forked from negar-rostamzadeh/LSTM-Attention
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main.py
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main.py
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import logging
import os
import time
import numpy as np
import theano.tensor as T
import theano
from blocks.algorithms import (GradientDescent, Adam,
CompositeRule, StepClipping)
from blocks.extensions import FinishAfter, Printing, ProgressBar
from blocks.bricks.cost import CategoricalCrossEntropy, MisclassificationRate
from blocks.extensions.monitoring import (TrainingDataMonitoring,
DataStreamMonitoring)
from blocks.bricks import Rectifier, Softmax, MLP
from blocks.main_loop import MainLoop
from blocks.model import Model
from utils import SaveLog, SaveParams, Glorot
from datasets import get_mnist_video_streams
from blocks.initialization import Constant
from LSTM_attention_model import LSTMAttention
from blocks.monitoring import aggregation
floatX = theano.config.floatX
logger = logging.getLogger('main')
def setup_model():
# shape: T x B x F
input_ = T.tensor3('features')
# shape: B
target = T.lvector('targets')
model = LSTMAttention(dim=500,
mlp_hidden_dims=[400, 4],
batch_size=100,
image_shape=(100, 100),
patch_shape=(28, 28),
weights_init=Glorot(),
biases_init=Constant(0))
model.initialize()
h, c, location, scale = model.apply(input_)
classifier = MLP([Rectifier(), Softmax()], [500, 100, 10],
weights_init=Glorot(),
biases_init=Constant(0))
model.h = h
classifier.initialize()
probabilities = classifier.apply(h[-1])
cost = CategoricalCrossEntropy().apply(target, probabilities)
error_rate = MisclassificationRate().apply(target, probabilities)
location_x_avg = T.mean(location[:, 0])
location_x_avg.name = 'location_x_avg'
location_y_avg = T.mean(location[:, 1])
location_y_avg.name = 'location_y_avg'
scale_x_avg = T.mean(scale[:, 0])
scale_x_avg.name = 'scale_x_avg'
scale_y_avg = T.mean(scale[:, 1])
scale_y_avg.name = 'scale_y_avg'
location_x_std = T.std(location[:, 0])
location_x_std.name = 'location_x_std'
location_y_std = T.std(location[:, 1])
location_y_std.name = 'location_y_std'
scale_x_std = T.std(scale[:, 0])
scale_x_std.name = 'scale_x_std'
scale_y_std = T.std(scale[:, 1])
scale_y_std.name = 'scale_y_std'
monitorings = [error_rate,
location_x_avg, location_y_avg, scale_x_avg, scale_y_avg,
location_x_std, location_y_std, scale_x_std, scale_y_std]
return cost, monitorings
def train(cost, monitorings, batch_size=100, num_epochs=500):
# Setting Loggesetr
timestr = time.strftime("%Y_%m_%d_at_%H_%M")
save_path = 'results/test_' + timestr
log_path = os.path.join(save_path, 'log.txt')
os.makedirs(save_path)
fh = logging.FileHandler(filename=log_path)
fh.setLevel(logging.DEBUG)
logger.addHandler(fh)
# Training
blocks_model = Model(cost)
all_params = blocks_model.parameters
print "Number of found parameters:" + str(len(all_params))
print all_params
# grads = T.grad(cost, all_params)
# from blocks.graph import ComputationGraph
# cg = ComputationGraph(cost)
# f = theano.function(cg.inputs, grads)
# tds, vds = get_mnist_video_streams(100)
# data = tds.get_epoch_iterator().next()
# res = f(data[1], data[0])
# res_norm = [np.mean(np.abs(r)) for r in res]
# params_dicts = blocks_model.get_parameter_dict()
# for e1, e2 in zip(params_dicts, res_norm):
# print str(e1) + ": " + str(e2)
# import ipdb; ipdb.set_trace()
clipping = StepClipping(threshold=np.cast[floatX](20))
adam = Adam(learning_rate=0.0001)
step_rule = CompositeRule([adam, clipping])
training_algorithm = GradientDescent(
cost=cost, parameters=all_params,
step_rule=step_rule)
monitored_variables = [
cost,
aggregation.mean(training_algorithm.total_gradient_norm)] + monitorings
blocks_model = Model(cost)
params_dicts = blocks_model.get_parameter_dict()
for name, param in params_dicts.iteritems():
to_monitor = training_algorithm.gradients[param].norm(2)
to_monitor.name = name + "_grad_norm"
monitored_variables.append(to_monitor)
to_monitor = param.norm(2)
to_monitor.name = name + "_norm"
monitored_variables.append(to_monitor)
train_data_stream, valid_data_stream = get_mnist_video_streams(batch_size)
train_monitoring = TrainingDataMonitoring(
variables=monitored_variables,
prefix="train",
after_epoch=True)
valid_monitoring = DataStreamMonitoring(
variables=monitored_variables,
data_stream=valid_data_stream,
prefix="valid",
after_epoch=True)
main_loop = MainLoop(
algorithm=training_algorithm,
data_stream=train_data_stream,
model=blocks_model,
extensions=[
train_monitoring,
valid_monitoring,
FinishAfter(after_n_epochs=num_epochs),
SaveParams('valid_misclassificationrate_apply_error_rate',
blocks_model, save_path),
SaveLog(save_path, after_epoch=True),
ProgressBar(),
Printing()])
main_loop.run()
def evaluate(model, load_path):
with open(load_path + '/trained_params_best.npz') as f:
loaded = np.load(f)
blocks_model = Model(model)
params_dicts = blocks_model.get_parameter_dict()
params_names = params_dicts.keys()
for param_name in params_names:
param = params_dicts[param_name]
assert param.get_value().shape == loaded[param_name].shape
param.set_value(loaded[param_name])
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO)
cost, monitorings = setup_model()
train(cost, monitorings)