def main():
random.seed(665243)
np.random.seed(61734)
np.set_printoptions(linewidth=100)
parser = argparse.ArgumentParser(description="Create summaries from w2vec model.")
parser.add_argument('--size', type=int, help="number of sentences to keep")
args = parser.parse_args()
data_dir = os.path.join("/users/mdenil/code/txtnets/txtnets_deployed/data", "stanfordmovie")
with open("model_w2vec_logreg.pkl") as model_file:
embedding_model = pickle.load(model_file)
logistic_regression = pickle.load(model_file)
with open(os.path.join(data_dir, "stanfordmovie.test.sentences.clean.projected.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]
objective = CrossEntropy()
test_data_provider = LabelledDocumentMinibatchProvider(
X=X,
Y=Y,
batch_size=1,
padding=None,
shuffle=False)
prog_bar = pyprind.ProgBar(test_data_provider.batches_per_epoch)
summaries = []
for _ in range(test_data_provider.batches_per_epoch):
x_batch, y_batch, meta_batch = test_data_provider.next_batch()
label = [":)", ":("][int(y_batch[0,1])]
sentence_importance_scores = get_sentence_importance_scores(
embedding_model, logistic_regression, x_batch)
most_important_sentence_indexes = np.argsort(sentence_importance_scores)
most_important_sentence_indexes = most_important_sentence_indexes[:args.size]
most_important_sentence_indexes.sort()
summary = []
for i in most_important_sentence_indexes:
summary.append(x_batch[i])
summaries.append([summary, label])
prog_bar.update()
with open("summaries_{}.json".format(args.size), 'w') as summaries_file:
json.dump(summaries, summaries_file)
summaries_file.write("\n")
def main():
random.seed(665243)
np.random.seed(61734)
np.set_printoptions(linewidth=100)
parser = argparse.ArgumentParser(description="Create summaries from w2vec model.")
parser.add_argument("--size", type=int, help="number of sentences to keep")
args = parser.parse_args()
data_dir = os.path.join("/users/mdenil/code/txtnets/txtnets_deployed/data", "stanfordmovie")
with open("model_w2vec_logreg.pkl") as model_file:
embedding_model = pickle.load(model_file)
logistic_regression = pickle.load(model_file)
with open(os.path.join(data_dir, "stanfordmovie.test.sentences.clean.projected.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]
objective = CrossEntropy()
test_data_provider = LabelledDocumentMinibatchProvider(X=X, Y=Y, batch_size=1, padding=None, shuffle=False)
prog_bar = pyprind.ProgBar(test_data_provider.batches_per_epoch)
summaries = []
for _ in range(test_data_provider.batches_per_epoch):
x_batch, y_batch, meta_batch = test_data_provider.next_batch()
label = [":)", ":("][int(y_batch[0, 1])]
sentence_importance_scores = get_sentence_importance_scores(embedding_model, logistic_regression, x_batch)
most_important_sentence_indexes = np.argsort(sentence_importance_scores)
most_important_sentence_indexes = most_important_sentence_indexes[: args.size]
most_important_sentence_indexes.sort()
summary = []
for i in most_important_sentence_indexes:
summary.append(x_batch[i])
summaries.append([summary, label])
prog_bar.update()
with open("summaries_{}.json".format(args.size), "w") as summaries_file:
json.dump(summaries, summaries_file)
summaries_file.write("\n")
batch_size = 25
train_data_provider = LabelledDocumentMinibatchProvider(
X=X[:-n_validation],
Y=Y[:-n_validation],
batch_size=batch_size,
padding='PADDING',
fixed_n_sentences=15,
fixed_n_words=50)
print train_data_provider.batches_per_epoch
validation_data_provider = LabelledDocumentMinibatchProvider(
X=X[-n_validation:],
Y=Y[-n_validation:],
batch_size=batch_size,
padding='PADDING',
fixed_n_sentences=15,
fixed_n_words=50)
model = CSM(
layers=[
DictionaryEncoding(vocabulary=encoding),
WordEmbedding(
dimension=20,
vocabulary_size=len(encoding),
padding=encoding['PADDING']),
Dropout(('b', 'w', 'f'), 0.2),
def run():
with open("{{train_data_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]
with open("{{train_encoding_json}}") as encoding_file:
encoding = json.load(encoding_file)
n_validation = {{n_validation}}
batch_size = {{batch_size}}
train_data_provider = LabelledDocumentMinibatchProvider(
X=X[:-n_validation],
Y=Y[:-n_validation],
batch_size=batch_size,
padding='PADDING',
fixed_n_sentences={{fixed_n_sentences}},
fixed_n_words={{fixed_n_words}})
print train_data_provider.batches_per_epoch
validation_data_provider = LabelledDocumentMinibatchProvider(
X=X[-n_validation:],
Y=Y[-n_validation:],
batch_size=batch_size,
padding='PADDING',
fixed_n_sentences={{fixed_n_sentences}},
fixed_n_words={{fixed_n_words}})
model = experiment_config.get_model(encoding)
print model
cost_function = CrossEntropy()
regularizer = L2Regularizer(lamb={{regularizer}})
objective = CostMinimizationObjective(cost=cost_function,
data_provider=train_data_provider,
regularizer=regularizer)
update_rule = AdaGrad(gamma={{adagrad_gamma}}, model_template=model)
optimizer = SGD(model=model, objective=objective, update_rule=update_rule)
n_epochs = {{n_epochs}}
n_batches = train_data_provider.batches_per_epoch * n_epochs
time_start = time.time()
best_acc = -1.0
progress = []
for batch_index, iteration_info in enumerate(optimizer):
if batch_index % {{validation_frequency}} == 0:
model_nodropout = cpu.model.dropout.remove_dropout(model)
Y_hat = []
Y_valid = []
for _ in xrange(validation_data_provider.batches_per_epoch):
X_valid_batch, Y_valid_batch, meta_valid = validation_data_provider.next_batch(
)
X_valid_batch = X_valid_batch
Y_valid_batch = Y_valid_batch
Y_valid.append(Y_valid_batch)
Y_hat.append(
model_nodropout.fprop(X_valid_batch, meta=meta_valid))
Y_valid = np.concatenate(Y_valid, axis=0)
Y_hat = np.concatenate(Y_hat, axis=0)
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))
if acc > best_acc:
best_acc = acc
with open(os.path.join("{{job_dir}}", "model_best.pkl"),
'w') as model_file:
pickle.dump(model, model_file, protocol=-1)
if batch_index % {{save_frequency}} == 0:
with open(
os.path.join("{{job_dir}}",
"model_{:05}.pkl".format(batch_index)),
'w') as model_file:
pickle.dump(model, model_file, protocol=-1)
print "B: {}, A: {}, C: {}, Prop1: {}, Param size: {}, best: {}".format(
batch_index, acc, iteration_info['cost'],
np.argmax(Y_hat, axis=1).mean(), np.mean(np.abs(model.pack())),
best_acc)
time_now = time.time()
examples_per_hr = (batch_index * batch_size) / (time_now -
time_start) * 3600
progress.append({
'batch': batch_index,
'validation_accuracy': acc,
'best_validation_accuracy': best_acc,
'cost': iteration_info['cost'],
'examples_per_hr': examples_per_hr,
})
with open(os.path.join("{{job_dir}}", "progress.pkl"),
'w') as progress_file:
pickle.dump(progress, progress_file, protocol=-1)
if batch_index >= n_batches:
break
time_end = time.time()
print "Time elapsed: {}s".format(time_end - time_start)
def run():
with open("{{train_data_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]
with open("{{train_encoding_json}}") as encoding_file:
encoding = json.load(encoding_file)
n_validation = {{n_validation}}
batch_size = {{batch_size}}
train_data_provider = LabelledDocumentMinibatchProvider(
X=X[:-n_validation],
Y=Y[:-n_validation],
batch_size=batch_size,
padding='PADDING',
fixed_n_sentences={{fixed_n_sentences}},
fixed_n_words={{fixed_n_words}})
print train_data_provider.batches_per_epoch
validation_data_provider = LabelledDocumentMinibatchProvider(
X=X[-n_validation:],
Y=Y[-n_validation:],
batch_size=batch_size,
padding='PADDING',
fixed_n_sentences={{fixed_n_sentences}},
fixed_n_words={{fixed_n_words}})
model = experiment_config.get_model(encoding)
print model
cost_function = CrossEntropy()
regularizer = L2Regularizer(lamb={{regularizer}})
objective = CostMinimizationObjective(
cost=cost_function,
data_provider=train_data_provider,
regularizer=regularizer)
update_rule = AdaGrad(
gamma={{adagrad_gamma}},
model_template=model)
optimizer = SGD(
model=model,
objective=objective,
update_rule=update_rule)
n_epochs = {{n_epochs}}
n_batches = train_data_provider.batches_per_epoch * n_epochs
time_start = time.time()
best_acc = -1.0
progress = []
for batch_index, iteration_info in enumerate(optimizer):
if batch_index % {{validation_frequency}} == 0:
model_nodropout = cpu.model.dropout.remove_dropout(model)
Y_hat = []
Y_valid = []
for _ in xrange(validation_data_provider.batches_per_epoch):
X_valid_batch, Y_valid_batch, meta_valid = validation_data_provider.next_batch()
X_valid_batch = X_valid_batch
Y_valid_batch = Y_valid_batch
Y_valid.append(Y_valid_batch)
Y_hat.append(model_nodropout.fprop(X_valid_batch, meta=meta_valid))
Y_valid = np.concatenate(Y_valid, axis=0)
Y_hat = np.concatenate(Y_hat, axis=0)
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))
if acc > best_acc:
best_acc = acc
with open(os.path.join("{{job_dir}}", "model_best.pkl"), 'w') as model_file:
pickle.dump(model, model_file, protocol=-1)
if batch_index % {{save_frequency}} == 0:
with open(os.path.join("{{job_dir}}", "model_{:05}.pkl".format(batch_index)), 'w') as model_file:
pickle.dump(model, model_file, protocol=-1)
print "B: {}, A: {}, C: {}, Prop1: {}, Param size: {}, best: {}".format(
batch_index,
acc,
iteration_info['cost'],
np.argmax(Y_hat, axis=1).mean(),
np.mean(np.abs(model.pack())),
best_acc)
time_now = time.time()
examples_per_hr = (batch_index * batch_size) / (time_now - time_start) * 3600
progress.append({
'batch': batch_index,
'validation_accuracy': acc,
'best_validation_accuracy': best_acc,
'cost': iteration_info['cost'],
'examples_per_hr': examples_per_hr,
})
with open(os.path.join("{{job_dir}}", "progress.pkl"), 'w') as progress_file:
pickle.dump(progress, progress_file, protocol=-1)
if batch_index >= n_batches:
break
time_end = time.time()
print "Time elapsed: {}s".format(time_end - time_start)
with open(
os.path.join(
data_dir,
"stanfordmovie.train.sentences.clean.dictionary.encoding.json")
) as encoding_file:
encoding = json.load(encoding_file)
print len(encoding)
n_validation = 500
batch_size = 25
train_data_provider = LabelledDocumentMinibatchProvider(
X=X[:-n_validation],
Y=Y[:-n_validation],
batch_size=batch_size,
padding='PADDING',
fixed_n_sentences=15,
fixed_n_words=50)
print train_data_provider.batches_per_epoch
validation_data_provider = LabelledDocumentMinibatchProvider(
X=X[-n_validation:],
Y=Y[-n_validation:],
batch_size=batch_size,
padding='PADDING',
fixed_n_sentences=15,
fixed_n_words=50)
model = CSM(layers=[