def evaluate_lenet5(learning_rate=0.1,
n_epochs=200,
dataset='mnist.pkl.gz',
nkerns=[16, 16, 16],
batch_size=500):
rng = numpy.random.RandomState(32324)
datasets = load_data(dataset)
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
n_train_batches = train_set_x.get_value(borrow=True).shape[0] // batch_size
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] // batch_size
n_test_batches = test_set_x.get_value(borrow=True).shape[0] // batch_size
index = T.lscalar() # index for each mini batch
train_epoch = T.lscalar()
x = T.matrix('x')
y = T.ivector('y')
# ------------------------------- Building Model ----------------------------------
print "...Building the model"
# output image size = (28-5+1+4)/2 = 14
layer_0_input = x.reshape((batch_size, 1, 28, 28))
layer_0 = LeNetConvPoolLayer(rng,
input=layer_0_input,
image_shape=(batch_size, 1, 28, 28),
filter_shape=(nkerns[0], 1, 5, 5),
poolsize=(2, 2),
border_mode=2)
#output image size = (14-3+1)/2 = 6
layer_1 = LeNetConvPoolLayer(rng,
input=layer_0.output,
image_shape=(batch_size, nkerns[0], 14, 14),
filter_shape=(nkerns[1], nkerns[0], 3, 3),
poolsize=(2, 2))
#output image size = (6-3+1)/2 = 2
layer_2 = LeNetConvPoolLayer(rng,
input=layer_1.output,
image_shape=(batch_size, nkerns[1], 6, 6),
filter_shape=(nkerns[2], nkerns[1], 3, 3),
poolsize=(2, 2))
# make the input to hidden layer 2 dimensional
layer_3_input = layer_2.output.flatten(2)
layer_3 = HiddenLayer(rng,
input=layer_3_input,
n_in=nkerns[2] * 2 * 2,
n_out=200,
activation=T.tanh)
layer_4 = LogReg(input=layer_3.output, n_in=200, n_out=10)
teacher_p_y_given_x = theano.shared(numpy.asarray(
pickle.load(open('prob_best_model.pkl', 'rb')),
dtype=theano.config.floatX),
borrow=True)
#cost = layer_4.neg_log_likelihood(y) + T.mean((teacher_W - layer_4.W)**2)/(2.*(1+epoch*2)) + T.mean((teacher_b-layer_4.b)**2)/(2.*(1+epoch*2))
# import pdb
# pdb.set_trace()
p_y_given_x = T.matrix('p_y_given_x')
e = theano.shared(value=0, name='e', borrow=True)
#cost = layer_4.neg_log_likelihood(y) + 1.0/(e)*T.mean((layer_4.p_y_given_x - p_y_given_x)**2)
cost = layer_4.neg_log_likelihood(
y) + 2.0 / (e) * T.mean(-T.log(layer_4.p_y_given_x) * p_y_given_x -
layer_4.p_y_given_x * T.log(p_y_given_x))
test_model = theano.function(
[index],
layer_4.errors(y),
givens={
x: test_set_x[index * batch_size:(index + 1) * batch_size],
y: test_set_y[index * batch_size:(index + 1) * batch_size]
})
validate_model = theano.function(
[index],
layer_4.errors(y),
givens={
x: valid_set_x[index * batch_size:(index + 1) * batch_size],
y: valid_set_y[index * batch_size:(index + 1) * batch_size]
})
# list of parameters
params = layer_4.params + layer_3.params + layer_2.params + layer_1.params + layer_0.params
grads = T.grad(cost, params)
updates = [(param_i, param_i - learning_rate * grad_i)
for param_i, grad_i in zip(params, grads)]
train_model = theano.function(
[index, train_epoch],
cost,
updates=updates,
givens={
x: train_set_x[index * batch_size:(index + 1) * batch_size],
y: train_set_y[index * batch_size:(index + 1) * batch_size],
p_y_given_x: teacher_p_y_given_x[index],
e: train_epoch
})
# -----------------------------------------Starting Training ------------------------------
print('..... Training ')
# for early stopping
patience = 10000
patience_increase = 2
improvement_threshold = 0.95
validation_frequency = min(n_train_batches, patience // 2)
best_validation_loss = numpy.inf # initialising loss to be inifinite
best_itr = 0
test_score = 0
start_time = timeit.default_timer()
#epo = theano.shared('epo')
epoch = 0
done_looping = False
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
for minibatch_index in range(n_train_batches):
iter = (epoch - 1) * n_train_batches + minibatch_index
if iter % 100 == 0:
print('training @ iter = ', iter)
cost_ij = train_model(minibatch_index, epoch)
if (iter + 1) % validation_frequency == 0:
# compute loss on validation set
validation_losses = [
validate_model(i) for i in range(n_valid_batches)
]
this_validation_loss = numpy.mean(validation_losses)
# import pdb
# pdb.set_trace()
print('epoch %i, minibatch %i/%i, validation error %f %%' %
(epoch, minibatch_index + 1, n_train_batches,
this_validation_loss * 100.))
# check with best validation score till now
if this_validation_loss < best_validation_loss:
# improve
if this_validation_loss < best_validation_loss * improvement_threshold:
patience = max(patience, iter * patience_increase)
best_validation_loss = this_validation_loss
best_itr = iter
test_losses = [
test_model(i) for i in range(n_test_batches)
]
test_score = numpy.mean(test_losses)
print('epoch %i, minibatch %i/%i, testing error %f %%' %
(epoch, minibatch_index + 1, n_train_batches,
test_score * 100.))
with open('best_model_3layer.pkl', 'wb') as f:
pickle.dump(params, f)
with open('Results_student_3.txt', 'wb') as f2:
f2.write(str(test_score * 100) + '\n')
#if patience <= iter:
# done_looping = True
# break
end_time = timeit.default_timer()
print('Optimization complete')
print(
'Best validation score of %f %% obtained at iteration %i,'
'with test performance %f %%' %
(best_validation_loss * 100., best_itr, test_score * 100))
print('The code ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(self,
learning_rate=0.1,
n_epochs=1,
dataset='mnist.pkl.gz',
nkerns=[20, 50],
batch_size=500,
testing=0):
rng = numpy.random.RandomState(32324)
datasets = self.data
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
n_train_batches = train_set_x.get_value(
borrow=True).shape[0] // batch_size
n_valid_batches = valid_set_x.get_value(
borrow=True).shape[0] // batch_size
n_test_batches = test_set_x.get_value(
borrow=True).shape[0] // batch_size
index = T.lscalar() # index for each mini batch
x = T.matrix('x')
y = T.ivector('y')
# ------------------------------- Building Model ----------------------------------
if testing == 0:
print "...Building the model"
# output image size = (28-5+1)/2 = 12
layer_0_input = x.reshape((batch_size, 1, 28, 28))
layer_0 = LeNetConvPoolLayer(rng,
input=layer_0_input,
image_shape=(batch_size, 1, 28, 28),
filter_shape=(nkerns[0], 1, 5, 5),
poolsize=(2, 2))
#output image size = (12-5+1)/2 = 4
layer_1 = LeNetConvPoolLayer(rng,
input=layer_0.output,
image_shape=(batch_size, nkerns[0], 12,
12),
filter_shape=(nkerns[1], nkerns[0], 5, 5),
poolsize=(2, 2))
# make the input to hidden layer 2 dimensional
layer_2_input = layer_1.output.flatten(2)
layer_2 = HiddenLayer(rng,
input=layer_2_input,
n_in=nkerns[1] * 4 * 4,
n_out=500,
activation=T.tanh)
layer_3 = LogReg(input=layer_2.output, n_in=500, n_out=10)
self.cost = layer_3.neg_log_likelihood(y)
self.s = layer_3.s
self.test_model = theano.function(
[index],
layer_3.errors(y),
givens={
x: test_set_x[index * batch_size:(index + 1) * batch_size],
y: test_set_y[index * batch_size:(index + 1) * batch_size]
})
self.validate_model = theano.function(
[index],
layer_3.errors(y),
givens={
x: valid_set_x[index * batch_size:(index + 1) * batch_size],
y: valid_set_y[index * batch_size:(index + 1) * batch_size]
})
self.train_predic = theano.function(
[index],
layer_3.prob_y_given_x(),
givens={
x: train_set_x[index * batch_size:(index + 1) * batch_size]
})
# list of parameters
self.params = layer_3.params + layer_2.params + layer_1.params + layer_0.params
grads = T.grad(self.cost, self.params)
self.coefficient = 1
self.shapes = [i.get_value().shape for i in self.params]
symbolic_types = T.scalar, T.vector, T.matrix, T.tensor3, T.tensor4
v = [symbolic_types[len(i)]() for i in self.shapes]
#import pdb
#pdb.set_trace()
gauss_vector = Gv(self.cost, self.s, self.params, v, self.coefficient)
self.get_cost = theano.function(
[
index,
],
self.cost,
givens={
x: train_set_x[index * batch_size:(index + 1) * batch_size],
y: train_set_y[index * batch_size:(index + 1) * batch_size]
},
on_unused_input='ignore')
self.get_grad = theano.function(
[
index,
],
grads,
givens={
x: train_set_x[index * batch_size:(index + 1) * batch_size],
y: train_set_y[index * batch_size:(index + 1) * batch_size]
},
on_unused_input='ignore')
self.get_s = theano.function(
[
index,
],
self.s,
givens={
x: train_set_x[index * batch_size:(index + 1) * batch_size],
},
on_unused_input='ignore')
self.function_Gv = theano.function(
[index],
gauss_vector,
givens={
x: train_set_x[index * batch_size:(index + 1) * batch_size],
y: valid_set_y[index * batch_size:(index + 1) * batch_size]
},
on_unused_input='ignore')
# # Using stochastic gradient updates
# updates = [ (param_i, param_i-learning_rate*grad_i) for param_i, grad_i in zip(params,grads) ]
# train_model = theano.function([index],cost, updates=updates,
# givens={
# x: train_set_x[index*batch_size:(index+1)*batch_size],
# y: train_set_y[index*batch_size:(index+1)*batch_size]
# })
# Using conjugate gradient updates
# 'cg_ = cg(cost,output,params,coefficient,v)
# updated_params = [(param_i, param_j) for param_i,param_j in zip(params,cg_)]'
#self.update_parameters= theano.function([updated_params],updates=[params,updated_params])
# -----------------------------------------Starting Training ------------------------------
if testing == 0:
print('..... Training ')
# for early stopping
patience = 10000
patience_increase = 2
improvement_threshold = 0.95
validation_frequency = min(n_train_batches, patience // 2)
self.best_validation_loss = numpy.inf # initialising loss to be inifinite
best_itr = 0
test_score = 0
start_time = timeit.default_timer()
epoch = 0
done_looping = False
while (epoch < n_epochs):
epoch = epoch + 1
for minibatch_index in range(n_train_batches):
iter = (epoch - 1) * n_train_batches + minibatch_index
if iter % 1 == 0:
print('training @ iter = ', iter)
self.cg(minibatch_index)
if testing == 0:
print('Optimization complete')
print(
'Best validation score of %f %% obtained at iteration %i,'
'with test performance %f %%' %
(best_validation_loss * 100., best_itr, test_score * 100))
print('The code ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate = 0.10, n_epochs=200, dataset='mnist.pkl.gz',nkerns = [16,16,16,12,12,12], batch_size = 500):
rng = numpy.random.RandomState(32324)
datasets = load_data(dataset)
train_set_x,train_set_y = datasets[0]
valid_set_x,valid_set_y = datasets[1]
test_set_x,test_set_y = datasets[2]
n_train_batches = train_set_x.get_value(borrow=True).shape[0]//batch_size
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]//batch_size
n_test_batches = test_set_x.get_value(borrow=True).shape[0]//batch_size
index = T.lscalar() # index for each mini batch
train_epoch = T.lscalar('train_epoch')
x = T.matrix('x')
y = T.ivector('y')
# ------------------------------- Building Model ----------------------------------
print "...Building the model"
layer_0_input = x.reshape((batch_size,1,28,28))
# output image size = (28-5+1+)/1 = 24
layer_0 = LeNetConvPoolLayer(rng,input = layer_0_input, image_shape=(batch_size,1,28,28),
filter_shape=(nkerns[0],1,5,5),poolsize=(1,1))
#output image size = (24-3+1) = 22
layer_1 = LeNetConvPoolLayer(rng, input = layer_0.output, image_shape = (batch_size, nkerns[0],24,24),
filter_shape = (nkerns[1],nkerns[0],3,3), poolsize=(1,1) )
#output image size = (22-3+1)/2 = 10
layer_2 = LeNetConvPoolLayer(rng, input = layer_1.output, image_shape = (batch_size, nkerns[1],22,22),
filter_shape = (nkerns[2],nkerns[1],3,3), poolsize=(2,2) )
#output image size = (10-3+1)/2 = 4
layer_3 = LeNetConvPoolLayer(rng, input = layer_2.output, image_shape = (batch_size, nkerns[2],10,10),
filter_shape = (nkerns[3], nkerns[2],3,3), poolsize=(2,2) )
#output image size = (4-3+2+1) = 4
layer_4 = LeNetConvPoolLayer(rng, input = layer_3.output, image_shape = (batch_size, nkerns[3],4,4),
filter_shape = (nkerns[4], nkerns[3],3,3), poolsize=(1,1), border_mode = 1 )
#output image size = (4-3+1)/2 = 2
layer_5 = LeNetConvPoolLayer(rng, input = layer_4.output, image_shape = (batch_size, nkerns[4],4,4),
filter_shape = (nkerns[5], nkerns[4],3,3), poolsize=(2,2), border_mode = 1 )
# make the input to hidden layer 2 dimensional
layer_6_input = layer_5.output.flatten(2)
layer_6 = HiddenLayer(rng,input = layer_6_input, n_in = nkerns[5]*2*2, n_out = 200, activation = T.tanh)
layer_7 = LogReg(input = layer_6.output, n_in=200, n_out = 10)
teacher_p_y_given_x = theano.shared(numpy.asarray(pickle.load(open('prob_best_model.pkl','rb')),dtype =theano.config.floatX), borrow=True)
p_y_given_x = T.matrix('p_y_given_x')
e = theano.shared(value = 0, name = 'e', borrow = True)
cost = layer_7.neg_log_likelihood(y) + 2.0/(e)*T.mean(-T.log(layer_7.p_y_given_x)*p_y_given_x - layer_7.p_y_given_x*T.log(p_y_given_x))
tg = theano.shared(numpy.asarray(pickle.load(open('modified_guided_data.pkl','rb')),dtype =theano.config.floatX), borrow=True)
guiding_weights = T.tensor4('guiding_weights')
#guide_cost = T.mean(-T.log(layer_3.output)*guiding_weights - layer_3.output*T.log(guiding_weights))
guide_cost = T.mean((layer_3.output-guiding_weights)**2)
test_model = theano.function([index],layer_7.errors(y),
givens={
x: test_set_x[index*batch_size:(index+1)*batch_size],
y: test_set_y[index*batch_size:(index+1)*batch_size]
})
validate_model = theano.function([index],layer_7.errors(y),
givens={
x: valid_set_x[index*batch_size:(index+1)*batch_size],
y: valid_set_y[index*batch_size:(index+1)*batch_size]
})
# list of parameters
params = layer_7.params + layer_6.params + layer_5.params + layer_4.params + layer_3.params + layer_2.params + layer_1.params + layer_0.params
params_gl = layer_3.params + layer_2.params + layer_1.params + layer_0.params
# import pdb
# pdb.set_trace()
grads_gl = T.grad(guide_cost,params_gl)
updates_gl = [ (param_i,param_i-learning_rate/10*grad_i) for param_i,grad_i in zip(params_gl,grads_gl) ]
grads = T.grad(cost,params)
updates = [ (param_i, param_i-learning_rate*grad_i) for param_i, grad_i in zip(params,grads) ]
train_model = theano.function([index,train_epoch],cost, updates=updates,
givens={
x: train_set_x[index*batch_size:(index+1)*batch_size],
y: train_set_y[index*batch_size:(index+1)*batch_size],
p_y_given_x: teacher_p_y_given_x[index],
e: train_epoch
})
train_till_guided_layer = theano.function([index],guide_cost,updates = updates_gl,
givens={
x: train_set_x[index*batch_size:(index+1)*batch_size],
y: train_set_y[index*batch_size:(index+1)*batch_size],
guiding_weights : tg[index]
},on_unused_input='ignore')
# -----------------------------------------Starting Training ------------------------------
print ('..... Training ' )
# for early stopping
patience = 10000
patience_increase = 2
improvement_threshold = 0.95
validation_frequency = min(n_train_batches, patience//2)
best_validation_loss = numpy.inf # initialising loss to be inifinite
best_itr = 0
test_score = 0
start_time = timeit.default_timer()
epoch = 0
done_looping = False
while (epoch < n_epochs) and (not done_looping) :
epoch = epoch+1
for minibatch_index in range(n_train_batches):
iter = (epoch - 1)*n_train_batches + minibatch_index
if iter%100==0:
print ('training @ iter = ', iter)
if epoch < n_epochs/5:
cost_ij_guided = train_till_guided_layer(minibatch_index)
cost_ij = train_model(minibatch_index,epoch)
if(iter +1)%validation_frequency ==0:
# compute loss on validation set
validation_losses = [validate_model(i) for i in range(n_valid_batches)]
this_validation_loss = numpy.mean(validation_losses)
# import pdb
# pdb.set_trace()
with open('Student_6_terminal_out','a+') as f_:
f_.write('epoch %i, minibatch %i/%i, validation error %f %% \n' %(epoch,minibatch_index+1,n_train_batches,this_validation_loss*100. ))
# check with best validation score till now
if this_validation_loss<best_validation_loss:
# improve
if this_validation_loss < best_validation_loss * improvement_threshold:
patience = max(patience, iter*patience_increase)
best_validation_loss = this_validation_loss
best_itr = iter
test_losses = [test_model(i) for i in range(n_test_batches)]
test_score = numpy.mean(test_losses)
with open('Student_6_terminal_out','a+') as f_:
f_.write('epoch %i, minibatch %i/%i, testing error %f %%\n' %(epoch, minibatch_index+1,n_train_batches,test_score*100.))
with open('best_model_7layer.pkl', 'wb') as f:
pickle.dump(params, f)
with open('Results_student_6.txt', 'wb') as f1:
f1.write(str(test_score*100)+'\n')
#if patience <= iter:
# done_looping = True
# break
end_time = timeit.default_timer()
with open('Student_6_terminal_out','a+') as f_:
f_.write('Optimization complete\n')
f_.write('Best validation score of %f %% obtained at iteration %i, with test performance %f %% \n' % (best_validation_loss*100., best_itr, test_score*100 ))
f_.write('The code ran for %.2fm \n' %((end_time - start_time)/60.))
def evaluate_lenet5(learning_rate = 0.1, n_epochs=200, dataset='mnist.pkl.gz',nkerns = [20,50], batch_size = 500 , testing =0):
rng = numpy.random.RandomState(32324)
datasets = load_data(dataset)
train_set_x,train_set_y = datasets[0]
valid_set_x,valid_set_y = datasets[1]
test_set_x,test_set_y = datasets[2]
n_train_batches = train_set_x.get_value(borrow=True).shape[0]//batch_size
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]//batch_size
n_test_batches = test_set_x.get_value(borrow=True).shape[0]//batch_size
index = T.lscalar() # index for each mini batch
x = T.matrix('x')
y = T.ivector('y')
# ------------------------------- Building Model ----------------------------------
if testing ==0:
print "...Building the model"
# output image size = (28-5+1)/2 = 12
layer_0_input = x.reshape((batch_size,1,28,28))
layer_0 = LeNetConvPoolLayer(rng,input = layer_0_input, image_shape=(batch_size,1,28,28),filter_shape=(nkerns[0],1,5,5),poolsize=(2,2))
#output image size = (12-5+1)/2 = 4
layer_1 = LeNetConvPoolLayer(rng, input = layer_0.output, image_shape = (batch_size, nkerns[0],12,12),
filter_shape = (nkerns[1],nkerns[0],5,5), poolsize=(2,2) )
# make the input to hidden layer 2 dimensional
layer_2_input = layer_1.output.flatten(2)
layer_2 = HiddenLayer(rng,input = layer_2_input, n_in = nkerns[1]*4*4, n_out = 500, activation = T.tanh)
layer_3 = LogReg(input = layer_2.output, n_in=500, n_out = 10)
cost = layer_3.neg_log_likelihood(y)
test_model = theano.function([index],layer_3.errors(y),
givens={
x: test_set_x[index*batch_size:(index+1)*batch_size],
y: test_set_y[index*batch_size:(index+1)*batch_size]
})
validate_model = theano.function([index],layer_3.errors(y),
givens={
x: valid_set_x[index*batch_size:(index+1)*batch_size],
y: valid_set_y[index*batch_size:(index+1)*batch_size]
})
train_predic = theano.function([index], layer_3.prob_y_given_x(),
givens={
x: train_set_x[index*batch_size:(index+1)*batch_size]
})
# list of parameters
layer_guided = theano.function([index], layer_1.output,
givens={
x: train_set_x[index*batch_size:(index+1)*batch_size]
})
params = layer_3.params + layer_2.params + layer_1.params + layer_0.params
grads = T.grad(cost,params)
updates = [ (param_i, param_i-learning_rate*grad_i) for param_i, grad_i in zip(params,grads) ]
train_model = theano.function([index],cost, updates=updates,
givens={
x: train_set_x[index*batch_size:(index+1)*batch_size],
y: train_set_y[index*batch_size:(index+1)*batch_size]
})
# -----------------------------------------Starting Training ------------------------------
if testing ==0:
print ('..... Training ' )
# for early stopping
patience = 10000
patience_increase = 2
improvement_threshold = 0.95
validation_frequency = min(n_train_batches, patience//2)
best_validation_loss = numpy.inf # initialising loss to be inifinite
best_itr = 0
test_score = 0
start_time = timeit.default_timer()
epoch = 0
done_looping = False
while (epoch < n_epochs) and (not done_looping) and testing ==0:
epoch = epoch+1
for minibatch_index in range(n_train_batches):
iter = (epoch - 1)*n_train_batches + minibatch_index
if iter%100 ==0:
print ('training @ iter = ', iter)
cost_ij = train_model(minibatch_index)
if(iter +1)%validation_frequency ==0:
# compute loss on validation set
validation_losses = [validate_model(i) for i in range(n_valid_batches)]
this_validation_loss = numpy.mean(validation_losses)
# import pdb
# pdb.set_trace()
print ('epoch %i, minibatch %i/%i, validation error %f %%' %(epoch,minibatch_index+1,n_train_batches,this_validation_loss*100. ))
# check with best validation score till now
if this_validation_loss<best_validation_loss:
# improve
# if this_validation_loss < best_validation_loss * improvement_threshold:
# patience = max(patience, iter*patience_increase)
best_validation_loss = this_validation_loss
best_itr = iter
test_losses = [test_model(i) for i in range(n_test_batches)]
test_score = numpy.mean(test_losses)
print ('epoch %i, minibatch %i/%i, testing error %f %%' %(epoch, minibatch_index+1,n_train_batches,test_score*100.))
with open('best_model.pkl', 'wb') as f:
pickle.dump(params, f)
with open('Results_teacher.txt','wb') as f2:
f2.write(str(test_score*100) + '\n')
p_y_given_x = [train_predic(i) for i in range(n_train_batches)]
with open ('prob_best_model.pkl','wb') as f1:
pickle.dump(p_y_given_x,f1)
# if patience <= iter:
# done_looping = True
# break
layer_2_op_dump = [layer_guided(i) for i in range(n_train_batches)]
with open ('layer_guided.pkl','wb') as lg:
pickle.dump(layer_2_op_dump,lg)
end_time = timeit.default_timer()
# p_y_given_x = [train_model(i) for i in range(n_train_batches)]
# with open ('prob_best_model.pkl') as f:
# pickle.dump(p_y_given_x)
if testing ==0 :
print ('Optimization complete')
print ('Best validation score of %f %% obtained at iteration %i,'
'with test performance %f %%' % (best_validation_loss*100., best_itr, test_score*100 ))
print('The code ran for %.2fm' %((end_time - start_time)/60.))
