gradients = T.grad(cost, wrt=params)
gradient_acc = [theano.shared(0 * p.get_value()) for p in params]
counter = theano.shared(np.float32(0.))
acc = theano.function(inputs=[X, Y],
outputs=cost,
updates=[(a, a + g)
for a, g in zip(gradient_acc, gradients)] +
[(counter, counter + np.float32(1.))])
update = theano.function(
inputs=[],outputs=[],
updates = updates.momentum(params,[ g / counter for g in gradient_acc ]) \
+ [ (a, np.float32(0) * a) for a in gradient_acc ] \
+ [ (counter,np.float32(0.)) ]
)
test = theano.function(inputs=[X, Y], outputs=probs[:, Y])
training_examples = [word.strip() for word in open('dictionary.txt')]
import random
for _ in xrange(1500):
random.shuffle(training_examples)
for i, string in enumerate(training_examples):
print acc(font.imagify(string), label_seq(string))
if i % 20 == 0: update()
if i % 100 == 0:
hinton.plot(test(font.imagify("test"), label_seq("test")).T,
max_arr=1.)
hinton.plot(font.imagify("test").T[::-1].astype('float32'))
P.save('model.pkl')
import theano.tensor as T
import numpy as np
from theano_toolkit import utils as U
from theano_toolkit import hinton
from theano_toolkit import updates
from theano_toolkit.parameters import Parameters
import ctc
import font
import lstm
from ocr import *
if __name__ == "__main__":
import sys
test_word = sys.argv[1]
P = Parameters()
X = T.matrix('X')
predict = build_model(P, 8, 512, len(font.chars) + 1)
probs = predict(X)
test = theano.function(inputs=[X], outputs=probs)
P.load('model.pkl')
image = font.imagify(test_word)
hinton.plot(image.astype(np.float32).T[::-1])
y_seq = label_seq(test_word)
probs = test(image)
print " ", ' '.join(font.chars[i] if i < len(font.chars) else "_"
for i in np.argmax(probs, axis=1))
hinton.plot(probs[:, y_seq].T, max_arr=1.)
acc = theano.function(
inputs=[X, Y],
outputs=cost,
updates = [
(a,a + g) for a,g in zip(gradient_acc,gradients)
] + [(counter,counter + np.float32(1.))]
)
update = theano.function(
inputs=[],outputs=[],
updates = updates.momentum(params,[ g / counter for g in gradient_acc ]) \
+ [ (a, np.float32(0) * a) for a in gradient_acc ] \
+ [ (counter,np.float32(0.)) ]
)
test = theano.function(
inputs=[X,Y],
outputs=probs[:,Y]
)
training_examples = [ word.strip() for word in open('dictionary.txt') ]
import random
for _ in xrange(1500):
random.shuffle(training_examples)
for i,string in enumerate(training_examples):
print acc(font.imagify(string),label_seq(string))
if i % 20 == 0: update()
if i % 100 == 0:
hinton.plot(test(font.imagify("test"),label_seq("test")).T,max_arr=1.)
hinton.plot(font.imagify("test").T[::-1].astype('float32'))
P.save('model.pkl')
import theano.tensor as T
import numpy as np
from theano_toolkit import utils as U
from theano_toolkit import hinton
from theano_toolkit import updates
from theano_toolkit.parameters import Parameters
import ctc
import font
import lstm
from ocr import *
if __name__ == "__main__":
import sys
test_word = sys.argv[1]
P = Parameters()
X = T.matrix('X')
predict = build_model(P,8,512,len(font.chars)+1)
probs = predict(X)
test = theano.function(inputs=[X],outputs=probs)
P.load('model.pkl')
image = font.imagify(test_word)
hinton.plot(image.astype(np.float32).T[::-1])
y_seq = label_seq(test_word)
probs = test(image)
print " ", ' '.join(font.chars[i] if i < len(font.chars) else "_" for i in np.argmax(probs,axis=1))
hinton.plot(probs[:,y_seq].T,max_arr=1.)
print ' '.join(rev_map[i] for i in question_data) for idx in ans_evd: print sentences[idx] print rev_map[ans_w] evidence_answer = answer(input_data,idxs,question_data) evd_prob = evidence_answer[:evidence_count] ans_prob = evidence_answer[-1] print "Evidences:" for i,e in enumerate(evd_prob): print e print "predicted", hinton.plot(e,max_arr=1) correct = np.zeros((e.shape[0],)) correct[ans_evd[i]] = 1 print "correct ", hinton.plot(correct) print "Answer:" print "predicted", hinton.plot(ans_prob,max_arr=1) correct = np.zeros((ans_prob.shape[0],)) correct[ans_w] = 1 print "correct ", hinton.plot(correct,max_arr=1)
print "Compiling functions..."
train, validate, sample = prepare_functions(
input_size=train_X_data.shape[1],
hidden_size=64,
latent_size=16,
step_count=10,
batch_size=batch_size,
train_X=train_X,
valid_X=valid_X)
batches = int(math.ceil(train_X_data.shape[0] / float(batch_size)))
print "Starting training..."
best_score = np.inf
for epoch in xrange(epochs):
vlb = validate()
print vlb,
if vlb < best_score:
x, x_samples, max_component, pi_samples = sample()
plot_samples(x, x_samples, max_component)
best_score = vlb
print "Saved."
hinton.plot(pi_samples.T)
print np.sum(pi_samples, axis=0)
else:
print
np.random.shuffle(train_X_data)
train_X.set_value(train_X_data)
for i in xrange(batches):
vals = train(i)
print ' '.join(map(str, vals))
