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conv_net_train.py
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conv_net_train.py
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"""
Sample code for
Convolutional Neural Networks for Sentence Classification
http://arxiv.org/pdf/1408.5882v2.pdf
Much of the code is modified from
- deeplearning.net (for ConvNet classes)
- https://github.com/mdenil/dropout (for dropout)
- https://groups.google.com/forum/#!topic/pylearn-dev/3QbKtCumAW4 (for Adadelta)
"""
import cPickle
import numpy as np
from collections import defaultdict, OrderedDict
import theano
import theano.tensor as T
from theano.ifelse import ifelse
import os
import warnings
import sys
import time
import getpass
import csv
warnings.filterwarnings("ignore")
#different non-linearities
def ReLU(x):
y = T.maximum(0.0, x)
return(y)
def Sigmoid(x):
y = T.nnet.sigmoid(x)
return(y)
def Tanh(x):
y = T.tanh(x)
return(y)
def Iden(x):
y = x
return(y)
def train_conv_net(datasets,
U,
ofile,
cv=0,
attr=0,
img_w=300,
filter_hs=[3,4,5],
hidden_units=[100,2],
dropout_rate=[0.5],
shuffle_batch=True,
n_epochs=25,
batch_size=50,
lr_decay = 0.95,
conv_non_linear="relu",
activations=[Iden],
sqr_norm_lim=9,
non_static=True):
"""
Train a simple conv net
img_h = sentence length (padded where necessary)
img_w = word vector length (300 for word2vec)
filter_hs = filter window sizes
hidden_units = [x,y] x is the number of feature maps (per filter window), and y is the penultimate layer
sqr_norm_lim = s^2 in the paper
lr_decay = adadelta decay parameter
"""
rng = np.random.RandomState(3435)
img_h = len(datasets[0][0][0])
filter_w = img_w
feature_maps = hidden_units[0]
filter_shapes = []
pool_sizes = []
for filter_h in filter_hs:
filter_shapes.append((feature_maps, 1, filter_h, filter_w))
pool_sizes.append((img_h-filter_h+1, img_w-filter_w+1))
parameters = [("image shape",img_h,img_w),("filter shape",filter_shapes), ("hidden_units",hidden_units),
("dropout", dropout_rate), ("batch_size",batch_size),("non_static", non_static),
("learn_decay",lr_decay), ("conv_non_linear", conv_non_linear), ("non_static", non_static)
,("sqr_norm_lim",sqr_norm_lim),("shuffle_batch",shuffle_batch)]
print parameters
#define model architecture
index = T.lscalar()
x = T.tensor3('x')
y = T.ivector('y')
mair = T.fmatrix('mair')
Words = theano.shared(value = U, name = "Words")
zero_vec_tensor = T.vector()
zero_vec = np.zeros(img_w)
set_zero = theano.function([zero_vec_tensor], updates=[(Words, T.set_subtensor(Words[0,:], zero_vec_tensor))], allow_input_downcast=True)
conv_layers = []
for i in xrange(len(filter_hs)):
filter_shape = filter_shapes[i]
pool_size = pool_sizes[i]
conv_layer = LeNetConvPoolLayer(rng, image_shape=None,
filter_shape=filter_shape, poolsize=pool_size, non_linear=conv_non_linear)
conv_layers.append(conv_layer)
layer0_input = Words[T.cast(x.flatten(),dtype="int32")].reshape((x.shape[0],x.shape[1],x.shape[2],Words.shape[1]))
def convolve_user_statuses(statuses):
layer1_inputs = []
def sum_mat(mat, out):
z=ifelse(T.neq(T.sum(mat),T.constant(0)),T.constant(1),T.constant(0))
return out+z, theano.scan_module.until(T.eq(z,T.constant(0)))
status_count,_ = theano.scan(fn = sum_mat, sequences=statuses, outputs_info=T.constant(0,dtype=theano.config.floatX))
# Slice-out dummy (zeroed) sentences
relv_input=statuses[:T.cast(status_count[-1],dtype='int32')].dimshuffle(0, 'x', 1, 2)
for conv_layer in conv_layers:
layer1_inputs.append(conv_layer.set_input(input=relv_input).flatten(2))
features = T.concatenate(layer1_inputs, axis=1)
avg_feat = T.max(features, axis=0)
return avg_feat
conv_feats, _ = theano.scan(fn= convolve_user_statuses, sequences= layer0_input)
# Add Mairesse features
layer1_input = T.concatenate([conv_feats, mair], axis=1)##mairesse_change
hidden_units[0] = feature_maps*len(filter_hs) + datasets[4].shape[1]##mairesse_change
classifier = MLPDropout(rng, input=layer1_input, layer_sizes=hidden_units, activations=activations, dropout_rates=dropout_rate)
svm_data = T.concatenate([classifier.layers[0].output, y.dimshuffle(0, 'x')], axis = 1)
#define parameters of the model and update functions using adadelta
params = classifier.params
for conv_layer in conv_layers:
params += conv_layer.params
if non_static:
#if word vectors are allowed to change, add them as model parameters
params += [Words]
cost = classifier.negative_log_likelihood(y)
dropout_cost = classifier.dropout_negative_log_likelihood(y)
grad_updates = sgd_updates_adadelta(params, dropout_cost, lr_decay, 1e-6, sqr_norm_lim)
#shuffle dataset and assign to mini batches. if dataset size is not a multiple of mini batches, replicate
#extra data (at random)
np.random.seed(3435)
if datasets[0].shape[0] % batch_size > 0:
extra_data_num = batch_size - datasets[0].shape[0] % batch_size
rand_perm = np.random.permutation(range(len(datasets[0])))
train_set_x = datasets[0][rand_perm]
train_set_y = datasets[1][rand_perm]
train_set_m = datasets[4][rand_perm]
extra_data_x = train_set_x[:extra_data_num]
extra_data_y = train_set_y[:extra_data_num]
extra_data_m = train_set_m[:extra_data_num]
new_data_x = np.append(datasets[0],extra_data_x,axis=0)
new_data_y = np.append(datasets[1],extra_data_y,axis=0)
new_data_m = np.append(datasets[4],extra_data_m,axis=0)
else:
new_data_x = datasets[0]
new_data_y = datasets[1]
new_data_m = datasets[4]
rand_perm = np.random.permutation(range(len(new_data_x)))
new_data_x = new_data_x[rand_perm]
new_data_y = new_data_y[rand_perm]
new_data_m = new_data_m[rand_perm]
n_batches = new_data_x.shape[0]/batch_size
n_train_batches = int(np.round(n_batches*0.9))
#divide train set into train/val sets
test_set_x = datasets[2]
test_set_y = np.asarray(datasets[3],"int32")
test_set_m = datasets[5]
train_set_x, train_set_y, train_set_m = shared_dataset((new_data_x[:n_train_batches*batch_size], new_data_y[:n_train_batches*batch_size], new_data_m[:n_train_batches*batch_size]))
val_set_x, val_set_y, val_set_m = shared_dataset((new_data_x[n_train_batches*batch_size:], new_data_y[n_train_batches*batch_size:], new_data_m[n_train_batches*batch_size:]))
n_val_batches = n_batches - n_train_batches
val_model = theano.function([index], classifier.errors(y),
givens={
x: val_set_x[index * batch_size: (index + 1) * batch_size],
y: val_set_y[index * batch_size: (index + 1) * batch_size],
mair: val_set_m[index * batch_size: (index + 1) * batch_size]},##mairesse_change
allow_input_downcast = True)
#compile theano functions to get train/val/test errors
test_model = theano.function([index], [classifier.errors(y), svm_data],
givens={
x: train_set_x[index * batch_size: (index + 1) * batch_size],
y: train_set_y[index * batch_size: (index + 1) * batch_size],
mair: train_set_m[index * batch_size: (index + 1) * batch_size]},##mairesse_change
allow_input_downcast=True)
train_model = theano.function([index], cost, updates=grad_updates,
givens={
x: train_set_x[index*batch_size:(index+1)*batch_size],
y: train_set_y[index*batch_size:(index+1)*batch_size],
mair: train_set_m[index * batch_size: (index + 1) * batch_size]},##mairesse_change
allow_input_downcast = True)
test_y_pred = classifier.predict(layer1_input)
test_error = T.sum(T.neq(test_y_pred, y))
true_p = T.sum(test_y_pred*y)
false_p = T.sum(test_y_pred*T.mod(y+T.ones_like(y),T.constant(2,dtype='int32')))
false_n = T.sum(y*T.mod(test_y_pred+T.ones_like(y),T.constant(2,dtype='int32')))
test_model_all = theano.function([x, y,
mair##mairesse_change
]
, [test_error, true_p, false_p, false_n, svm_data], allow_input_downcast = True)
test_batches = test_set_x.shape[0]/batch_size;
#start training over mini-batches
print '... training'
epoch = 0
best_val_perf = 0
val_perf = 0
test_perf = 0
fscore = 0
cost_epoch = 0
while (epoch < n_epochs):
start_time = time.time()
epoch = epoch + 1
if shuffle_batch:
for minibatch_index in np.random.permutation(range(n_train_batches)):
cost_epoch = train_model(minibatch_index)
set_zero(zero_vec)
else:
for minibatch_index in xrange(n_train_batches):
cost_epoch = train_model(minibatch_index)
set_zero(zero_vec)
train_losses = [test_model(i) for i in xrange(n_train_batches)]
train_perf = 1 - np.mean([loss[0] for loss in train_losses])
val_losses = [val_model(i) for i in xrange(n_val_batches)]
val_perf = 1- np.mean(val_losses)
epoch_perf = 'epoch: %i, training time: %.2f secs, train perf: %.2f %%, val perf: %.2f %%' % (epoch, time.time()-start_time, train_perf * 100., val_perf*100.)
print(epoch_perf)
ofile.write(epoch_perf+"\n")
ofile.flush()
if val_perf >= best_val_perf:
best_val_perf = val_perf
test_loss_list = [test_model_all(test_set_x[idx*batch_size:(idx+1)*batch_size], test_set_y[idx*batch_size:(idx+1)*batch_size],
test_set_m[idx*batch_size:(idx+1)*batch_size]##mairesse_change
) for idx in xrange(test_batches)]
if test_set_x.shape[0]>test_batches*batch_size:
test_loss_list.append(test_model_all(test_set_x[test_batches*batch_size:], test_set_y[test_batches*batch_size:],
test_set_m[test_batches*batch_size:]##mairesse_change
))
test_loss_list_temp=test_loss_list
test_loss_list=np.asarray([t[:-1] for t in test_loss_list])
test_loss = np.sum(test_loss_list[:, 0])/float(test_set_x.shape[0])
test_perf = 1- test_loss
tp = np.sum(test_loss_list[:, 1])
fp = np.sum(test_loss_list[:, 2])
fn = np.sum(test_loss_list[:, 3])
tn = test_set_x.shape[0]-(tp+fp+fn)
fscore=np.mean([2*tp/float(2*tp+fp+fn), 2*tn/float(2*tn+fp+fn)])
svm_test=np.concatenate([t[-1] for t in test_loss_list_temp], axis=0)
svm_train=np.concatenate([t[1] for t in train_losses], axis=0)
output="Test result: accu: "+str(test_perf)+", macro_fscore: "+str(fscore)+"\ntp: "+str(tp)+" tn:"+str(tn)+" fp: "+str(fp)+" fn: "+str(fn)
print output
ofile.write(output+"\n")
ofile.flush()
# dump train and test features
cPickle.dump(svm_test, open("cvte"+str(attr)+str(cv)+".p", "wb"))
cPickle.dump(svm_train, open("cvtr"+str(attr)+str(cv)+".p", "wb"))
updated_epochs = refresh_epochs()
if updated_epochs!=None and n_epochs!=updated_epochs:
n_epochs = updated_epochs
print 'Epochs updated to '+str(n_epochs)
return test_perf, fscore
def refresh_epochs():
try:
f=open('n_epochs','r')
except Exception:
return None
try:
n = int(f.readline().strip())
except Exception:
f.close()
return None
f.close()
return n
def shared_dataset(data_xy, borrow=True):
""" Function that loads the dataset into shared variables
The reason we store our dataset in shared variables is to allow
Theano to copy it into the GPU memory (when code is run on GPU).
Since copying data into the GPU is slow, copying a minibatch everytime
is needed (the default behaviour if the data is not in a shared
variable) would lead to a large decrease in performance.
"""
data_x, data_y, data_m = data_xy
shared_x = theano.shared(np.asarray(data_x,
dtype=theano.config.floatX),
borrow=borrow)
shared_y = theano.shared(np.asarray(data_y,
dtype=theano.config.floatX),
borrow=borrow)
shared_m = theano.shared(np.asarray(data_m,
dtype=theano.config.floatX),
borrow=borrow)
return shared_x, T.cast(shared_y, 'int32'), shared_m
def sgd_updates_adadelta(params,cost,rho=0.95,epsilon=1e-6,norm_lim=9,word_vec_name='Words'):
"""
adadelta update rule, mostly from
https://groups.google.com/forum/#!topic/pylearn-dev/3QbKtCumAW4 (for Adadelta)
"""
updates = OrderedDict({})
exp_sqr_grads = OrderedDict({})
exp_sqr_ups = OrderedDict({})
gparams = []
for param in params:
empty = np.zeros_like(param.get_value())
exp_sqr_grads[param] = theano.shared(value=as_floatX(empty),name="exp_grad_%s" % param.name)
gp = T.grad(cost, param)
exp_sqr_ups[param] = theano.shared(value=as_floatX(empty), name="exp_grad_%s" % param.name)
gparams.append(gp)
for param, gp in zip(params, gparams):
exp_sg = exp_sqr_grads[param]
exp_su = exp_sqr_ups[param]
up_exp_sg = rho * exp_sg + (1 - rho) * T.sqr(gp)
updates[exp_sg] = up_exp_sg
step = -(T.sqrt(exp_su + epsilon) / T.sqrt(up_exp_sg + epsilon)) * gp
updates[exp_su] = rho * exp_su + (1 - rho) * T.sqr(step)
stepped_param = param + step
updates[param] = stepped_param
return updates
def as_floatX(variable):
if isinstance(variable, float):
return np.cast[theano.config.floatX](variable)
if isinstance(variable, np.ndarray):
return np.cast[theano.config.floatX](variable)
return theano.tensor.cast(variable, theano.config.floatX)
def safe_update(dict_to, dict_from):
"""
re-make update dictionary for safe updating
"""
for key, val in dict(dict_from).iteritems():
if key in dict_to:
raise KeyError(key)
dict_to[key] = val
return dict_to
def get_idx_from_sent(status, word_idx_map, charged_words, max_l=51, max_s=200, k=300, filter_h=5):
"""
Transforms sentence into a list of indices. Pad with zeroes.
"""
x = []
pad = filter_h - 1
length = len(status)
pass_one=True
while len(x)==0:
for i in range(length):
words = status[i].split()
if pass_one:
words_set = set(words)
if len(charged_words.intersection(words_set))==0:
continue
else:
if np.random.randint(0,2)==0:
continue
y=[]
for i in xrange(pad):
y.append(0)
for word in words:
if word in word_idx_map:
y.append(word_idx_map[word])
while len(y) < max_l+2*pad:
y.append(0)
x.append(y)
pass_one=False
if len(x) < max_s:
x.extend([[0]*(max_l+2*pad)]*(max_s-len(x)))
return x
def make_idx_data_cv(revs, word_idx_map, mairesse, charged_words, cv, per_attr=0, max_l=51, max_s=200, k=300, filter_h=5):
"""
Transforms sentences into a 2-d matrix.
"""
trainX, testX, trainY, testY, mTrain, mTest = [], [], [], [], [], []
for rev in revs:
sent = get_idx_from_sent(rev["text"], word_idx_map,
charged_words,
max_l, max_s, k, filter_h)
if rev["split"]==cv:
testX.append(sent)
testY.append(rev['y'+str(per_attr)])
mTest.append(mairesse[rev["user"]])
else:
trainX.append(sent)
trainY.append(rev['y'+str(per_attr)])
mTrain.append(mairesse[rev["user"]])
trainX = np.array(trainX,dtype="int32")
testX = np.array(testX,dtype="int32")
trainY = np.array(trainY,dtype="int32")
testY = np.array(testY,dtype="int32")
mTrain = np.array(mTrain, dtype=theano.config.floatX)
mTest = np.array(mTest, dtype=theano.config.floatX)
return [trainX, trainY, testX, testY, mTrain, mTest]
if __name__=="__main__":
print "loading data...",
x = cPickle.load(open("essays_mairesse.p","rb"))
revs, W, W2, word_idx_map, vocab, mairesse = x[0], x[1], x[2], x[3], x[4], x[5]
print "data loaded!"
mode= sys.argv[1]
word_vectors = sys.argv[2]
attr = int(sys.argv[3])
if mode=="-nonstatic":
print "model architecture: CNN-non-static"
non_static=True
elif mode=="-static":
print "model architecture: CNN-static"
non_static=False
execfile("conv_net_classes.py")
if word_vectors=="-rand":
print "using: random vectors"
U = W2
elif word_vectors=="-word2vec":
print "using: word2vec vectors"
U = W
r = range(0,10)
ofile=file('perf_output_'+str(attr)+'.txt','w')
charged_words=[]
emof=open("Emotion_Lexicon.csv","rb")
csvf=csv.reader(emof, delimiter=',',quotechar='"')
first_line=True
for line in csvf:
if first_line:
first_line=False
continue
if line[11]=="1":
charged_words.append(line[0])
emof.close()
charged_words=set(charged_words)
results = []
for i in r:
datasets = make_idx_data_cv(revs, word_idx_map, mairesse, charged_words, i, attr, max_l=149, max_s=312, k=300, filter_h=3)
perf, fscore = train_conv_net(datasets,
U,
ofile,
cv=i,
attr=attr,
lr_decay=0.95,
filter_hs=[1,2,3],
conv_non_linear="relu",
hidden_units=[200,200,2],
shuffle_batch=True,
n_epochs=50,
sqr_norm_lim=9,
non_static=non_static,
batch_size=50,
dropout_rate=[0.5, 0.5, 0.5],
activations=[Sigmoid])
output = "cv: " + str(i) + ", perf: " + str(perf)+ ", macro_fscore: " + str(fscore)
print output
ofile.write(output+"\n")
ofile.flush()
results.append([perf, fscore])
results=np.asarray(results)
perf_out = 'Perf : '+str(np.mean(results[:, 0]))
fscore_out = 'Macro_Fscore : '+str(np.mean(results[:, 1]))
print perf_out
print fscore_out
ofile.write(perf_out+"\n"+fscore_out)
ofile.close()