n_out = len(np.unique(test_y))
shuffle_indices = np.random.permutation(train_x.shape[0])
datasets = (train_x[shuffle_indices], train_y[shuffle_indices], validate_x, validate_y, test_x, test_y)
test_accuracy = train_ngram_net(
U=W,
datasets=datasets,
n_epochs=10,
ngrams=(3, 2),
ngram_out=(150, 50),
non_static=False,
input_shape=input_shape,
concat_out=True,
n_kernels=(8, 16),
use_bias=False,
lr_rate=0.02,
dropout=True,
dropout_rate=0.5,
n_hidden=600,
n_out=n_out,
ngram_activation=leaky_relu,
activation=leaky_relu,
batch_size=50,
l2_ratio=1e-5,
update_rule='adagrad',
skip_gram=False,
)
return test_accuracy
def error_analysis(data=SST_SENT_POL):
train_x, train_y, validate_x, validate_y, test_x, test_y, \
W, mask = prepare_datasets(data, resplit=False, validation_ratio=0.0)
# get input shape
input_shape = (train_x[0].shape[0], W.shape[1])
print "input data shape", input_shape
n_out = len(np.unique(test_y))
shuffle_indices = np.random.permutation(train_x.shape[0])
datasets = (train_x[shuffle_indices], train_y[shuffle_indices], validate_x, validate_y, test_x, test_y)
best_prediction = train_ngram_net(
U=W,
datasets=datasets,
n_epochs=10,
ngrams=(1, 2),
ngram_out=(300, 250),
non_static=False,
input_shape=input_shape,
concat_out=False,
n_kernels=(4, 4),
use_bias=False,
lr_rate=0.02,
dropout=True,
dropout_rate=0.2,
n_hidden=250,
n_out=n_out,
ngram_activation=leaky_relu,
activation=leaky_relu,
batch_size=50,
l2_ratio=1e-5,
update_rule='adagrad',
skip_gram=False,
predict=True
)
raw_datasets = load_raw_datasets(datasets=data)
_, _, validate_raw, _, _, _ = raw_datasets
from collections import Counter
errors = []
for i in xrange(len(best_prediction)):
if best_prediction[i] != validate_y[i]:
errors.append("%d & %d" % (validate_y[i], best_prediction[i]))
print validate_y[i], best_prediction[i], " ".join(validate_raw[i])
errors = Counter(errors)
print errors.most_common(10)
