def __init__(self,batch_size,num_kernels,kernel_sizes,channel):
self.layer0_input_size = (batch_size, 1, 100, 100) # fixed size from the data
self.edge0 = (100 - kernel_sizes[0][0] + 1) / 2
self.layer0_output_size = (batch_size, num_kernels[0], self.edge0, self.edge0)
# check that we have an even multiple of 2 before pooling
assert ((100 - kernel_sizes[0][0] + 1) % 2) == 0
# The actual input is the placeholder x reshaped to the input size of the network
self.layer0_input = x[channel].reshape(self.layer0_input_size)
self.layer0 = LeNetConvPoolLayer(rng,
input=self.layer0_input,
image_shape=self.layer0_input_size,
subsample= (1,1),
filter_shape=(num_kernels[0], 1) + kernel_sizes[0],
poolsize=(2, 2))
# ## Layer 1 - Second convolutional Layer
# The second layer takes **`(batch_size, 10, 10, 10)`** as input, convolves it with 10 different **10x5x5** filters, and then downsamples (via maxpooling) in a **2x2** region. Each filter/maxpool combination produces an output of size **`(10-5+1)/2 = 3`** on a side.
# The size of the second layer's output is therefore **`(batch_size, 10, 3, 3)`**.
self.layer1_input_size = self.layer0_output_size
self.edge1 = (self.edge0 - kernel_sizes[1][0] + 1) / 2
self.layer1_output_size = (batch_size, num_kernels[1], self.edge1, self.edge1)
# check that we have an even multiple of 2 before pooling
assert ((self.edge0 - kernel_sizes[1][0] + 1) % 2) == 0
self.layer1 = LeNetConvPoolLayer(rng,
input=self.layer0.output,
image_shape=self.layer1_input_size,
subsample= (1,1),
filter_shape=(num_kernels[1], num_kernels[0]) + kernel_sizes[1],
poolsize=(2, 2))
def __init__(self, batch_size, num_kernels, kernel_sizes, channel, x, y):
self.layer0_input_size = (batch_size, 1, 100, 100
) # Input size from data
self.edge0 = (100 - kernel_sizes[0][0] + 1) / 3 # New edge size
self.layer0_output_size = (batch_size, num_kernels[0], self.edge0,
self.edge0) # Output size
assert (
(100 - kernel_sizes[0][0] + 1) % 3
) == 0 # Check pooling size # Check pooling size
# Initialize Layer 0
self.layer0_input = x.reshape(self.layer0_input_size)
self.layer0 = LeNetConvPoolLayer(rng,
input=self.layer0_input,
image_shape=self.layer0_input_size,
subsample=(1, 1),
filter_shape=(num_kernels[0], 1) +
kernel_sizes[0],
poolsize=(3, 3))
self.layer1_input_size = self.layer0_output_size # Input size Layer 1
self.edge1 = (self.edge0 - kernel_sizes[1][0] + 1) / 2 # New edge size
self.layer1_output_size = (batch_size, num_kernels[1], self.edge1,
self.edge1) # Output size
assert ((self.edge0 - kernel_sizes[1][0] + 1) %
2) == 0 # Check pooling size
# Initialize Layer 1
self.layer1 = LeNetConvPoolLayer(
rng,
input=self.layer0.output,
image_shape=self.layer1_input_size,
subsample=(1, 1),
filter_shape=(num_kernels[1], num_kernels[0]) + kernel_sizes[1],
poolsize=(2, 2))
def load_trained_model():
global if_load_trained_model
global train_model_route
global layer0_input
global layer0
global layer1
global layer2_input
global layer2
global layer3
global test_results
if_load_trained_model = 1
print "loading trained model for the first time"
trained_model_pkl = open(train_model_route, 'r')
trained_model_state_list = cPickle.load(trained_model_pkl)
trained_model_state_array = numpy.load(trained_model_pkl)
layer0_state, layer1_state, layer2_state, layer3_state = trained_model_state_array
ishape = (50, 50) # this is the size of MNIST images
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
layer0_input = x.reshape((batch_size, 1, 50, 50))
# Construct the first convolutional pooling layer:
# filtering reduces the image size to (28-5+1,28-5+1)=(24,24)
# maxpooling reduces this further to (24/2,24/2) = (12,12)
# 4D output tensor is thus of shape (batch_size,nkerns[0],12,12)
layer0 = LeNetConvPoolLayer(rng, input=layer0_input, \
image_shape=(batch_size, 1, 50, 50), \
filter_shape=(nkerns[0], 1, 10, 10), poolsize=(2, 2), \
W=layer0_state[0], b=layer0_state[1] \
)
# Construct the second convolutional pooling layer
# filtering reduces the image size to (12-5+1,12-5+1)=(8,8)
# maxpooling reduces this further to (8/2,8/2) = (4,4)
# 4D output tensor is thus of shape (nkerns[0],nkerns[1],4,4)
layer1 = LeNetConvPoolLayer(rng, input=layer0.output,
image_shape=(batch_size, nkerns[0], 20, 20),
filter_shape=(nkerns[1], nkerns[0], 5, 5), poolsize=(2, 2), \
W=layer1_state[0], b=layer1_state[1] \
)
layer2_input = layer1.output.flatten(2)
# construct a fully-connected sigmoidal layer
layer2 = HiddenLayer(rng, input=layer2_input, n_in=nkerns[1] * 8 * 8,
n_out=100, activation=T.tanh,\
W=layer2_state[0], b=layer2_state[1] \
)
# classify the values of the fully-connected sigmoidal layer
layer3 = LogisticRegression(input=layer2.output, n_in=100, n_out=3, \
W=layer3_state[0], b=layer3_state[1] \
)
test_results = theano.function(inputs=[x], \
outputs= layer3.y_pred)
def convLayer0(input2, nkerns=[20, 50]):
rng = numpy.random.RandomState(23455)
x = T.matrix('x') # the data is presented as rasterized images
layer0_input = x.reshape((1, 1, 50, 50))
print(type(layer0_input))
print(layer0_input.ndim)
# Construct the first convolutional pooling layer:
# filtering reduces the image size to (28-5+1,28-5+1)=(24,24)
# maxpooling reduces this further to (24/2,24/2) = (12,12)
# 4D output tensor is thus of shape (batch_size,nkerns[0],12,12)
layer0 = LeNetConvPoolLayer(rng,
input=layer0_input,
image_shape=(1, 1, IMAGE_WIDTH, IMAGE_HEIGHT),
filter_shape=(nkerns[0], 1, 5, 5),
poolsize=(2, 2))
f = theano.function([x], layer0.output)
print(type(input2))
output = f(input2)
print(output.shape)
for k in range(20):
for i in range(23):
for j in range(23):
output[0][k][i][j] = output[0][k][i][j] * 256
for i in range(20):
cv2.imwrite(Constants.IMG_DIR_TENCENT_SPLIT + str(i) + "111.jpg",
output[0][i])
for i in range(22):
for j in range(22):
print(output[0][0][i][j])
f = open('model.dat', 'rb')
params = cPickle.load(f)
f.close()
input = T.matrix('input')
label = T.ivector('label')
nkerns = [20, 50]
rng = np.random.RandomState(3510)
batch_size = 1
layer0_input = input.reshape((batch_size, 1, 50, 50))
layer0 = LeNetConvPoolLayer(
rng,
input=layer0_input,
image_shape=(batch_size, 1, 50, 50),
filter_shape=(nkerns[0], 1, 5, 5),
poolsize=(2, 2),
stride=(3, 3),
W=params[6].get_value(),
b=params[7].get_value(),
)
layer1 = LeNetConvPoolLayer(
rng,
input=layer0.output,
image_shape=(batch_size, nkerns[0], 8, 8),
filter_shape=(nkerns[1], nkerns[0], 4, 4),
poolsize=(1, 1),
stride=(1, 1),
W=params[4].get_value(),
b=params[5].get_value(),
)
def main_ver1_sqeu_2(learning_rate=0.05, weight_decay=0.001, n_epochs=200, nkerns=[20, 30],batch_size=500):
name = 'Sequence_'
rng = numpy.random.RandomState(23455)
datasets = loaddata_mnist()
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 = train_set_x.get_value(borrow=True).shape[0]
n_valid = valid_set_x.get_value(borrow=True).shape[0]
n_test = test_set_x.get_value(borrow=True).shape[0]
# print(str(n_train), str(n_valid),str(n_test))
test_set_x = test_set_x.reshape((n_test, 1, 28, 28))
valid_set_x = valid_set_x.reshape((n_valid, 1, 28, 28))
train_set_x = train_set_x.reshape((n_train, 1, 28, 28))
n_train_batches = n_train // batch_size
n_valid_batches = n_valid // batch_size
n_test_batches = n_test // batch_size
x = T.matrix('x')
y = T.ivector('y')
index = T.lscalar()
print('... loading the model')
layer0_input = x.reshape((batch_size, 1, 28, 28))
layer0 = LeNetConvPoolLayer(
rng,
input=layer0_input,
image_shape=(batch_size, 1, 28, 28),
filter_shape=(nkerns[0], 1, 5, 5),
poolsize=(2, 2)
)
layer1 = LeNetConvPoolLayer(
rng,
input=layer0.output,
image_shape=(batch_size, nkerns[0], 12, 12),
filter_shape=(nkerns[1], nkerns[0], 5, 5),
poolsize=(2, 2)
)
layer2_input = layer1.output.flatten(2)
# construct a fully-connected sigmoidal layer
layer2 = HiddenLayer(
rng,
input=layer2_input,
n_in=nkerns[1] * 4 * 4,
n_out=500,
activation=T.tanh
)
# classify the values of the fully-connected sigmoidal layer
layer3 = LogisticRegression_nonzeroini(rng, input=layer2.output, n_in=500, n_out=10)
cost = layer3.negative_log_likelihood(y)
params = layer3.params + layer2.params + layer1.params + layer0.params
grads = T.grad(cost, params)
updates = [
(param_i, param_i - learning_rate * grad_i)# + weight_decay * param_i)
for param_i, grad_i in zip(params, grads)]
patience_increase = 4
improvement_threshold = 0.00001
start_time = timeit.default_timer()
print('... training')
temp_time_1 = timeit.default_timer()
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
patience = 200000
validation_frequency = min(n_train_batches, patience // 2)
epoch = 0
done_looping = False
error_line = numpy.zeros(n_epochs)
test_model = theano.function(
[index],
layer3.errors(y),
givens={
layer0.input: 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],
layer3.errors(y),
givens={
layer0.input: valid_set_x[index * batch_size: (index + 1) * batch_size],
y: valid_set_y[index * batch_size: (index + 1) * batch_size]})
train_model = theano.function(
[index],
cost,
updates=updates,
givens={
layer0.input: train_set_x[index * batch_size: (index + 1) * batch_size],
y: train_set_y[index * batch_size: (index + 1) * batch_size]})
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)
if (iter + 1) % validation_frequency == 0:
validation_losses = [validate_model(i) for i
in range(n_valid_batches)]
this_validation_loss = numpy.mean(validation_losses)
print('epoch %i, minibatch %i/%i, validation error %f' %
(epoch, minibatch_index + 1, n_train_batches,
this_validation_loss*100))
error_line[epoch-1] = this_validation_loss
if this_validation_loss < best_validation_loss:
if this_validation_loss < best_validation_loss * \
improvement_threshold:
patience = max(patience, iter * patience_increase)
best_validation_loss = this_validation_loss
best_iter = 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, test error of '
'best model %f') %
(epoch, minibatch_index + 1, n_train_batches,
test_score*100))
[t_layer0, t_layer1, t_layer2_input, t_layer2, t_layer3] = \
[layer0, layer1, layer2_input, layer2, layer3]
temp_model = [layer0, layer1, layer2_input, layer2, layer3]
with open(name + str(epoch) + '.pkl', 'wb') as f:
pickle.dump(temp_model, f)
if patience <= iter:
done_looping = True
break
with open(name + 'final.pkl', 'wb') as f:
pickle.dump([t_layer0, t_layer1, t_layer2_input, t_layer2, t_layer3], f)
error_line = error_line[0:epoch-1]/100
scipy.io.savemat('Sqeuence.mat', mdict={'Error_Spectrum': error_line})
temp_time_2 = timeit.default_timer()
print('%.2fm' % ((temp_time_2 - temp_time_1) / 60.))
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, best_iter + 1, test_score))
print('The code for file ran for %.2fm' % ((end_time - start_time) / 60.))
def random_epoch_train_begining(learning_rate=0.05,
weight_decay=0.001,
nkerns=[20, 50],
n_epochs=200,
batch_size=500,
dataset='mnist.pkl.gz',
name_given='test'):
#name = 'FashionMnist_'+str(learning_rate)+'_'+str(weight_decay) + '_' + str(nkerns) + 'Rand_Trans_Relu2_Begin'
name = name_given
rng = numpy.random.RandomState(23455)
datasets = loaddata_mnist(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 = train_set_x.get_value(borrow=True).shape[0]
n_valid = valid_set_x.get_value(borrow=True).shape[0]
n_test = test_set_x.get_value(borrow=True).shape[0]
test_set_x = test_set_x.reshape((n_test, 1, 28, 28))
valid_set_x = valid_set_x.reshape((n_valid, 1, 28, 28))
train_set_x = train_set_x.reshape((n_train, 1, 28, 28))
temp_train_set_x = theano.shared(numpy.zeros(train_set_x.shape.eval(),
dtype=theano.config.floatX),
borrow=True)
temp_train_set_xx = T.Rebroadcast((1, True))(temp_train_set_x)
temp_valid_set_x = theano.shared(numpy.zeros(valid_set_x.shape.eval(),
dtype=theano.config.floatX),
borrow=True)
temp_valid_set_xx = T.Rebroadcast((1, True))(temp_valid_set_x)
temp_test_set_x = theano.shared(numpy.zeros(test_set_x.shape.eval(),
dtype=theano.config.floatX),
borrow=True)
temp_test_set_xx = T.Rebroadcast((1, True))(temp_test_set_x)
n_train_batches = n_train // batch_size
n_valid_batches = n_valid // batch_size
n_test_batches = n_test // batch_size
x = T.matrix('x')
y = T.ivector('y')
index = T.lscalar()
dummy = T.ftensor4('dummy')
update_train = (temp_train_set_x, dummy)
update_valid = (temp_valid_set_x, dummy)
update_test = (temp_test_set_x, dummy)
replace_train = theano.function([dummy],
temp_train_set_x,
updates=[update_train])
replace_valid = theano.function([dummy],
temp_valid_set_x,
updates=[update_valid])
replace_test = theano.function([dummy],
temp_test_set_x,
updates=[update_test])
print('... loading the model')
layer0_input = x.reshape((batch_size, 1, 28, 28))
layer0 = LeNetConvPoolLayer(rng,
input=layer0_input,
image_shape=(batch_size, 1, 28, 28),
filter_shape=(nkerns[0], 1, 5, 5),
poolsize=(2, 2))
layer1 = LeNetConvPoolLayer(rng,
input=layer0.output,
image_shape=(batch_size, nkerns[0], 12, 12),
filter_shape=(nkerns[1], nkerns[0], 5, 5),
poolsize=(2, 2))
layer2_input = layer1.output.flatten(2)
# construct a fully-connected sigmoidal layer
layer2 = HiddenLayer(rng,
input=layer2_input,
n_in=nkerns[1] * 4 * 4,
n_out=500,
activation=T.tanh)
# classify the values of the fully-connected sigmoidal layer
layer3 = LogisticRegression(input=layer2.output, n_in=500, n_out=10)
cost = layer3.negative_log_likelihood(y)
params = layer3.params + layer2.params + layer1.params + layer0.params
grads = T.grad(cost, params)
updates = [(param_i,
param_i - learning_rate * (grad_i + weight_decay * param_i))
for param_i, grad_i in zip(params, grads)]
patience_increase = 2
improvement_threshold = 0.995
start_time = timeit.default_timer()
rand_trans_x = numpy.random.random_integers(-10, 10, 200)
rand_trans_y = numpy.random.random_integers(-10, 10, 200)
numpy.save('rand_trans_x.npy', rand_trans_x)
numpy.save('rand_trans_y.npy', rand_trans_y)
error_line = numpy.zeros(n_epochs)
test_model = theano.function(
[index],
layer3.errors(y),
givens={
layer0.input: temp_test_set_xx[index * 500:(index + 1) * 500],
y: test_set_y[index * 500:(index + 1) * 500]
})
validate_model = theano.function(
[index],
layer3.errors(y),
givens={
layer0.input: temp_valid_set_xx[index * 500:(index + 1) * 500],
y: valid_set_y[index * 500:(index + 1) * 500]
})
train_model = theano.function(
[index],
cost,
updates=updates,
givens={
layer0.input: temp_train_set_xx[index * 500:(index + 1) * 500],
y: train_set_y[index * 500:(index + 1) * 500]
})
print('... training')
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
patience = 20000
validation_frequency = min(n_train_batches, patience // 2)
epoch = 0
done_looping = False
while (epoch < n_epochs) and (not done_looping):
horizontal = rand_trans_x[epoch]
vertical = rand_trans_y[epoch]
tran_test_set_x = theano_translation_updating(test_set_x, horizontal,
vertical).reshape(
(-1, 1, 28, 28))
tran_valid_set_x = theano_translation_updating(valid_set_x, horizontal,
vertical).reshape(
(-1, 1, 28, 28))
tran_train_set_x = theano_translation_updating(train_set_x, horizontal,
vertical).reshape(
(-1, 1, 28, 28))
replace_test(tran_test_set_x)
replace_valid(tran_valid_set_x)
replace_train(tran_train_set_x)
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 zero-one loss on validation set
validation_losses = [
validate_model(i) for i in range(n_valid_batches)
]
this_validation_loss = numpy.mean(validation_losses)
print('Horizontal Shift:', horizontal, 'Vertical Shift:',
vertical)
print('epoch %i, minibatch %i/%i, validation error %f %%' %
(epoch, minibatch_index + 1, n_train_batches,
this_validation_loss * 100.))
error_line[epoch - 1] = this_validation_loss
# if we got the best validation score until now
if this_validation_loss < best_validation_loss:
# improve patience if loss improvement is good enough
if this_validation_loss < best_validation_loss * \
improvement_threshold:
patience = max(patience, iter * patience_increase)
# save best validation score and iteration number
best_validation_loss = this_validation_loss
best_iter = iter
# test it on the test set
test_losses = [
test_model(i) for i in range(n_test_batches)
]
test_score = numpy.mean(test_losses)
print((' epoch %i, minibatch %i/%i, test error of '
'best model %f %%') %
(epoch, minibatch_index + 1, n_train_batches,
test_score * 100.))
if patience <= iter:
done_looping = True
break
[t_layer0, t_layer1, t_layer2_input, t_layer2, t_layer3] = \
[layer0, layer1, layer2_input, layer2, layer3]
with open(name + '.pkl', 'wb') as f:
pickle.dump([t_layer0, t_layer1, t_layer2_input, t_layer2, t_layer3],
f)
error_line = error_line[0:epoch - 1] * 100
scipy.io.savemat(name + '.mat', mdict={'Error_Spectrum': error_line})
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_iter + 1, test_score * 100.))
print('The code for file ran for %.2fm' % ((end_time - start_time) / 60.))
def run():
preProcess = PreProcess(load_in=True)
data = preProcess.run()
train_set_x, train_set_y = data[0], data[3]
valid_set_x, valid_set_y = data[1], data[4]
test_set_x, test_set_y = data[2], data[5]
# network parameters
num_kernels = [10, 10]
kernel_sizes = [(9, 9), (5, 5)]
#exit()
sigmoidal_output_size = 20
# training parameters
learning_rate = 0.1
batch_size = 50
# Setup 2: compute batch sizes for train/test/validation
# borrow=True gets us the value of the variable without making a copy.
n_train_batches = train_set_x.get_value(borrow=True).shape[0]
n_test_batches = test_set_x.get_value(borrow=True).shape[0]
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
n_train_batches /= batch_size
n_test_batches /= batch_size
n_valid_batches /= batch_size
# Setup 3.
# Declare inputs to network - x and y are placeholders
# that will be used in the training/testing/validation functions below.
x = T.matrix('x') # input image data
y = T.ivector('y') # input label data
# ## Layer 0 - First convolutional Layer
# The first layer takes **`(batch_size, 1, 28, 28)`** as input, convolves it with **10** different **9x9** filters, and then downsamples (via maxpooling) in a **2x2** region. Each filter/maxpool combination produces an output of size **`(28-9+1)/2 = 10`** on a side.
# The size of the first layer's output is therefore **`(batch_size, 10, 10, 10)`**.
layer0_input_size = (batch_size, 1, 100, 100) # fixed size from the data
edge0 = (100 - kernel_sizes[0][0] + 1) / 2
layer0_output_size = (batch_size, num_kernels[0], edge0, edge0)
# check that we have an even multiple of 2 before pooling
assert ((100 - kernel_sizes[0][0] + 1) % 2) == 0
# The actual input is the placeholder x reshaped to the input size of the network
layer0_input = x.reshape(layer0_input_size)
layer0 = LeNetConvPoolLayer(rng,
input=layer0_input,
image_shape=layer0_input_size,
filter_shape=(num_kernels[0], 1) +
kernel_sizes[0],
poolsize=(2, 2))
# ## Layer 1 - Second convolutional Layer
# The second layer takes **`(batch_size, 10, 10, 10)`** as input, convolves it with 10 different **10x5x5** filters, and then downsamples (via maxpooling) in a **2x2** region. Each filter/maxpool combination produces an output of size **`(10-5+1)/2 = 3`** on a side.
# The size of the second layer's output is therefore **`(batch_size, 10, 3, 3)`**.
layer1_input_size = layer0_output_size
edge1 = (edge0 - kernel_sizes[1][0] + 1) / 2
layer1_output_size = (batch_size, num_kernels[1], edge1, edge1)
# check that we have an even multiple of 2 before pooling
assert ((edge0 - kernel_sizes[1][0] + 1) % 2) == 0
layer1 = LeNetConvPoolLayer(rng,
input=layer0.output,
image_shape=layer1_input_size,
filter_shape=(num_kernels[1], num_kernels[0]) +
kernel_sizes[1],
poolsize=(2, 2))
# ## Layer 2 - Fully connected sigmoidal layer
#exit()
# The sigmoidal layer takes a vector as input.
# We flatten all but the first two dimensions, to get an input of size **`(batch_size, 30 * 4 * 4)`**.
#raw_random= raw_random.RandomStreamsBase()
srng = theano.tensor.shared_randomstreams.RandomStreams(
rng.randint(999999))
#def rectify(X):
# return T.maximum(X,0.)
def dropout(X, p=0.5):
if p > 0:
retain_prob = 1 - p
X *= srng.binomial(X.shape,
p=retain_prob,
dtype=theano.config.floatX)
X /= retain_prob
return X
layer2_input = layer1.output.flatten(2)
layer2 = HiddenLayer(rng,
input=dropout(layer2_input),
n_in=num_kernels[1] * edge1 * edge1,
n_out=sigmoidal_output_size,
activation=T.tanh)
# ## Layer 3 - Logistic regression output layer
# A fully connected logistic regression layer converts the sigmoid's layer output to a class label.
layer3 = LogisticRegression(input=layer2.output,
n_in=sigmoidal_output_size,
n_out=sport_n)
# # Training the network
# To train the network, we have to define a cost function. We'll use the Negative Log Likelihood of the model, relative to the true labels **`y`**.
# The cost we minimize during training is the NLL of the model.
# Recall: y is a placeholder we defined above
cost = layer3.negative_log_likelihood(y)
# ### Gradient descent
# We will train with Stochastic Gradient Descent. To do so, we need the gradient of the cost relative to the parameters of the model. We can get the parameters for each label via the **`.params`** attribute.
# create a list of all model parameters to be fit by gradient descent
params = layer3.params + layer2.params + layer1.params + layer0.params
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
# ## Update
updates = [
(param_i, param_i - learning_rate * grad_i) # <=== SGD update step
for param_i, grad_i in zip(params, grads)
]
index = T.lscalar(
) # index to a batch of training/validation/testing examples
train_model = theano.function(
[index],
cost,
updates=updates,
givens={
x: train_set_x[index * batch_size:(index + 1) *
batch_size], # <=== batching
y: train_set_y[index * batch_size:(index + 1) *
batch_size] # <=== batching
})
# ## Validation function
# To track progress on a held-out set, we count the number of misclassified examples in the validation set.
validate_model = theano.function(
[index],
layer3.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]
})
# ## Test function
# After training, we check the number of misclassified examples in the test set.
test_model = theano.function(
[index],
layer3.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]
})
# # Training loop
# We use SGD for a fixed number of iterations over the full training set (an "epoch"). Usually, we'd use a more complicated rule, such as iterating until a certain number of epochs fail to produce improvement in the validation set.
for epoch in range(30):
costs = [train_model(i) for i in xrange(n_train_batches)]
validation_losses = [
validate_model(i) for i in xrange(n_valid_batches)
]
print "Epoch {} NLL {:.2} %err in validation set {:.1%}".format(
epoch + 1, np.mean(costs), np.mean(validation_losses))
# ## Learned features
#filters = tile_raster_images(layer0.W.get_value(borrow=True), img_shape=(9, 9), tile_shape=(1,10), tile_spacing=(3, 3),
# scale_rows_to_unit_interval=True,
# output_pixel_vals=True)
#plt.imshow(filters)
#plt.show()
# ## Check performance on the test set
test_errors = [test_model(i) for i in range(n_test_batches)]
print "test errors: {:.1%}".format(np.mean(test_errors))
input = T.matrix('input')
label = T.ivector('label')
batch_size = 100
nkerns = [10, 25]
layer0_input = input.reshape((batch_size, 1, 50, 50))
# layer 0: convolution
# input: batch_size * 1 * 50 * 50
# conv width: (50 - 5)/3 + 1 = 16
# pool width: 16 / 2 = 8
# output: batch_size * nkerns[0] * 8 * 8
layer0 = LeNetConvPoolLayer(rng,
input=layer0_input,
image_shape=(batch_size, 1, 50, 50),
filter_shape=(nkerns[0], 1, 5, 5),
poolsize=(2, 2),
stride=(3, 3))
# layer 1: convolution
# input: batch_size * nkerns[0] * 8 * 8
# conv width: (8 - 4) + 1 = 5
# pool width: 5 / 1 = 5
# output: batch_size * nkerns[1] * 5 * 5
layer1 = LeNetConvPoolLayer(rng,
input=layer0.output,
image_shape=(batch_size, nkerns[0], 8, 8),
filter_shape=(nkerns[1], nkerns[0], 4, 4),
poolsize=(1, 1),
stride=(1, 1))
def evaluate_lenet5(learning_rate, n_epochs, nkerns, batch_size):
"""
Demonstrates lenet on a small sample of the cacophony dataset
using a network consisting of:
- two (convolutional + max pool) layers
- one fully connected hidden layer
- logistic regression to determine the final class from the hidden layer outputs
:type learning_rate: float
:param learning_rate: learning rate used (factor for the stochastic
gradient)
:type n_epochs: int
:param n_epochs: maximal number of epochs to run the optimizer
:type nkerns: list of ints
:param nkerns: number of kernels in each layer
Adapted from convolutional_mlp::evaluate_lenet5
"""
filter_size = 5 # number of pixels across for the convolutional filter
rng = numpy.random.RandomState(
23455) # Use this one for the same result each time
# rng = numpy.random.RandomState()
datasets = load_data()
# Image list, classification list
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0]
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
n_test_batches = test_set_x.get_value(borrow=True).shape[0]
n_train_batches //= batch_size
n_valid_batches //= batch_size
n_test_batches //= batch_size
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
# start-snippet-1
x = T.matrix('x') # the data is presented as rasterized images
y = T.vector('y', "int64") # the labels are presented as 1D vector of
# [int] labels
######################
# BUILD ACTUAL MODEL #
######################
print('... building the model')
# Reshape matrix of rasterized images of shape (batch_size, 48 * 64)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
# (48, 64) is the size of cacophony small images. (height, width)
layer0_input = x.reshape(
(batch_size, 1, IMAGE_HEIGHTS[0], IMAGE_WIDTHS[0]))
# Construct the first convolutional pooling layer:
# filtering reduces the image size to (48-5+1 , 64-5+1) = (44, 60)
# maxpooling reduces this further to (44/2, 60/2) = (22, 30)
# 4D output tensor is thus of shape (batch_size, nkerns[0], 22, 30)
layer0 = LeNetConvPoolLayer(
rng,
input=layer0_input,
image_shape=(batch_size, 1, IMAGE_HEIGHTS[0], IMAGE_WIDTHS[0]),
filter_shape=(nkerns[0], 1, FILTER_SIZES[0], FILTER_SIZES[0]),
poolsize=(MAX_POOLING_SIZES[0], MAX_POOLING_SIZES[0]))
# Construct the second convolutional pooling layer
# filtering reduces the image size to (22-5+1, 30-5+1) = (18, 26)
# maxpooling reduces this further to (18/2, 26/2) = (9, 13)
# 4D output tensor is thus of shape (batch_size, nkerns[1], 9, 13)
layer1 = LeNetConvPoolLayer(
rng,
input=layer0.output,
image_shape=(
batch_size, nkerns[0], IMAGE_HEIGHTS[1],
IMAGE_WIDTHS[1]), # previous layer generated 22*30 sized images
filter_shape=(nkerns[1], nkerns[0], FILTER_SIZES[1], FILTER_SIZES[1]),
poolsize=(MAX_POOLING_SIZES[1], MAX_POOLING_SIZES[1]))
# the HiddenLayer being fully-connected, it operates on 2D matrices of
# shape (batch_size, num_pixels) (i.e matrix of rasterized images).
# This will generate a matrix of shape (batch_size, nkerns[1] * 9 * 13),
# or (1, 50 * 9 * 13) with the default values.
layer2_input = layer1.output.flatten(2)
# construct a fully-connected sigmoidal layer
layer2 = HiddenLayer(
rng,
input=layer2_input,
n_in=nkerns[1] * IMAGE_HEIGHTS[2] * IMAGE_WIDTHS[
2], # 9*13 is the number of pixels in the "image" from the previous layer
n_out=batch_size,
activation=T.tanh)
# classify the values of the fully-connected sigmoidal layer
layer3 = LogisticRegression(input=layer2.output, n_in=batch_size,
n_out=5) # n_out is the number of classes
# the cost we minimize during training is the NLL of the model
cost = layer3.negative_log_likelihood(y)
# create a function to compute the mistakes that are made by the model
test_model = theano.function(
[index],
layer3.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],
layer3.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]
})
# create a list of all model parameters to be fit by gradient descent
params = layer3.params + layer2.params + layer1.params + layer0.params
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
# train_model is a function that updates the model parameters by
# SGD Since this model has many parameters, it would be tedious to
# manually create an update rule for each model parameter. We thus
# create the updates list by automatically looping over all
# (params[i], grads[i]) pairs.
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]
})
# end-snippet-1
###############
# TRAIN MODEL #
###############
print('... training')
# early-stopping parameters
patience = 10000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience // 2)
# go through this many
# minibatches before checking the network
# on the validation set; in this case we
# check every epoch
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = timeit.default_timer()
epoch = 0
done_looping = False
while (epoch < n_epochs) and (not done_looping):
epoch += 1
for minibatch_index in range(n_train_batches):
iterator = (epoch - 1) * n_train_batches + minibatch_index
if iterator % 100 == 0:
print('training @ iterator = ', iterator)
cost_ij = train_model(minibatch_index)
if (iterator + 1) % validation_frequency == 0:
# compute zero-one loss on validation set
validation_losses = [
validate_model(i) for i in range(n_valid_batches)
]
this_validation_loss = numpy.mean(validation_losses)
print('epoch %i, minibatch %i/%i, validation error %f %%' %
(epoch, minibatch_index + 1, n_train_batches,
this_validation_loss * 100.))
# if we got the best validation score until now
if this_validation_loss < best_validation_loss:
# improve patience if loss improvement is good enough
if this_validation_loss < best_validation_loss * \
improvement_threshold:
patience = max(patience, iterator * patience_increase)
# save best validation score and iteration number
best_validation_loss = this_validation_loss
best_iter = iterator
# test it on the test set
test_losses = [
test_model(i) for i in range(n_test_batches)
]
test_score = numpy.mean(test_losses)
print((' epoch %i, minibatch %i/%i, test error of '
'best model %f %%') %
(epoch, minibatch_index + 1, n_train_batches,
test_score * 100.))
if patience <= iterator:
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_iter + 1, test_score * 100.))
print(
('The code for file ' + os.path.split(__file__)[1] + ' ran for %.2fm' %
((end_time - start_time) / 60.)),
file=sys.stderr)
display_output(test_set_x, batch_size, layer0, nkerns[0])
# display the final filters for the convolutional layers
display_conv_filters("Layer 0", layer0)
display_conv_filters("Layer 1", layer1)
def __init__(self,
datasets,
nkerns=[32, 48],
batch_size=1000,
normalized_width=20,
distortion=0,
cuda_convnet=1,
params=[None, None, None, None, None, None, None, None]):
""" Demonstrates Ciresan 2012 on MNIST dataset
Some minor differences here:
---
- Ciresan initializes Conv layers with: "uniform random distribution
in the range [−0.05, 0.05]." (Ciresan IJCAI 2011)
- Ciresan uses a sigma of 6
- Ciresan uses nkerns=[20, 40] which were increased here to be nkerns=[32, 48]
in order to be compatible with cuda_convnet
:type learning_rate: float
:param learning_rate: learning rate used (factor for the stochastic
gradient)
:type n_epochs: int
:param n_epochs: maximal number of epochs to run the optimizer
:type dataset: string
:param dataset: path to the dataset used for training /testing (MNIST here)
:type nkerns: list of ints
:param nkerns: number of kernels on each layer
:type params: list of None or Numpy matricies/arrays
:param params: W/b weights in the order: layer3W, layer3b, layer2W, layer2b, layer1W, layer1b, layer0W, layer0b
"""
layer3W, layer3b, layer2W, layer2b, layer1W, layer1b, layer0W, layer0b = params
rng = numpy.random.RandomState(23455)
# TODO: could make this a theano sym variable to abstract
# loaded data from column instantiation
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
# TODO: could move this to train method
# compute number of minibatches for training, validation and testing
self.n_train_batches = train_set_x.get_value(borrow=True).shape[0]
self.n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
self.n_test_batches = test_set_x.get_value(borrow=True).shape[0]
self.n_train_batches /= batch_size
self.n_valid_batches /= batch_size
self.n_test_batches /= batch_size
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
learning_rate = T.fscalar()
# start-snippet-1
x = T.matrix('x') # the data is presented as rasterized images
y = T.ivector('y') # the labels are presented as 1D vector of
# [int] labels
######################
# BUILD ACTUAL MODEL #
######################
print '... building the column'
if distortion:
distortion_layer = ElasticLayer(x.reshape((batch_size, 29, 29)),
29,
magnitude=ALPHA,
sigma=SIGMA)
network_input = distortion_layer.output.reshape(
(batch_size, 1, 29, 29))
else:
network_input = x.reshape((batch_size, 1, 29, 29))
if cuda_convnet:
layer0_input = network_input.dimshuffle(1, 2, 3, 0)
else:
layer0_input = network_input
layer0_imageshape = (1, 29, 29,
batch_size) if cuda_convnet else (batch_size, 1,
29, 29)
layer0_filtershape = (1, 4, 4,
nkerns[0]) if cuda_convnet else (nkerns[0], 1, 4,
4)
layer0 = LeNetConvPoolLayer(rng,
input=layer0_input,
image_shape=layer0_imageshape,
filter_shape=layer0_filtershape,
poolsize=(2, 2),
cuda_convnet=cuda_convnet,
W=layer0W,
b=layer0b)
layer1_imageshape = (nkerns[0], 13, 13,
batch_size) if cuda_convnet else (batch_size,
nkerns[0], 13,
13)
layer1_filtershape = (nkerns[0], 5, 5,
nkerns[1]) if cuda_convnet else (nkerns[1],
nkerns[0], 5, 5)
layer1 = LeNetConvPoolLayer(rng,
input=layer0.output,
image_shape=layer1_imageshape,
filter_shape=layer1_filtershape,
poolsize=(3, 3),
cuda_convnet=cuda_convnet,
W=layer1W,
b=layer1b)
# the HiddenLayer being fully-connected, it operates on 2D matrices of
# shape (batch_size, num_pixels) (i.e matrix of rasterized images).
# This will generate a matrix of shape (batch_size, nkerns[1] * 4 * 4),
# or (500, 50 * 4 * 4) = (500, 800) with the default values.
if cuda_convnet:
layer2_input = layer1.output.dimshuffle(3, 0, 1, 2).flatten(2)
else:
layer2_input = layer1.output.flatten(2)
layer2 = HiddenLayer(rng,
input=layer2_input,
n_in=nkerns[1] * 3 * 3,
n_out=150,
W=layer2W,
b=layer2b,
activation=T.tanh)
# classify the values of the fully-connected sigmoidal layer
layer3 = LogisticRegression(input=layer2.output,
n_in=150,
n_out=10,
W=layer3W,
b=layer3b)
# the cost we minimize during training is the NLL of the model
cost = layer3.negative_log_likelihood(y)
# create a function to compute the mistakes that are made by the model
self.test_model = theano.function(
[index],
layer3.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]
})
# create a function to compute probabilities of all output classes
self.test_output_batch = theano.function(
[index],
layer3.p_y_given_x,
givens={
x: test_set_x[index * batch_size:(index + 1) * batch_size]
})
self.validate_model = theano.function(
[index],
layer3.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]
})
# create a list of all model parameters to be fit by gradient descent
self.params = layer3.params + layer2.params + layer1.params + layer0.params
self.column_params = [
nkerns, batch_size, normalized_width, distortion, cuda_convnet
]
# create a list of gradients for all model parameters
grads = T.grad(cost, self.params)
# train_model is a function that updates the model parameters by
# SGD Since this model has many parameters, it would be tedious to
# manually create an update rule for each model parameter. We thus
# create the updates list by automatically looping over all
# (params[i], grads[i]) pairs.
updates = [(param_i, param_i - (learning_rate) * grad_i)
for param_i, grad_i in zip(self.params, grads)]
# Suggested by Alex Krizhevsky, found on:
# http://yyue.blogspot.com/2015/01/a-brief-overview-of-deep-learning.html
optimal_ratio = 0.001
# should show what multiple current learning rate is of optimal learning rate
grads_L1 = sum([abs(grad).sum() for grad in grads])
params_L1 = sum([abs(param).sum() for param in self.params])
update_ratio = (learning_rate /
(optimal_ratio)) * (grads_L1 / params_L1)
self.train_model = theano.function(
[index, learning_rate], [cost, update_ratio],
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]
})
def runDeepLearning():
### Loading training set and separting it into training set and testing set
myDataset = Dataset()
preprocess = 0
datasets = myDataset.loadTrain(preprocess)
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
dataset_test = myDataset.loadTest(preprocess)
test_set_x, test_set_y, test_set_y_array = dataset_test[0]
# temporary solution to get the ground truth of sample out to test_set_y_array.
# the reason is that after T.cast, test_set_y becomes TensorVariable, which I do not find way to output its
# value...anyone can help?
### Model parameters
learning_rate = 0.02
n_epochs = 3000
nkerns = [
30, 40, 40
] # number of kernal at each layer, current best performance is 50.0% on testing set, kernal number is [30,40,40]
batch_size = 500
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0]
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
n_test_batches = test_set_x.get_value(borrow=True).shape[0]
n_train_batches /= batch_size
n_valid_batches /= batch_size
n_test_batches /= batch_size
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as rasterized images
y = T.ivector('y') # the labels are presented as 1D vector of
# [int] labels
ishape = (48, 48) # size of input images
nClass = 7
rng = np.random.RandomState(23455)
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
layer0_input = x.reshape((batch_size, 1, ishape[0], ishape[0]))
# Construct the first convolutional pooling layer:
# filtering reduces the image size to (28-5+1,28-5+1)=(24,24)
# maxpooling reduces this further to (24/2,24/2) = (12,12)
# 4D output tensor is thus of shape (batch_size,nkerns[0],12,12)
layer0 = LeNetConvPoolLayer(rng,
input=layer0_input,
image_shape=(batch_size, 1, ishape[0],
ishape[0]),
filter_shape=(nkerns[0], 1, 5, 5),
poolsize=(2, 2))
# Construct the second convolutional pooling layer
# filtering reduces the image size to (12-5+1,12-5+1)=(8,8)
# maxpooling reduces this further to (8/2,8/2) = (4,4)
# 4D output tensor is thus of shape (nkerns[0],nkerns[1],4,4)
layer1 = LeNetConvPoolLayer(rng,
input=layer0.output,
image_shape=(batch_size, nkerns[0], 22, 22),
filter_shape=(nkerns[1], nkerns[0], 5, 5),
poolsize=(2, 2))
layer2 = LeNetConvPoolLayer(rng,
input=layer1.output,
image_shape=(nkerns[0], nkerns[1], 9, 9),
filter_shape=(nkerns[2], nkerns[1], 2, 2),
poolsize=(2, 2))
# the TanhLayer being fully-connected, it operates on 2D matrices of
# shape (batch_size,num_pixels) (i.e matrix of rasterized images).
# This will generate a matrix of shape (20,32*4*4) = (20,512)
layer3_input = layer2.output.flatten(2)
# construct a fully-connected sigmoidal layer
layer3 = HiddenLayer(rng,
input=layer3_input,
n_in=nkerns[2] * 4 * 4,
n_out=500,
activation=T.tanh)
# classify the values of the fully-connected sigmoidal layer
layer4 = LogisticRegression(input=layer3.output, n_in=500, n_out=nClass)
# the cost we minimize during training is the NLL of the model
cost = layer4.negative_log_likelihood(y)
# create a function to compute the mistakes that are made by the model
validate_model = theano.function(
[index],
layer4.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]
})
test_model = theano.function(
[index],
layer4.errorsLabel(y),
givens={
x: test_set_x[index * batch_size:(index + 1) * batch_size],
y: test_set_y[index * batch_size:(index + 1) * batch_size]
})
# create a list of all model parameters to be fit by gradient descent
params = layer4.params + layer3.params + layer2.params + layer1.params + layer0.params
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
# train_model is a function that updates the model parameters by
# SGD Since this model has many parameters, it would be tedious to
# manually create an update rule for each model parameter. We thus
# create the updates list by automatically looping over all
# (params[i],grads[i]) pairs.
updates = []
for param_i, grad_i in zip(params, grads):
updates.append((param_i, param_i - learning_rate * grad_i))
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]
})
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 10000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = np.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
epoch = 0
done_looping = False
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
for minibatch_index in xrange(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 zero-one loss on validation set
validation_losses = [
validate_model(i) for i in xrange(n_valid_batches)
]
this_validation_loss = np.mean(validation_losses)
print('epoch %i, minibatch %i/%i, validation error %f %%' % \
(epoch, minibatch_index + 1, n_train_batches, \
this_validation_loss * 100.))
# if we got the best validation score until now
if this_validation_loss < best_validation_loss:
#improve patience if loss improvement is good enough
if this_validation_loss < best_validation_loss * \
improvement_threshold:
patience = max(patience, iter * patience_increase)
# save best validation score and iteration number
best_validation_loss = this_validation_loss
best_iter = iter
# test it on the test set
#test_losses = [test_model(i) for i in xrange(n_test_batches)]
test_output = [
test_model(i) for i in xrange(n_test_batches)
]
test_losses = [item[0] for item in test_output]
#test_y_gt = [label[0] for label in item[1] for item in test_output] #
test_y_pred = np.array(
[label for label in item[1] for item in test_output])
test_y_gt = np.array(
[label for label in item[2] for item in test_output])
#test_y_pred = np.array([item[1] for item in test_output] )
## the predicted_labels for the input
### it seems that the batchsize cannot be change in Theano.function while training model ###
#test_label = reduce(lambda x,y: x+y,test_label)
#print test_y_pred
#print test_y_gt
#print test_set_y_array
errorNum = np.count_nonzero(test_y_gt - test_y_pred)
errorSampleIndex = [
i for i in range(len(test_y_pred))
if test_y_pred[i] != test_set_y_array[i]
]
#print errorNum, len(errorSampleIndex)
test_score = np.mean(test_losses)
print((' epoch %i, minibatch %i/%i, test error of best '
'model %f %%') %
(epoch, minibatch_index + 1, n_train_batches,
test_score * 100.))
print((' on all test sample %f %%') %
((float(errorNum) / float(len(test_y_pred)) * 100.)))
if patience <= iter:
done_looping = True
break
end_time = time.clock()
print('Optimization complete.')
#TODO: write the code to save the trained model and test the trained model on test data
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
# save the misclassified samples
myDataset.plotSample(test_set_x.get_value(), test_set_y,
[i for i in range(0, 100)])
def Buildnet(params, nkerns=[20, 50], batch_size=500):
rng = numpy.random.RandomState(23455)
datasets = load_data(0)
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0]
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
n_test_batches = test_set_x.get_value(borrow=True).shape[0]
n_train_batches /= batch_size
n_valid_batches /= batch_size
n_test_batches /= batch_size
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as rasterized images
y = T.ivector('y') # the labels are presented as 1D vector of
# [int] labels
ishape = (28, 28) # this is the size of MNIST images
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
layer0_input = x.reshape((batch_size, 1, 28, 28))
# Construct the first convolutional pooling layer:
# filtering reduces the image size to (28-5+1,28-5+1)=(24,24)
# maxpooling reduces this further to (24/2,24/2) = (12,12)
# 4D output tensor is thus of shape (batch_size,nkerns[0],12,12)
layer0 = LeNetConvPoolLayer(rng,
input=layer0_input,
image_shape=(batch_size, 1, 28, 28),
filter_shape=(nkerns[0], 1, 5, 5),
poolsize=(2, 2))
# Construct the second convolutional pooling layer
# filtering reduces the image size to (12-5+1,12-5+1)=(8,8)
# maxpooling reduces this further to (8/2,8/2) = (4,4)
# 4D output tensor is thus of shape (nkerns[0],nkerns[1],4,4)
layer1 = LeNetConvPoolLayer(rng,
input=layer0.output,
image_shape=(batch_size, nkerns[0], 12, 12),
filter_shape=(nkerns[1], nkerns[0], 5, 5),
poolsize=(2, 2))
# the HiddenLayer being fully-connected, it operates on 2D matrices of
# shape (batch_size,num_pixels) (i.e matrix of rasterized images).
# This will generate a matrix of shape (20,32*4*4) = (20,512)
layer2_input = layer1.output.flatten(2)
# construct a fully-connected sigmoidal layer
layer2 = HiddenLayer(rng,
input=layer2_input,
n_in=nkerns[1] * 4 * 4,
n_out=500,
activation=T.tanh)
# classify the values of the fully-connected sigmoidal layer
layer3 = LogisticRegression(input=layer2.output, n_in=500, n_out=3)
# the cost we minimize during training is the NLL of the model
cost = layer3.negative_log_likelihood(y)
f = theano.function(
inputs=[index],
outputs=[layer2.output, layer3.y_pred, y],
givens={
x: test_set_x[index * batch_size:(index + 1) * batch_size],
y: test_set_y[index * batch_size:(index + 1) * batch_size]
})
# numepoch = len(params)
layer3.W.set_value(params[-1][0])
layer3.b.set_value(params[-1][1])
layer2.W.set_value(params[-1][2])
layer2.b.set_value(params[-1][3])
layer1.W.set_value(params[-1][4])
layer1.b.set_value(params[-1][5])
layer0.W.set_value(params[-1][6])
layer0.b.set_value(params[-1][7])
outputvectors = numpy.zeros((10000, 500))
labels = numpy.zeros((10000, 1))
reallabels = numpy.zeros((10000, 1))
for minibatch_index in xrange(n_test_batches):
vector, label, reallabel = f(minibatch_index)
outputvectors[minibatch_index * batch_size:(minibatch_index + 1) *
batch_size] = vector
labels[minibatch_index * batch_size:(minibatch_index + 1) * batch_size,
0] = label
reallabels[minibatch_index * batch_size:(minibatch_index + 1) *
batch_size, 0] = reallabel
return [outputvectors, labels, reallabels]
def perturb_bfgs(perturbation,
params,
shape,
oldoutput,
c=1,
nkerns=[20, 50],
batch_size=1):
#print '... building the model'
rng = numpy.random.RandomState(23455)
x = T.tensor4()
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
layer0_input = x.reshape((batch_size, 1, 28, 28))
# Construct the first convolutional pooling layer:
# filtering reduces the image size to (28-5+1,28-5+1)=(24,24)
# maxpooling reduces this further to (24/2,24/2) = (12,12)
# 4D output tensor is thus of shape (batch_size,nkerns[0],12,12)
layer0 = LeNetConvPoolLayer(rng,
input=layer0_input,
image_shape=(batch_size, 1, 28, 28),
filter_shape=(nkerns[0], 1, 5, 5),
poolsize=(2, 2))
# Construct the second convolutional pooling layer
# filtering reduces the image size to (12-5+1,12-5+1)=(8,8)
# maxpooling reduces this further to (8/2,8/2) = (4,4)
# 4D output tensor is thus of shape (nkerns[0],nkerns[1],4,4)
layer1 = LeNetConvPoolLayer(rng,
input=layer0.output,
image_shape=(batch_size, nkerns[0], 12, 12),
filter_shape=(nkerns[1], nkerns[0], 5, 5),
poolsize=(2, 2))
# the HiddenLayer being fully-connected, it operates on 2D matrices of
# shape (batch_size,num_pixels) (i.e matrix of rasterized images).
# This will generate a matrix of shape (20,32*4*4) = (20,512)
layer2_input = layer1.output.flatten(2)
# construct a fully-connected sigmoidal layer
layer2 = HiddenLayer(rng,
input=layer2_input,
n_in=nkerns[1] * 4 * 4,
n_out=500,
activation=T.tanh)
# classify the values of the fully-connected sigmoidal layer
layer3 = LogisticRegression(input=layer2.output, n_in=500, n_out=3)
f = theano.function(inputs=[x], outputs=[layer2.output, layer3.y_pred])
layer3.W.set_value(params[-1][0])
layer3.b.set_value(params[-1][1])
layer2.W.set_value(params[-1][2])
layer2.b.set_value(params[-1][3])
layer1.W.set_value(params[-1][4])
layer1.b.set_value(params[-1][5])
layer0.W.set_value(params[-1][6])
layer0.b.set_value(params[-1][7])
perturbed = shape
oldoutputs = oldoutput
distances = 0
perturblength = numpy.sqrt(numpy.sum(perturbation**2))
shapes = perturbed + perturbation
outputs, labels = f(shapes.reshape(1, 1, 28, 28))
print labels
for o in oldoutputs:
distances += numpy.sqrt(numpy.sum((outputs - o)**2))
distances /= len(oldoutputs)
return c * perturblength + distances
def perturb_random(params, shape, oldoutput, nkerns=[20, 50], batch_size=500):
print '... building the model'
rng = numpy.random.RandomState(23455)
x = T.tensor4()
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
layer0_input = x.reshape((batch_size, 1, 28, 28))
# Construct the first convolutional pooling layer:
# filtering reduces the image size to (28-5+1,28-5+1)=(24,24)
# maxpooling reduces this further to (24/2,24/2) = (12,12)
# 4D output tensor is thus of shape (batch_size,nkerns[0],12,12)
layer0 = LeNetConvPoolLayer(rng,
input=layer0_input,
image_shape=(batch_size, 1, 28, 28),
filter_shape=(nkerns[0], 1, 5, 5),
poolsize=(2, 2))
# Construct the second convolutional pooling layer
# filtering reduces the image size to (12-5+1,12-5+1)=(8,8)
# maxpooling reduces this further to (8/2,8/2) = (4,4)
# 4D output tensor is thus of shape (nkerns[0],nkerns[1],4,4)
layer1 = LeNetConvPoolLayer(rng,
input=layer0.output,
image_shape=(batch_size, nkerns[0], 12, 12),
filter_shape=(nkerns[1], nkerns[0], 5, 5),
poolsize=(2, 2))
# the HiddenLayer being fully-connected, it operates on 2D matrices of
# shape (batch_size,num_pixels) (i.e matrix of rasterized images).
# This will generate a matrix of shape (20,32*4*4) = (20,512)
layer2_input = layer1.output.flatten(2)
# construct a fully-connected sigmoidal layer
layer2 = HiddenLayer(rng,
input=layer2_input,
n_in=nkerns[1] * 4 * 4,
n_out=500,
activation=T.tanh)
# classify the values of the fully-connected sigmoidal layer
layer3 = LogisticRegression(input=layer2.output, n_in=500, n_out=10)
f = theano.function(inputs=[x], outputs=[layer2.output, layer3.y_pred])
layer3.W.set_value(params[-1][0])
layer3.b.set_value(params[-1][1])
layer2.W.set_value(params[-1][2])
layer2.b.set_value(params[-1][3])
layer1.W.set_value(params[-1][4])
layer1.b.set_value(params[-1][5])
layer0.W.set_value(params[-1][6])
layer0.b.set_value(params[-1][7])
# perturb 500 shapes at each iteration, with ptimes iterations
perturbed = numpy.tile(shape[0], (500, 1))
oldoutputs = numpy.tile(oldoutput, (500, 1))
label = shape[1]
ptimes = 500
imagelength = numpy.sqrt(numpy.sum(shape[0]**2))
outputlength = numpy.sqrt(numpy.sum(oldoutput**2))
p = []
s = []
for i in range(ptimes):
print 'perturbing ' + str(i) + ' ......'
perturbation = numpy.random.normal(0, 0.15, perturbed.shape)
perturblength = numpy.sqrt(numpy.sum(perturbation**2, axis=1))
shapes = perturbed + perturbation
outputs, labels = f(shapes.reshape(500, 1, 28, 28))
distances = numpy.sum((outputs - oldoutputs)**2, axis=1)
pos = numpy.argmax(distances)
print 'distance ' + str(numpy.sqrt(distances[pos]))
pert = {}
pert['perturbation'] = perturbation[pos]
pert['plength'] = perturblength[pos]
pert['ilength'] = imagelength
pert['olength'] = outputlength
pert['distance'] = numpy.sqrt(distances[pos])
pert['output'] = outputs[pos]
pert['label'] = labels[pos]
p.append(pert)
if len(numpy.nonzero(labels != label)[0]) != 0:
print 'success!' + str(label) + ' '
pos = numpy.nonzero(labels != label)[0][0]
print labels[pos]
pert = {}
pert['perturbation'] = perturbation[pos]
pert['plength'] = perturblength[pos]
pert['ilength'] = imagelength
pert['olength'] = outputlength
pert['distance'] = numpy.sqrt(distances[pos])
pert['output'] = outputs[pos]
pert['label'] = labels[pos]
s.append(pert)
return p, s
def __init__(self, n_basket, n_hidden, n_vocabulary, n_embedding_dimension):
# n_window to put together a few records of input training (may enhance the sense of sequence)
# Such that x is an n_embedding_dimension * n_window-dimensional vector
# The transformation matrix of the word vector (broadly, the property vector of the item)
iscnn = False
iscostplus = True
nkerns = n_embedding_dimension # Seemingly can not equal! . !! . . . . . First keep
filter_shape = (nkerns, 1, n_basket, 1)
# rng = np.random.RandomState(23455)
rng = np.random.RandomState(23456)
poolsize = (1, n_embedding_dimension)
print "1. Neuron parameter construction ............",
embedding = np.random.uniform(-0.5, 0.5, (n_vocabulary, n_embedding_dimension)).astype(theano.config.floatX)
embedding[-1] = 0.
self.embedding = theano.shared(
value=embedding.astype(theano.config.floatX),
name='embedding',
borrow=True
)
# Simply defining -1 as an attribute does not seem right, but you can not think of a good way to change it.
# X by u
self.u = theano.shared(
value=np.random.uniform(-0.5, 0.5, (nkerns, n_hidden)).astype(theano.config.floatX),
# This dimension should be modified like the vector dimension of the cnn output
name='u',
borrow=True
)
# H by w
self.w = theano.shared(
value=np.random.uniform(-0.5, 0.5, (n_hidden, n_hidden)).astype(theano.config.floatX),
name='w',
borrow=True
)
self.hidden_lay0 = theano.shared(
value=np.zeros(n_hidden, dtype=theano.config.floatX),
name='hidden_lay0',
borrow=True
)
fan_in = np.prod(filter_shape[1:])
fan_out = (filter_shape[0] * np.prod(filter_shape[2:]) // np.prod(poolsize))
# initialize weights with random weights
W_bound = np.sqrt(6. / (fan_in + fan_out))
self.w_cnn = theano.shared(np.asarray(rng.uniform(low=-W_bound, high=W_bound, size=filter_shape),
dtype=theano.config.floatX), borrow=True)
b_values = np.zeros((filter_shape[0],), dtype=theano.config.floatX)
self.b_cnn = theano.shared(value=b_values, borrow=True)
input_item_id = T.lmatrix('input_item_id') # The the input matrix
input_size = T.lvector('input_size')
neg_item_id = T.lmatrix('neg_item_id')
x = self.embedding[input_item_id].reshape((input_item_id.shape[0], n_basket, n_embedding_dimension))
x.name = 'x'
# y = self.embedding[next_item_id].reshape((1, n_window * n_embedding_dimension))[0]
neg = self.embedding[neg_item_id].reshape((neg_item_id.shape[0], n_basket, n_embedding_dimension))
neg.name = 'neg'
# After the embedding of the feature matrix through a cnn
# . . . Note the convolution here first
if iscnn:
cnn_x = LeNetConvPoolLayer(
rng,
input=x.reshape((x.shape[0], 1, n_basket, n_embedding_dimension)),
image_shape=(None, 1, n_basket, n_embedding_dimension),
# In fact image_shape almost no role in this variable, the first dimension casually write on the line
filter_shape=filter_shape,
W=self.w_cnn,
b=self.b_cnn,
poolsize=poolsize
)
cnn_x_output = cnn_x.output.flatten(2)
self.param = (self.embedding, self.u, self.w, self.w_cnn, self.b_cnn) # , self.v)
self.name = ('embedding', 'u', 'w', 'w_cnn', 'b_cnn')
else:
def pooling_max(abasker_t, basket_size_t):
pool_result_t = T.max(abasker_t[: basket_size_t], axis=0)
return pool_result_t
pool_result, _ = theano.scan(fn=pooling_max,
sequences=[x.reshape((x.shape[0], n_basket, n_embedding_dimension)),
input_size])
cnn_x = pool_result
cnn_x_output = cnn_x.flatten(2)
self.param = (self.embedding, self.u, self.w)
self.name = ('embedding', 'u', 'w')
print "done"
print "2. Loss function construction ..............",
def recurrence(x_t, h_tml):
# Defines the looping function
h_t = T.nnet.sigmoid(T.dot(x_t, self.u) + T.dot(h_tml, self.w))
return h_t
h, _ = theano.scan(
fn=recurrence,
sequences=cnn_x_output,
outputs_info=[self.hidden_lay0]
)
h.name = 'h'
self.user_feature = h[-1, :] #
self.user_feature.name = 'user_feature'
# Loss function
if iscostplus:
def cla_cost(x_t, h_t):
s_tt = T.dot((x[x_t+1][:input_size[x_t+1]] - neg[x_t+1][:input_size[x_t+1]]), h_t)
s_t = T.sum(T.log(1 + T.exp(-s_tt)))
return s_t
s, _ = theano.scan(
fn=cla_cost,
sequences=[T.arange(x.shape[0]-1), h]
)
cost = T.sum(s)
else:
cost_temp = T.dot(x[-1][:input_size[-1]], h[-2]) - T.dot(neg[-1][:input_size[-1]], h[-2])
cost = T.sum(T.log(1 + T.exp(-cost_temp)))
print "done"
print "3. Random gradient descending update formula ......",
learning_rate = T.dscalar('learning_rate')
lamda = T.dscalar('lamda')
gradient = T.grad(cost, self.param)
updates = [(p, p - learning_rate * (g + p * lamda)) for p, g in zip(self.param, gradient)]
print "done"
print "4. Predictive function definition ..............",
y_pred = T.argsort(T.dot(self.embedding, self.user_feature)) # quel -6 fa prendere la top 5 in ordine crescente
self.predict = theano.function(inputs=[input_item_id, input_size], outputs=y_pred)
print "done"
print "5. Training function definition ..............",
self.train = theano.function(inputs=[input_item_id, neg_item_id, input_size, learning_rate, lamda],
outputs=cost,
updates=updates)
print "done"
print "6. Evaluation function definition ..............",
self.evaluation_recall_6 = theano.function(inputs=[input_item_id, input_size], outputs=y_pred)
print "done"
self.normalize = theano.function(inputs=[],
updates={self.embedding:\
self.embedding/T.sqrt((self.embedding**2).sum(axis=1)).dimshuffle(0, 'x')*10})
batch_size = 1
# allocate symbolic variables for the data
#index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as rasterized images
# Reshape matrix of rasterized images of shape (1, 50*50)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
layer0_input = x.reshape((batch_size, 1, 50, 50))
# Construct the first convolutional pooling layer:
# filtering reduces the image size to (28-5+1,28-5+1)=(24,24)
# maxpooling reduces this further to (24/2,24/2) = (12,12)
# 4D output tensor is thus of shape (batch_size,nkerns[0],12,12)
layer0 = LeNetConvPoolLayer(rng, input=layer0_input, \
image_shape=(batch_size, 1, 50, 50), \
filter_shape=(nkerns[0], 1, 10, 10), poolsize=(2, 2) \
)
# Construct the second convolutional pooling layer
# filtering reduces the image size to (12-5+1,12-5+1)=(8,8)
# maxpooling reduces this further to (8/2,8/2) = (4,4)
# 4D output tensor is thus of shape (nkerns[0],nkerns[1],4,4)
layer1 = LeNetConvPoolLayer(rng, input=layer0.output,
image_shape=(batch_size, nkerns[0], 20, 20),
filter_shape=(nkerns[1], nkerns[0], 5, 5), poolsize=(2, 2) \
)
# the TanhLayer being fully-connected, it operates on 2D matrices of
# shape (batch_size,num_pixels) (i.e matrix of rasterized images).
# This will generate a matrix of shape (20,32*4*4) = (20,512)
layer2_input = layer1.output.flatten(2)
def display(params, digit, epoch, mode = 'mat', size = (56, 56)):
#epoch contains a list of numbers to show
#for example, epoch = [0, 2, 4] can show epoch 0 (original stage) and epoch 2 4
#after running the CNN, params can be used directly, and can also use numpy.load('params.npy') to get
#digit is a single digit of image set, for example, digit = train_set_x.get_value()[number]
nkerns=[20, 50]
rng = numpy.random.RandomState(23455)
#show original digit
if os.path.exists('digit') == 0:
os.mkdir('digit')
if mode == 'png':
plt.figure(1)
plt.gray()
plt.axis('off')
plt.imshow(digit.reshape(size))
plt.savefig('digit/activity of layer0 (original digit).png')
digit = digit.reshape(1, 1, size[0], size[1])
inputdigit = T.tensor4()
#building CNN with exactly the same parameters
print '...building layer1'
layer0_input = inputdigit
layer0 = LeNetConvPoolLayer(rng, input=layer0_input,
image_shape=(1, 1, size[0], size[1]),
filter_shape=(nkerns[0], 1, 5, 5), poolsize=(2, 2))
print '...building layer2'
layer1 = LeNetConvPoolLayer(rng, input=layer0.output,
image_shape=(1, nkerns[0], (size[0] - 4) / 2, (size[1] - 4) / 2),
filter_shape=(nkerns[1], nkerns[0], 5, 5), poolsize=(2, 2))
print '...building layer3'
layer2_input = layer1.output.flatten(2)
layer2 = HiddenLayer(rng, input=layer2_input, n_in=nkerns[1] * (size[0] / 4 - 3) * (size[1] / 4 - 3),
n_out=500, activation=T.tanh)
print '...building layer4'
layer3 = LogisticRegression(input=layer2.output, n_in=500, n_out=10)
f = theano.function(inputs = [inputdigit], outputs = [layer0.conv_out, layer0.output, layer1.conv_out, layer1.output, layer2.output, layer3.p_y_given_x, layer3.y_pred])
#export filters and activity in different epochs
for num in epoch:
print '...epoch ' + str(num)
layer3.W.set_value(params[num][0])
layer3.b.set_value(params[num][1])
layer2.W.set_value(params[num][2])
layer2.b.set_value(params[num][3])
layer1.W.set_value(params[num][4])
layer1.b.set_value(params[num][5])
layer0.W.set_value(params[num][6])
layer0.b.set_value(params[num][7])
[conv0, output0, conv1, output1, output2, output3, y] = f(digit)
if mode == 'png':
plt.figure(2)
plt.gray()
for i in range(nkerns[0]):
plt.subplot(4, 5, i + 1)
plt.axis('off')
plt.imshow(layer0.W.get_value()[i, 0])
plt.savefig('digit/filter of layer1 in epoch ' + str(num) + '.png')
plt.figure(3)
plt.gray()
for i in range(nkerns[1]):
plt.subplot(5, 10, i + 1)
plt.axis('off')
plt.imshow(layer1.W.get_value()[i, 0])
plt.savefig('digit/filter of layer2 in epoch ' + str(num) + '.png')
plt.figure(4)
plt.gray()
plt.axis('off')
plt.imshow(layer2.W.get_value())
plt.savefig('digit/filter of layer3 in epoch ' + str(num) + '.png')
plt.figure(5)
plt.gray()
plt.axis('off')
plt.imshow(layer3.W.get_value())
plt.savefig('digit/filter of layer4 in epoch ' + str(num) + '.png')
plt.figure(6)
plt.gray()
for i in range(nkerns[0]):
plt.subplot(4, 5, i + 1)
plt.axis('off')
plt.imshow(output0[0, i])
plt.savefig('digit/activity of layer1 after downsampling in epoch ' + str(num) + '.png')
plt.figure(7)
plt.gray()
plt.axis('off')
for i in range(nkerns[1]):
plt.subplot(5, 10, i + 1)
plt.axis('off')
plt.imshow(conv1[0, i])
plt.savefig('digit/activity of layer2 before downsampling in epoch ' + str(num) + '.png')
plt.figure(8)
plt.gray()
plt.axis('off')
for i in range(nkerns[0]):
plt.subplot(4, 5, i + 1)
plt.axis('off')
plt.imshow(conv0[0, i])
plt.savefig('digit/activity of layer1 before downsampling in epoch ' + str(num) + '.png')
plt.figure(9)
plt.gray()
for i in range(nkerns[1]):
plt.subplot(5, 10, i + 1)
plt.axis('off')
plt.imshow(output1[0, i])
plt.savefig('digit/activity of layer2 after downsampling in epoch ' + str(num) + '.png')
plt.figure(10)
plt.gray()
plt.axis('off')
plt.imshow(numpy.tile(output2, (10, 1)))
plt.savefig('digit/activity of layer3 in epoch ' + str(num) + '.png')
plt.figure(11)
plt.gray()
plt.axis('off')
plt.imshow(numpy.tile(output3, (10, 1)))
plt.savefig('digit/activity of layer4 in epoch ' + str(num) + '.png')
if mode == 'mat':
sio.savemat('digit in epoch ' + str(num) + '.mat', {'ActivityOfLayer0' : digit.reshape(size),
'ActivityOfLayer1before' : conv0[0],
'ActivityOfLayer1after' : output0[0],
'ActivityOfLayer2before' : conv1[0],
'ActivityOfLayer2after' : output1[0],
'ActivityOfLayer3' : output2,
'ActivityOfLayer4' : output3,
'FilterOfLayer1' : layer0.W.get_value()[:, 0, :, :],
'FilterOfLayer2' : layer1.W.get_value()[:, 0, :, :],
'FilterOfLayer3' : layer2.W.get_value(),
'FilterOfLayer4' : layer3.W.get_value(),
'y_predict' : y})
return y
def evaluate_lenet5(learning_rate=0.05, n_epochs=10,
nkerns=[20, 50], batch_size=50):
global train_dataset_route
global valid_dataset_route
global train_limit
global valid_limit
print train_dataset_route, type(train_dataset_route)
""" Demonstrates lenet on MNIST dataset
:type learning_rate: float
:param learning_rate: learning rate used (factor for the stochastic
gradient)
:type n_epochs: int
:param n_epochs: maximal number of epochs to run the optimizer
:type nkerns: list of ints
:param nkerns: number of kernels on each layer
"""
rng = numpy.random.RandomState(23455)
datasets = load_data.load_spc_data(train_dataset_route, valid_dataset_route, train_limit, valid_limit)
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0]
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
n_train_batches /= batch_size
n_valid_batches /= batch_size
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as rasterized images
y = T.ivector('y') # the labels are presented as 1D vector of
# [int] labels
ishape = (100, 100) # this is the size of MNIST images
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
layer0_input = x.reshape((batch_size, 1, 100, 100))
# Construct the first convolutional pooling layer:
# filtering reduces the image size to (28-5+1,28-5+1)=(24,24)
# maxpooling reduces this further to (24/2,24/2) = (12,12)
# 4D output tensor is thus of shape (batch_size,nkerns[0],12,12)
layer0 = LeNetConvPoolLayer(rng, input=layer0_input,
image_shape=(batch_size, 1, 100, 100),
filter_shape=(nkerns[0], 1, 40, 40), poolsize=(2, 2))
# Construct the second convolutional pooling layer
# filtering reduces the image size to (12-5+1,12-5+1)=(8,8)
# maxpooling reduces this further to (8/2,8/2) = (4,4)
# 4D output tensor is thus of shape (nkerns[0],nkerns[1],4,4)
layer1 = LeNetConvPoolLayer(rng, input=layer0.output,
image_shape=(batch_size, nkerns[0], 30, 30),
filter_shape=(nkerns[1], nkerns[0], 15, 15), poolsize=(2, 2))
# the TanhLayer being fully-connected, it operates on 2D matrices of
# shape (batch_size,num_pixels) (i.e matrix of rasterized images).
# This will generate a matrix of shape (20,32*4*4) = (20,512)
layer2_input = layer1.output.flatten(2)
# construct a fully-connected sigmoidal layer
layer2 = HiddenLayer(rng, input=layer2_input, n_in=nkerns[1] * 8 * 8,
n_out=100, activation=T.tanh)
# classify the values of the fully-connected sigmoidal layer
layer3 = LogisticRegression(input=layer2.output, n_in=100, n_out=2)
# the cost we minimize during training is the NLL of the model
cost = layer3.negative_log_likelihood(y)
# create a function to compute the mistakes that are made by the model
'''
test_model = theano.function([index], layer3.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]})
test_results = theano.function(inputs=[index],
outputs= layer3.y_pred,
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], layer3.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]})
# create a list of all model parameters to be fit by gradient descent
params = layer3.params + layer2.params + layer1.params + layer0.params
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
# train_model is a function that updates the model parameters by
# SGD Since this model has many parameters, it would be tedious to
# manually create an update rule for each model parameter. We thus
# create the updates list by automatically looping over all
# (params[i],grads[i]) pairs.
updates = []
for param_i, grad_i in zip(params, grads):
updates.append((param_i, param_i - learning_rate * grad_i))
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]})
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 10000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
epoch = 0
done_looping = False
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
for minibatch_index in xrange(n_train_batches):
iter = (epoch - 1) * n_train_batches + minibatch_index
if iter % 100 == 0:
print 'training @ iter = ', iter , ' patience = ' , patience
cost_ij = train_model(minibatch_index)
if (iter + 1) % validation_frequency == 0:
# compute zero-one loss on validation set
validation_losses = [validate_model(i) for i
in xrange(n_valid_batches)]
this_validation_loss = numpy.mean(validation_losses)
print('epoch %i, minibatch %i/%i, validation error %f %%' % \
(epoch, minibatch_index + 1, n_train_batches, \
this_validation_loss * 100.))
# if we got the best validation score until now
if this_validation_loss < best_validation_loss:
#improve patience if loss improvement is good enough
if this_validation_loss < best_validation_loss * \
improvement_threshold:
patience = max(patience, iter * patience_increase)
# save best validation score and iteration number
best_validation_loss = this_validation_loss
best_iter = iter
layer0_state = layer0.__getstate__()
layer1_state = layer1.__getstate__()
layer2_state = layer2.__getstate__()
layer3_state = layer3.__getstate__()
trained_model_list = [layer0_state, layer1_state, layer2_state, layer3_state]
trained_model_array = numpy.asarray(trained_model_list)
classifier_file = open(train_model_route, 'w')
cPickle.dump([1,2,3], classifier_file, protocol=2)
numpy.save(classifier_file, trained_model_array)
classifier_file.close()
if patience <= iter:
done_looping = True
print patience , iter
break
end_time = time.clock()
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
def create_cf_cnn(n_in, n_out, nkerns=[20, 50]):
params_DB = shelve.open('params_cnn.dat')
best_params = params_DB['params']
params_DB.close()
x = T.vector('x')
rng = numpy.random.RandomState(1234)
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
layer0_input = x.reshape((1, 1, 30, 50))
# Construct the first convolutional pooling layer:
# filtering reduces the image size to (28-5+1,28-5+1)=(24,24)
# maxpooling reduces this further to (24/2,24/2) = (12,12)
# 4D output tensor is thus of shape (batch_size,nkerns[0],12,12)
layer0 = LeNetConvPoolLayer(rng,
input=layer0_input,
image_shape=(1, 1, 30, 50),
filter_shape=(nkerns[0], 1, 5, 5),
poolsize=(2, 2))
# Construct the second convolutional pooling layer
# filtering reduces the image size to (12-5+1,12-5+1)=(8,8)
# maxpooling reduces this further to (8/2,8/2) = (4,4)
# 4D output tensor is thus of shape (nkerns[0],nkerns[1],4,4)
layer1 = LeNetConvPoolLayer(rng,
input=layer0.output,
image_shape=(1, nkerns[0], 13, 23),
filter_shape=(nkerns[1], nkerns[0], 5, 5),
poolsize=(2, 2))
# the HiddenLayer being fully-connected, it operates on 2D matrices of
# shape (batch_size,num_pixels) (i.e matrix of rasterized images).
# This will generate a matrix of shape (20,32*4*4) = (20,512)
layer2_input = layer1.output.flatten(2)
# construct a fully-connected sigmoidal layer
layer2 = HiddenLayer(rng,
input=layer2_input,
n_in=nkerns[1] * 4 * 9,
n_out=500,
activation=T.tanh)
# classify the values of the fully-connected sigmoidal layer
layer3 = LogisticRegression(input=layer2.output, n_in=500, n_out=2)
params = layer3.params + layer2.params + layer1.params + layer0.params
#print best_params[0].get_value()
for i in range(len(best_params)):
try:
inc = T.vector('inc')
setvalue = theano.function([inc],
params[i],
updates=[(params[i], inc)])
setvalue(best_params[i])
except:
try:
inc = T.matrix('inc')
setvalue = theano.function([inc],
params[i],
updates=[(params[i], inc)])
setvalue(best_params[i])
except:
inc = T.tensor4('inc')
setvalue = theano.function([inc],
params[i],
updates=[(params[i], inc)])
setvalue(best_params[i])
#print classifier.params[0].get_value()
#print classifier.logRegressionLayer.W.get_value()
vect = T.vector('vect')
cf = theano.function(inputs=[vect],
outputs=layer3.p_y_given_x,
givens={x: vect})
return cf
def evaluate_lenet5(dataset_route=DataHome+"DogVsCat_test_feature_2500.csv", \
nkerns=[20, 50], batch_size=5):
""" Demonstrates lenet on MNIST dataset
:type learning_rate: float
:param learning_rate: learning rate used (factor for the stochastic
gradient)
:type n_epochs: int
:param n_epochs: maximal number of epochs to run the optimizer
:type dataset: string
:param dataset: path to the dataset used for training /testing (MNIST here)
:type nkerns: list of ints
:param nkerns: number of kernels on each layer
"""
rng = numpy.random.RandomState(23455)
trained_model_pkl = open(ModelHome + train_model_route, 'r')
trained_model_state_list = cPickle.load(trained_model_pkl)
trained_model_state_array = numpy.load(trained_model_pkl)
layer0_state, layer1_state, layer2_state, layer3_state = trained_model_state_array
test_set = tdtf.read_data_to_ndarray(dataset_route, limit=None, header_n=0)
test_set_x, id_arr = test_set
datasets = load_data.shared_dataset(test_set)
test_set_x, test_set_y = datasets
print test_set_x.shape, test_set_y.shape
# compute number of minibatches for training, validation and testing
n_test_batches = test_set_x.get_value(borrow=True).shape[0]
n_test_batches /= batch_size
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as rasterized images
y = T.ivector('y') # the labels are presented as 1D vector of
# [int] labels
ishape = (50, 50) # this is the size of MNIST images
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
layer0_input = x.reshape((batch_size, 1, 50, 50))
# Construct the first convolutional pooling layer:
# filtering reduces the image size to (28-5+1,28-5+1)=(24,24)
# maxpooling reduces this further to (24/2,24/2) = (12,12)
# 4D output tensor is thus of shape (batch_size,nkerns[0],12,12)
layer0 = LeNetConvPoolLayer(rng, input=layer0_input, \
image_shape=(batch_size, 1, 50, 50), \
filter_shape=(nkerns[0], 1, 10, 10), poolsize=(2, 2), \
W=layer0_state[0], b=layer0_state[1] \
)
# Construct the second convolutional pooling layer
# filtering reduces the image size to (12-5+1,12-5+1)=(8,8)
# maxpooling reduces this further to (8/2,8/2) = (4,4)
# 4D output tensor is thus of shape (nkerns[0],nkerns[1],4,4)
layer1 = LeNetConvPoolLayer(rng, input=layer0.output,
image_shape=(batch_size, nkerns[0], 20, 20),
filter_shape=(nkerns[1], nkerns[0], 5, 5), poolsize=(2, 2), \
W=layer1_state[0], b=layer1_state[1] \
)
# the TanhLayer being fully-connected, it operates on 2D matrices of
# shape (batch_size,num_pixels) (i.e matrix of rasterized images).
# This will generate a matrix of shape (20,32*4*4) = (20,512)
layer2_input = layer1.output.flatten(2)
# construct a fully-connected sigmoidal layer
layer2 = HiddenLayer(rng, input=layer2_input, n_in=nkerns[1] * 8 * 8,
n_out=100, activation=T.tanh,\
W=layer2_state[0], b=layer2_state[1] \
)
# classify the values of the fully-connected sigmoidal layer
layer3 = LogisticRegression(input=layer2.output, n_in=100, n_out=2, \
W=layer3_state[0], b=layer3_state[1] \
)
print "predicting"
start_time = time.clock()
# create a function to compute the mistakes that are made by the model
test_results = theano.function(
inputs=[index],
outputs=layer3.y_pred,
givens={x: test_set_x[index * batch_size:(index + 1) * batch_size]})
test_res = [test_results(i) for i in xrange(n_test_batches)]
print test_res
id_l = []
label_l = []
index = 0
for arr in test_res:
for label in arr:
label_l.append(label)
id_l.append(id_arr[index])
index += 1
tdtf.wr_to_csv(header=['id', 'label'],
id_list=id_l,
pred_list=label_l,
filename=test_label_route)
end_time = time.clock()
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def train(learning_rate=0.1, n_epochs=10, kernel_shapes = [7,5],
nkerns=[15,15], batch_size=1000, batch_type = 'fast',
mynet = 'best', representation='raw', momentum=0, history=4):
# TODO: implement history of boards
rng = numpy.random.RandomState(42)
trainP = 0.998
validP = 0.001
testP = 0.001
print "... Reading cached values ..."
(trainCumLengths,validCumLengths,testCumLengths,filenames) = pickle.load(open("results/lengths.cache",'r'))
print "... Getting filenames ..."
datasetKGS = "../../go-data"
datasetPro = "../../pro-GoGod"
# use both datasets, test and valid set are only Pro games
# fn1 = readGame.getFilenames(datasetKGS,1,0,1)[0]
# random.shuffle(fn1)
# fn2 = readGame.getFilenames(datasetPro,1,0,1)[0]
# NOTE: last 5% of professional games never used!
# fn2 = fn2[:int(len(fn2)*0.95)]
# random.shuffle(fn2)
# filenames = fn2 #fn1 + fn2
n = len(filenames)
print "... Learning set contains " + str(n) + " games"
print "... Computing cumulative game lengths ..."
trainNames = filenames[:int(trainP*n)]
validNames = filenames[int(trainP*n):int(trainP*n+validP*n)]
testNames = filenames[int(trainP*n+validP*n):int(trainP*n+validP*n+testP*n)]
# random.shuffle(trainNames)
# trainCumLengths = readGame.getCumGameLengths(trainNames)
# validCumLengths = readGame.getCumGameLengths(validNames)
# testCumLengths = readGame.getCumGameLengths(testNames)
# fw = open("results/"lengths.cache","wb")
# pickle.dump((trainCumLengths,validCumLengths,testCumLengths,filenames),fw)
# fw.close()
print "... Preprocessing initial batches ..."
minn = batch_size / 80 +1
temp = time.time()
test_batch_x, test_batch_y = utils.shared_dataset(readGame.processSGFs(testNames[:minn],representation),batch_size=batch_size)
train_batch_x, train_batch_y = utils.shared_dataset(readGame.processSGFs(trainNames[:minn],representation),batch_size=batch_size)
valid_batch_x, valid_batch_y = utils.shared_dataset(readGame.processSGFs(validNames[:minn],representation),batch_size=batch_size)
print " average processing time per game: " + str((time.time()-temp)/18.0) + " seconds, per epoch: " + str(int((time.time()-temp)/18*n/60/60)) + " hours"
# compute number of minibatches for training, validation and testing
n_train_batches = trainCumLengths[-1]
n_valid_batches = validCumLengths[-1]
n_test_batches = testCumLengths[-1]
n_train_batches /= batch_size
n_valid_batches /= batch_size
n_test_batches /= batch_size
# allocate symbolic variables for the data
iteration = T.lscalar() # iteration number of a minibatch
x = T.matrix('x') # the data is presented as rasterized images
y = T.ivector('y') # the labels are presented as 1D vector of
# [int] labels
gs = 19 # size of the go board
ishape = (gs, gs) # this is the size of MNIST images
fw = open("results/"+mynet+"_"+str(learning_rate)+"_"+str(nkerns[0])+".res","w")
######################
# BUILD ACTUAL MODEL #
######################
print '... Building the model ...'
nc = 2 if representation=='raw' else 6 # if raw
nc *= 1+history
if mynet == "default":
# default is 7x7, regular 3 kernels
layer0_input = x.reshape((batch_size, nc, gs, gs))
layer0 = LeNetConvPoolLayer(rng, input=layer0_input,
image_shape=(batch_size, nc, gs, gs),
filter_shape=(nkerns[0], nc, 7, 7), poolsize=(1, 1))
layer2_input = layer0.output.flatten(2)
layer2 = HiddenLayer(rng, input=layer2_input, n_in=nkerns[0] * 13 * 13,
n_out=500, activation=T.tanh)
layer3 = LogisticRegression(input=layer2.output, n_in=500, n_out=361)
cost = layer3.negative_log_likelihood(y)
# prevGrads = [theano.shared(numpy.zeros((500,361),dtype=theano.config.floatX),borrow=True),
# theano.shared(numpy.zeros((361,),dtype=theano.config.floatX),borrow=True),
# theano.shared(numpy.zeros((nkerns[0] *13*13,500), dtype=theano.config.floatX),borrow=True),
# theano.shared(numpy.zeros((500,),dtype=theano.config.floatX),borrow=True),
# theano.shared(numpy.zeros((nkerns[0],nc,7,7),dtype=theano.config.floatX),borrow=True),
# theano.shared(numpy.zeros((nkerns[0],),dtype=theano.config.floatX),borrow=True),
# ]
params = layer3.params + layer2.params + layer0.params
if mynet == "best":
ks = kernel_shapes
sp1= gs-ks[0]+1
sp2= sp1-ks[1]+1
layer0_input = x.reshape((batch_size, nc, gs, gs))
layer0 = LeNetConvPoolLayer(rng, input=layer0_input,
image_shape=(batch_size, nc, gs, gs),
filter_shape=(nkerns[0], nc, ks[0], ks[0]), poolsize=(1, 1))
layer1 = LeNetConvPoolLayer(rng, input=layer0.output,
image_shape=(batch_size, nkerns[0], sp1, sp1),
filter_shape=(nkerns[1], nkerns[0], ks[1], ks[1]), poolsize=(1, 1))
layer3 = LogisticRegression(input=layer1.output.flatten(2), n_in=nkerns[1]*sp2*sp2, n_out=gs*gs)
cost = layer3.negative_log_likelihood(y)
prevGrads = [theano.shared(numpy.zeros((nkerns[1]*9*9,361),dtype=theano.config.floatX),borrow=True),
theano.shared(numpy.zeros((gs*gs,),dtype=theano.config.floatX),borrow=True),
theano.shared(numpy.zeros((nkerns[0],nkerns[1],ks[1],ks[1]), dtype=theano.config.floatX),borrow=True),
theano.shared(numpy.zeros((nkerns[1],),dtype=theano.config.floatX),borrow=True),
theano.shared(numpy.zeros((nkerns[0],nc,ks[0],ks[0]),dtype=theano.config.floatX),borrow=True),
theano.shared(numpy.zeros((nkerns[0],),dtype=theano.config.floatX),borrow=True),
]
params = layer3.params + layer1.params + layer0.params
if mynet == "padded": # TODO: add zero padding test deeper architectures
ks = kernel_shapes
sp1= gs-ks[0]+1
sp2= sp1-ks[1]+1
layer0_input = x.reshape((batch_size, nc, gs, gs))
layer0 = LeNetConvPoolLayer(rng, input=layer0_input,
image_shape=(batch_size, nc, gs, gs),
filter_shape=(nkerns[0], nc, ks[0], ks[0]), poolsize=(1, 1))
layer1 = LeNetConvPoolLayer(rng, input=layer0.output,
image_shape=(batch_size, nkerns[0], sp1, sp1),
filter_shape=(nkerns[1], nkerns[0], ks[1], ks[1]), poolsize=(1, 1))
layer3 = LogisticRegression(input=layer1.output.flatten(2), n_in=nkerns[1]*sp2*sp2, n_out=gs*gs)
cost = layer3.negative_log_likelihood(y)
params = layer3.params + layer1.params + layer0.params
# create a function to compute the mistakes that are made by the model
test_model = theano.function([], layer3.errors(y),
givens={
x: test_batch_x,
y: T.cast(test_batch_y, 'int32')})
validate_model = theano.function([], layer3.errors(y),
givens={
x: valid_batch_x,
y: T.cast(valid_batch_y, 'int32')})
predictions = theano.function([], layer3.get_predictions(),
givens={
x: valid_batch_x})
conditional_dist = theano.function([], layer3.get_conditional_dist(),
givens={
x: valid_batch_x})
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
# train_model is a function that updates the model parameters by
# SGD Since this model has many parameters, it would be tedious to
# manually create an update rule for each model parameter. We thus
# create the updates list by automatically looping over all
# (params[i],grads[i]) pairs.
updates = []
#adjusted_rate = learning_rate - iteration*(learning_rate/(float(n_epochs) * n_train_batches))
adjusted_rate = learning_rate if T.lt(iteration,3000*200) else 0.1*learning_rate
for param_i, grad_i in zip(params, grads):#, prev_grad_i , prevGrads):
updates.append((param_i, param_i - adjusted_rate * grad_i))# - momentum * prev_grad_i))
#for i,grad in enumerate(grads):
# updates.append((prevGrads[i], grad))
train_model = theano.function([iteration], cost, updates=updates,
givens={
x: train_batch_x,
y: T.cast(train_batch_y, 'int32')},on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... Training ...'
# early-stopping parameters
patience = 10000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.999 # a relative improvement of this much is
# considered significant
validation_frequency = 10000 # min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
epoch = 0
done_looping = False
stime = time.time()
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
for minibatch_index in xrange(n_train_batches):
iter = (epoch - 1) * n_train_batches + minibatch_index
if iter % 1000 == 0:
print 'training @ iter = ', iter
pickle.dump((updates,cost,layer0,layer1,layer3,test_model,predictions,conditional_dist),open("results/"+str(batch_size)+representation+str(history)+".model","w"))
if iter ==5:
print 'estimated train time per epoch = '+ str((time.time() - stime) * n_train_batches/60.0/iter/60.0) + " hours"
ax,ay = getBatch(trainNames, minibatch_index, trainCumLengths, batch_size,representation,batchType=batch_type,history=history)
train_batch_x.set_value(ax)
train_batch_y.set_value(ay)
cost_ij = train_model(iter)
if (iter + 1) % validation_frequency == 0 or iter==5:
# compute zero-one loss on validation set
validation_losses = []
for i in xrange(n_valid_batches):
vx,vy = getBatch(validNames, i, validCumLengths, batch_size,representation,batchType='fast',history=history)
valid_batch_x.set_value(vx)
valid_batch_y.set_value(vy)
validation_losses.append(validate_model())
this_validation_loss = numpy.mean(validation_losses)
print('epoch %i, minibatch %i/%i, validation error %f %%' % \
(epoch, minibatch_index + 1, n_train_batches, \
this_validation_loss * 100.))
# if we got the best validation score until now
if this_validation_loss < best_validation_loss:
#improve patience if loss improvement is good enough
if this_validation_loss < best_validation_loss * \
improvement_threshold:
patience = max(patience, iter * patience_increase)
# save best validation score and iteration number
best_validation_loss = this_validation_loss
best_iter = iter
# test it on the test set
test_losses=[]
for i in xrange(n_test_batches):
tx,ty = getBatch(testNames, i, testCumLengths, batch_size,representation,batchType='fast',history=history)
test_batch_x.set_value(tx)
test_batch_y.set_value(ty)
test_losses.append(test_model())
test_score = numpy.mean(test_losses)
print((' epoch %i, minibatch %i/%i, test error of best '
'model %f %%') %
(epoch, minibatch_index + 1, n_train_batches,
test_score * 100.))
fw.write("Epoch "+str(epoch) + ": " +str((1-this_validation_loss)*100.)+ "%\n")
pickle.dump((updates,cost,layer0,layer1,layer3,test_model,predictions,conditional_dist),open("results/"+str(batch_size)+representation+str(history)+".model","w"))
#if patience <= iter:
# done_looping = True
# break
fw.close()
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as rasterized images
y = T.ivector('y') # the labels are presented as 1D vector of
# [int] labels
# Reshape matrix of rasterized images of shape (1, 50*50)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
layer0_input = x.reshape((batch_size, 1, layer0_input_img_size[0], layer0_input_img_size[1]))
# Construct the first convolutional pooling layer:
# filtering reduces the image size to (28-5+1,28-5+1)=(24,24)
# maxpooling reduces this further to (24/2,24/2) = (12,12)
# 4D output tensor is thus of shape (batch_size,nkerns[0],12,12)
layer0 = LeNetConvPoolLayer(rng, input=layer0_input, \
image_shape=(batch_size, 1, layer0_input_img_size[0], layer0_input_img_size[1]), \
filter_shape=(nkerns[0], 1, filter0_shape[0], filter0_shape[1]), poolsize=(2, 2) \
)
# Construct the second convolutional pooling layer
# filtering reduces the image size to (12-5+1,12-5+1)=(8,8)
# maxpooling reduces this further to (8/2,8/2) = (4,4)
# 4D output tensor is thus of shape (nkerns[0],nkerns[1],4,4)
layer1 = LeNetConvPoolLayer(rng, input=layer0.output,
image_shape=(batch_size, nkerns[0], layer1_input_img_size[0], layer1_input_img_size[1]),
filter_shape=(nkerns[1], nkerns[0], filter1_shape[0], filter1_shape[1]), poolsize=(2, 2) \
)
# the TanhLayer being fully-connected, it operates on 2D matrices of
# shape (batch_size,num_pixels) (i.e matrix of rasterized images).
# This will generate a matrix of shape (20,32*4*4) = (20,512)
layer2_input = layer1.output.flatten(2)
def load_trained_model():
global if_load_trained_model
global train_model_route
global layer0_input
global layer0
global layer1
global layer2_input
global layer2
global layer3
global test_results
global layer0_input_img_size # ishape
global filter0_shape
global layer1_input_img_size
global filter1_shape
global layer2_input_img_size
global layer2_out
if_load_trained_model = True
print "loading trained model for the first time"
trained_model_pkl = open(train_model_route, 'r')
trained_model_state_list = cPickle.load(trained_model_pkl)
trained_model_state_array = numpy.load(trained_model_pkl)
layer0_state, layer1_state, layer2_state, layer3_state = trained_model_state_array
######################
# BUILD ACTUAL MODEL #
######################
print '... loading the model'
# Reshape matrix of rasterized images of shape (1, 50*50)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
layer0_input = x.reshape((batch_size, 1, layer0_input_img_size[0], layer0_input_img_size[1]))
# Construct the first convolutional pooling layer:
# filtering reduces the image size to (28-5+1,28-5+1)=(24,24)
# maxpooling reduces this further to (24/2,24/2) = (12,12)
# 4D output tensor is thus of shape (batch_size,nkerns[0],12,12)
layer0 = LeNetConvPoolLayer(rng, input=layer0_input, \
image_shape=(batch_size, 1, layer0_input_img_size[0], layer0_input_img_size[1]), \
filter_shape=(nkerns[0], 1, filter0_shape[0], filter0_shape[1]), poolsize=(2, 2), \
W=layer0_state[0], b=layer0_state[1] \
)
# Construct the second convolutional pooling layer
# filtering reduces the image size to (12-5+1,12-5+1)=(8,8)
# maxpooling reduces this further to (8/2,8/2) = (4,4)
# 4D output tensor is thus of shape (nkerns[0],nkerns[1],4,4)
layer1 = LeNetConvPoolLayer(rng, input=layer0.output,
image_shape=(batch_size, nkerns[0], layer1_input_img_size[0], layer1_input_img_size[1]),
filter_shape=(nkerns[1], nkerns[0], filter1_shape[0], filter1_shape[1]), poolsize=(2, 2), \
W=layer1_state[0], b=layer1_state[1] \
)
# the TanhLayer being fully-connected, it operates on 2D matrices of
# shape (batch_size,num_pixels) (i.e matrix of rasterized images).
# This will generate a matrix of shape (20,32*4*4) = (20,512)
layer2_input = layer1.output.flatten(2)
# construct a fully-connected sigmoidal layer
layer2 = HiddenLayer(rng, input=layer2_input, n_in=nkerns[1] * layer2_input_img_size[0] * layer2_input_img_size[1],
n_out=layer2_out, activation=T.tanh, \
W=layer2_state[0], b=layer2_state[1] \
)
# classify the values of the fully-connected sigmoidal layer
layer3 = LogisticRegression(input=layer2.output, n_in=layer2_out, n_out=N_OUT, \
W=layer3_state[0], b=layer3_state[1] \
)
test_results = theano.function(inputs=[x], \
outputs= layer3.y_pred)
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size, 64 * 64)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
# (28, 28) is the size of MNIST images.
layer0_input = x.reshape((batch_size, 1, 64,64))
# Construct the first convolutional pooling layer:
# filtering reduces the image size to (64-5+1 , 64-5+1) = (60, 60)
# maxpooling reduces this further to (60/2, 60/2) = (30, 30)
# 4D output tensor is thus of shape (batch_size, nkerns[0], 12, 12)
layer0 = LeNetConvPoolLayer(
rng,
input=layer0_input,
image_shape=(batch_size, 1, 64, 64),
filter_shape=(nkerns[0], 1, 5, 5),
poolsize=(2, 2)
)
# Construct the second convolutional pooling layer
# filtering reduces the image size to (30-5+1, 30-5+1) = (26, 26)
# maxpooling reduces this further to (26/2, 26/2) = (13,13)
# 4D output tensor is thus of shape (nkerns[0], nkerns[1], 13, 13)
layer1 = LeNetConvPoolLayer(
rng,
input=layer0.output,
image_shape=(batch_size, nkerns[0], 30, 30),
filter_shape=(nkerns[1], nkerns[0], 5, 5),
poolsize=(2, 2)
)
