X = new_X
train_data_provider = LabelledSequenceMinibatchProvider(
X=X[:-500],
Y=Y[:-500],
batch_size=100,
padding='PADDING')
print train_data_provider.batches_per_epoch
n_validation = 500
validation_data_provider = LabelledSequenceMinibatchProvider(
X=X[-n_validation:],
Y=Y[-n_validation:],
batch_size=n_validation,
padding='PADDING')
# ~70% after 300 batches of 100, regularizer L2=1e-4 on tweets100k
#
# model = CSM(
# layers=[
# DictionaryEncoding(vocabulary=encoding),
#
# WordEmbedding( # really a character embedding
# dimension=32,
# vocabulary_size=len(encoding)),
#
# SentenceConvolution(
def optimize_and_save(model, alphabet, n_batches, data_file_name, chars_or_words, result_file_name):
print result_file_name
with gzip.open(data_file_name) as data_file:
data = json.loads(data_file.read())
X, Y = map(list, zip(*data))
# shuffle
combined = zip(X, Y)
random.shuffle(combined)
X, Y = map(list, zip(*combined))
# map labels to something useful
Y = [ [":)", ":("].index(y) for y in Y ]
if chars_or_words == 'chars':
X = [list(x) for x in X]
elif chars_or_words == 'words':
# replace unknowns with an unknown character
tokenizer = WordPunctTokenizer()
new_X = []
for x in X:
new_X.append([w if w in alphabet else 'UNKNOWN' for w in tokenizer.tokenize(x)])
X = new_X
else:
raise ValueError("I don't know what that means :(")
train_data_provider = LabelledSequenceMinibatchProvider(
X=X[:-500],
Y=Y[:-500],
batch_size=50,
padding='PADDING')
validation_data_provider = LabelledSequenceMinibatchProvider(
X=X[-500:],
Y=Y[-500:],
batch_size=500,
padding='PADDING')
cost_function = CrossEntropy()
objective = CostMinimizationObjective(
cost=cost_function,
data_provider=train_data_provider)
update_rule = AdaGrad(
gamma=0.05,
model_template=model)
regularizer = L2Regularizer(lamb=1e-4)
optimizer = SGD(
model=model,
objective=objective,
update_rule=update_rule,
regularizer=regularizer)
print model
monitor_info = []
iteration_info = []
for batch_index, info in enumerate(optimizer):
iteration_info.append(info)
if batch_index % 10 == 0:
X_valid, Y_valid, meta_valid = validation_data_provider.next_batch()
Y_hat = model.fprop(X_valid, meta=meta_valid)
assert np.all(np.abs(Y_hat.sum(axis=1) - 1) < 1e-6)
acc = np.mean(np.argmax(Y_hat, axis=1) == np.argmax(Y_valid, axis=1))
prop_1 = np.argmax(Y_hat, axis=1).mean()
monitor_info.append({
'batch_index': batch_index,
'acc': acc,
'prop_1': prop_1,
})
print "B: {}, A: {}, C: {}, Prop1: {}, Param size: {}".format(
batch_index,
acc, info['cost'],
prop_1,
np.mean(np.abs(model.pack())))
if batch_index == n_batches - 1:
break
result = {
'model': model,
'iteration_info': iteration_info,
'monitor_info': monitor_info,
}
with open(result_file_name, 'w') as result_file:
pickle.dump(result, result_file, protocol=-1)
def main():
random.seed(34532)
np.random.seed(675)
np.set_printoptions(linewidth=100)
data_dir = os.path.join("/users/mdenil/code/txtnets/txtnets_deployed/data",
"stanfordmovie")
trainer = Word2Vec(train=os.path.join(
data_dir, "stanfordmovie.train.sentences.clean.projected.txt"),
output="stanford-movie-vectors.bin",
cbow=1,
size=300,
window=8,
negative=25,
hs=0,
sample=1e-4,
threads=20,
binary=1,
iter=15,
min_count=1)
trainer.train()
gensim_model = gensim.models.Word2Vec.load_word2vec_format(
"/users/mdenil/code/txtnets/txtnets_deployed/code/stanford-movie-vectors.bin",
binary=True)
# print(gensim_model.most_similar(["refund"]))
# print(gensim_model.most_similar(["amazing"]))
embedding_model = txtnets_model_from_gensim_word2vec(gensim_model)
with open(
os.path.join(
data_dir,
"stanfordmovie.train.sentences.clean.projected.flat.json")
) as data_file:
data = json.load(data_file)
random.shuffle(data)
X, Y = map(list, zip(*data))
Y = [[":)", ":("].index(y) for y in Y]
batch_size = 100
n_validation = 500
train_data_provider = LabelledSequenceMinibatchProvider(
X=X[:-n_validation],
Y=Y[:-n_validation],
batch_size=batch_size,
padding='PADDING')
transformed_train_data_provider = TransformedLabelledDataProvider(
data_source=train_data_provider, transformer=embedding_model)
validation_data_provider = LabelledSequenceMinibatchProvider(
X=X[-n_validation:],
Y=Y[-n_validation:],
batch_size=batch_size,
padding='PADDING')
transformed_validation_data_provider = TransformedLabelledDataProvider(
data_source=validation_data_provider, transformer=embedding_model)
logistic_regression = CSM(layers=[
Sum(axes=['w']),
Softmax(n_input_dimensions=gensim_model.syn0.shape[1], n_classes=2)
])
cost_function = CrossEntropy()
regularizer = L2Regularizer(lamb=1e-4)
objective = CostMinimizationObjective(
cost=cost_function,
data_provider=transformed_train_data_provider,
regularizer=regularizer)
update_rule = AdaGrad(gamma=0.1, model_template=logistic_regression)
optimizer = SGD(model=logistic_regression,
objective=objective,
update_rule=update_rule)
for batch_index, iteration_info in enumerate(optimizer):
if batch_index % 100 == 0:
# print(iteration_info['cost'])
Y_hat = []
Y_valid = []
for _ in xrange(
transformed_validation_data_provider.batches_per_epoch):
X_valid_batch, Y_valid_batch, meta_valid = transformed_validation_data_provider.next_batch(
)
Y_valid.append(get(Y_valid_batch))
Y_hat.append(
get(
logistic_regression.fprop(X_valid_batch,
meta=meta_valid)))
Y_valid = np.concatenate(Y_valid, axis=0)
Y_hat = np.concatenate(Y_hat, axis=0)
acc = np.mean(
np.argmax(Y_hat, axis=1) == np.argmax(Y_valid, axis=1))
print("B: {}, A: {}, C: {}".format(batch_index, acc,
iteration_info['cost']))
with open("model_w2vec_logreg.pkl", 'w') as model_file:
pickle.dump(embedding_model.move_to_cpu(),
model_file,
protocol=-1)
pickle.dump(logistic_regression.move_to_cpu(),
model_file,
protocol=-1)
print len(alphabet)
# lists of characters.
# X = [list(x) for x in X]
# lists of words
# replace unknowns with an unknown character
tokenizer = WordPunctTokenizer()
new_X = []
for x in X:
new_X.append(
[w if w in alphabet else 'UNKNOWN' for w in tokenizer.tokenize(x)])
X = new_X
train_data_provider = LabelledSequenceMinibatchProvider(X=X[:-500],
Y=Y[:-500],
batch_size=100,
padding='PADDING')
print train_data_provider.batches_per_epoch
n_validation = 500
validation_data_provider = LabelledSequenceMinibatchProvider(
X=X[-n_validation:],
Y=Y[-n_validation:],
batch_size=n_validation,
padding='PADDING')
# ~70% after 300 batches of 100, regularizer L2=1e-4 on tweets100k
#
# model = CSM(
# layers=[
def optimize_and_save(model, alphabet, n_batches, data_file_name,
chars_or_words, result_file_name):
print result_file_name
with gzip.open(data_file_name) as data_file:
data = json.loads(data_file.read())
X, Y = map(list, zip(*data))
# shuffle
combined = zip(X, Y)
random.shuffle(combined)
X, Y = map(list, zip(*combined))
# map labels to something useful
Y = [[":)", ":("].index(y) for y in Y]
if chars_or_words == 'chars':
X = [list(x) for x in X]
elif chars_or_words == 'words':
# replace unknowns with an unknown character
tokenizer = WordPunctTokenizer()
new_X = []
for x in X:
new_X.append([
w if w in alphabet else 'UNKNOWN'
for w in tokenizer.tokenize(x)
])
X = new_X
else:
raise ValueError("I don't know what that means :(")
train_data_provider = LabelledSequenceMinibatchProvider(X=X[:-500],
Y=Y[:-500],
batch_size=50,
padding='PADDING')
validation_data_provider = LabelledSequenceMinibatchProvider(
X=X[-500:], Y=Y[-500:], batch_size=500, padding='PADDING')
cost_function = CrossEntropy()
objective = CostMinimizationObjective(cost=cost_function,
data_provider=train_data_provider)
update_rule = AdaGrad(gamma=0.05, model_template=model)
regularizer = L2Regularizer(lamb=1e-4)
optimizer = SGD(model=model,
objective=objective,
update_rule=update_rule,
regularizer=regularizer)
print model
monitor_info = []
iteration_info = []
for batch_index, info in enumerate(optimizer):
iteration_info.append(info)
if batch_index % 10 == 0:
X_valid, Y_valid, meta_valid = validation_data_provider.next_batch(
)
Y_hat = model.fprop(X_valid, meta=meta_valid)
assert np.all(np.abs(Y_hat.sum(axis=1) - 1) < 1e-6)
acc = np.mean(
np.argmax(Y_hat, axis=1) == np.argmax(Y_valid, axis=1))
prop_1 = np.argmax(Y_hat, axis=1).mean()
monitor_info.append({
'batch_index': batch_index,
'acc': acc,
'prop_1': prop_1,
})
print "B: {}, A: {}, C: {}, Prop1: {}, Param size: {}".format(
batch_index, acc, info['cost'], prop_1,
np.mean(np.abs(model.pack())))
if batch_index == n_batches - 1:
break
result = {
'model': model,
'iteration_info': iteration_info,
'monitor_info': monitor_info,
}
with open(result_file_name, 'w') as result_file:
pickle.dump(result, result_file, protocol=-1)
