print err
#print "Analytic %.9f, Numerical %.9f, Relative Error %.9f" % (dL[j][i], numGrad, err)
err2 += err
count += 1
if 0.001 > err2:
print "Grad Check Passed for dL"
else:
print "Grad Check Failed for dL: Sum of Error = %.9f" % (err2 / count)
if __name__ == '__main__':
import loadTree as tree
train = tree.load_trees('./data/train.json', tree.aspect_label)
training_word_map = tree.load_word_map()
numW = len(training_word_map)
tree.convert_trees(train, training_word_map)
wvecDim = 10
outputDim = 5
memDim = 15
rnn = TreeLSTM(wvecDim, memDim, outputDim, numW, mb_size=4)
rnn.init_params()
mbData = train[:10]
print "Numerical gradient check..."
rnn.check_grad(mbData)
def run(args=None):
usage = "usage : %prog [options]"
parser = optparse.OptionParser(usage=usage)
parser.add_option("--test", action="store_true", dest="test", default=False)
# Optimizer
parser.add_option("--minibatch", dest="minibatch", type="int", default=30)
parser.add_option("--optimizer", dest="optimizer", type="string", default="adagrad")
parser.add_option("--epochs", dest="epochs", type="int", default=50)
parser.add_option("--step", dest="step", type="float", default=5e-2)
parser.add_option("--rho", dest="rho", type="float", default=1e-3)
# Dimension
parser.add_option("--wvecDim", dest="wvec_dim", type="int", default=30)
parser.add_option("--memDim", dest="mem_dim", type="int", default=30)
parser.add_option("--outFile", dest="out_file", type="string", default="models/test.bin")
parser.add_option("--inFile", dest="in_file", type="string", default="models/test.bin")
parser.add_option("--data", dest="data", type="string", default="train")
parser.add_option("--model", dest="model", type="string", default="RNN")
parser.add_option("--label", dest="label_method", type="string", default="rating")
(opts, args) = parser.parse_args(args)
evaluate_accuracy_while_training = True
if opts.label_method == 'rating':
label_method = tree.rating_label
opts.output_dim = 5
elif opts.label_method == 'aspect':
label_method = tree.aspect_label
opts.output_dim = 5
elif opts.label_method == 'pair':
label_method = tree.pair_label
opts.output_dim = 25
else:
raise '%s is not a valid labelling method.' % opts.label_method
# Testing
if opts.test:
test(opts.in_file, opts.data, label_method, opts.model)
return
print "Loading data..."
train_accuracies = []
dev_accuracies = []
# load training data
trees = tree.load_trees('./data/train.json', label_method)
training_word_map = tree.load_word_map()
opts.num_words = len(training_word_map)
tree.convert_trees(trees, training_word_map)
labels = [each.label for each in trees]
count = np.zeros(opts.output_dim)
for label in labels: count[label] += 1
# weight = 10 / (count ** 0.1)
weight = np.ones(opts.output_dim)
if opts.model == 'RNTN':
nn = RNTN(opts.wvec_dim, opts.output_dim, opts.num_words, opts.minibatch, rho=opts.rho)
elif opts.model == 'RNN':
nn = RNN(opts.wvec_dim, opts.output_dim, opts.num_words, opts.minibatch, rho=opts.rho, weight=weight)
elif opts.model == 'TreeLSTM':
nn = TreeLSTM(opts.wvec_dim, opts.mem_dim, opts.output_dim, opts.num_words, opts.minibatch, rho=opts.rho)
elif opts.model == 'TreeTLSTM':
nn = TreeTLSTM(opts.wvec_dim, opts.mem_dim, opts.output_dim, opts.num_words, opts.minibatch, rho=opts.rho)
else:
raise '%s is not a valid neural network so far only RNTN, RNN, RNN2, RNN3, and DCNN' % opts.model
nn.init_params()
sgd = optimizer.SGD(nn, alpha=opts.step, minibatch=opts.minibatch, optimizer=opts.optimizer)
dev_trees = tree.load_trees('./data/dev.json', label_method)
tree.convert_trees(dev_trees, training_word_map)
for e in range(opts.epochs):
start = time.time()
print "Running epoch %d" % e
sgd.run(trees, e)
end = time.time()
print "Time per epoch : %f" % (end - start)
with open(opts.out_file, 'w') as fid:
pickle.dump(opts, fid)
pickle.dump(sgd.costt, fid)
nn.to_file(fid)
if evaluate_accuracy_while_training:
# pdb.set_trace()
print "testing on training set real quick"
train_accuracies.append(test(opts.out_file, "train", label_method, opts.model, trees))
print "testing on dev set real quick"
dev_accuracies.append(test(opts.out_file, "dev", label_method, opts.model, dev_trees))
if evaluate_accuracy_while_training:
print train_accuracies
print dev_accuracies
plt.plot(train_accuracies, label='train')
plt.plot(dev_accuracies, label='dev')
plt.legend(loc=2)
plt.axvline(x=np.argmax(dev_accuracies), linestyle='--')
plt.show()
print "Analytic %.9f, Numerical %.9f, Relative Error %.9f" % (
dL[j][i], numGrad, err)
err2 += err
count += 1
if 0.001 > err2:
print "Grad Check Passed for dL"
else:
print "Grad Check Failed for dL: Sum of Error = %.9f" % (err2 /
count)
if __name__ == '__main__':
import loadTree as tree
train = tree.load_trees('./data/train.json', tree.aspect_label)
training_word_map = tree.load_word_map()
numW = len(training_word_map)
tree.convert_trees(train, training_word_map)
wvecDim = 10
outputDim = 5
memDim = 25
lstm = LSTM(wvecDim, memDim, outputDim, numW, mb_size=4)
lstm.init_params()
mbData = train[:4]
print "Numerical gradient check..."
rnn.check_grad(mbData, 1e-7)
def run(args=None):
usage = "usage : %prog [options]"
parser = optparse.OptionParser(usage=usage)
parser.add_option("--test", action="store_true", dest="test", default=False)
# Optimizer
parser.add_option("--minibatch", dest="minibatch", type="int", default=30)
parser.add_option("--optimizer", dest="optimizer", type="string", default="adagrad")
parser.add_option("--epochs", dest="epochs", type="int", default=50)
parser.add_option("--step", dest="step", type="float", default=5e-2)
parser.add_option("--rho", dest="rho", type="float", default=1e-3)
# Dimension
parser.add_option("--wvecDim", dest="wvec_dim", type="int", default=30)
parser.add_option("--memDim", dest="mem_dim", type="int", default=30)
parser.add_option("--outFile", dest="out_file", type="string", default="models/test.bin")
parser.add_option("--inFile", dest="in_file", type="string", default="models/test.bin")
parser.add_option("--data", dest="data", type="string", default="train")
parser.add_option("--model", dest="model", type="string", default="RNN")
parser.add_option("--label", dest="label_method", type="string", default="rating")
(opts, args) = parser.parse_args(args)
evaluate_accuracy_while_training = True
if opts.label_method == 'rating':
label_method = tree.rating_label
opts.output_dim = 5
elif opts.label_method == 'aspect':
label_method = tree.aspect_label
opts.output_dim = 5
elif opts.label_method == 'pair':
label_method = tree.pair_label
opts.output_dim = 25
else:
raise '%s is not a valid labelling method.' % opts.label_method
# Testing
if opts.test:
test(opts.in_file, opts.data, label_method, opts.model)
return
print "Loading data..."
train_accuracies = []
dev_accuracies = []
# load training data
trees = tree.load_trees('./data/train.json', label_method)
training_word_map = tree.load_word_map()
opts.num_words = len(training_word_map)
tree.convert_trees(trees, training_word_map)
labels = [each.label for each in trees]
count = np.zeros(opts.output_dim)
for label in labels: count[label] += 1
# weight = 10 / (count ** 0.1)
weight = np.ones(opts.output_dim)
if opts.model == 'RNTN':
nn = RNTN(opts.wvec_dim, opts.output_dim, opts.num_words, opts.minibatch, rho=opts.rho)
elif opts.model == 'RNN':
nn = RNN(opts.wvec_dim, opts.output_dim, opts.num_words, opts.minibatch, rho=opts.rho, weight=weight)
elif opts.model == 'TreeLSTM':
nn = TreeLSTM(opts.wvec_dim, opts.mem_dim, opts.output_dim, opts.num_words, opts.minibatch, rho=opts.rho)
elif opts.model == 'TreeTLSTM':
nn = TreeTLSTM(opts.wvec_dim, opts.mem_dim, opts.output_dim, opts.num_words, opts.minibatch, rho=opts.rho)
else:
raise '%s is not a valid neural network so far only RNTN, RNN, RNN2, RNN3, and DCNN' % opts.model
nn.init_params()
sgd = optimizer.SGD(nn, alpha=opts.step, minibatch=opts.minibatch, optimizer=opts.optimizer)
dev_trees = tree.load_trees('./data/dev.json', label_method)
tree.convert_trees(dev_trees, training_word_map)
# epochs 表示交叉验证的次数
for e in range(opts.epochs):
start = time.time()
print "Running epoch %d" % e
sgd.run(trees, e)
end = time.time()
print "Time per epoch : %f" % (end - start)
with open(opts.out_file, 'w') as fid:
pickle.dump(opts, fid)
pickle.dump(sgd.costt, fid)
nn.to_file(fid)
if evaluate_accuracy_while_training:
# pdb.set_trace()
print "testing on training set real quick"
train_accuracies.append(test(opts.out_file, "train", label_method, opts.model, trees))
print "testing on dev set real quick"
dev_accuracies.append(test(opts.out_file, "dev", label_method, opts.model, dev_trees))
if evaluate_accuracy_while_training:
print train_accuracies
print dev_accuracies
plt.plot(train_accuracies, label='train')
plt.plot(dev_accuracies, label='dev')
plt.legend(loc=2)
plt.axvline(x=np.argmax(dev_accuracies), linestyle='--')
plt.show()
import loadTree
import numpy as np
import cPickle as pickle
training_word_map = loadTree.load_word_map()
training_word_lst = loadTree.load_word_list()
glove_dic = 'data/glove.42B.300d.bin'
glove_vec = 'data/glove.42B.300d.npy'
with open(glove_dic, 'r') as fid:
glove_word_map = pickle.load(fid)
glove_word_vec = np.load(glove_vec)
L = np.empty((300, len(training_word_lst)))
for word in training_word_lst:
if word in glove_word_vec:
L[:, training_word_map[word]] = glove_word_vec[glove_word_map[word]]