def get_img():
process = Process()
img_input,img_labels = process.read_in_images(["train-input"],["train-labels"])
img_input = process.normalize(img_input)
return img_input
def test_SdA(sample_size = 60,
finetune_lr = 0.01,
pretraining_epochs = 20,
pretrain_lr = 0.01,
training_epochs = 100,
batch_size = 30,
corruption_levels = [0.2],
hidden_layers_sizes = [2000],
img_size = (1020,1020),
img_size_test = (600,1020)):
process = Process()
img_input,img_labels = process.read_in_images(["train-input"],["train-labels"])
img_input = process.normalize(img_input)
#img_input = process.apply_clahe(img_input)
#img_input = process.local_normalization(img_input)
img_input = img_input[:,:img_size[0],:img_size[1]]
train_set = img_input
valid_set = img_input[:1]
test_set = img_input[:1,:img_size_test[0],:img_size_test[1]]
train_set_x,train_set_y = process.manipulate(train_set),train_set
valid_set_x,valid_set_y = process.manipulate(valid_set),valid_set
test_set_x,test_set_y = process.manipulate(test_set),test_set
train_set_x, table =process.generate_set(train_set_x, sample_size = sample_size, stride = sample_size, img_size = img_size)
valid_set_x, table =process.generate_set(valid_set_x, sample_size = sample_size, stride = sample_size, img_size = img_size)
test_set_x, table =process.generate_set(test_set_x, sample_size = sample_size, stride = sample_size, img_size = img_size_test)
train_set_y, table =process.generate_set(train_set_y, sample_size = sample_size, stride = sample_size, img_size = img_size)
valid_set_y, table =process.generate_set(valid_set_y, sample_size = sample_size, stride = sample_size, img_size = img_size)
test_set_y, table =process.generate_set(test_set_y, sample_size = sample_size, stride = sample_size, img_size = img_size_test)
train_set_x,train_set_y = train_set_x.astype(np.float32),train_set_y.astype(np.float32)
valid_set_x,valid_set_y = valid_set_x.astype(np.float32),valid_set_y.astype(np.float32)
test_set_x,test_set_y = test_set_x.astype(np.float32), test_set_y.astype(np.float32)
train_set_x, train_set_y = theano.shared(train_set_x,borrow=True),theano.shared(train_set_y,borrow=True)
valid_set_x, valid_set_y = theano.shared(valid_set_x,borrow=True),theano.shared(valid_set_y,borrow=True)
test_set_x, test_set_y = theano.shared(test_set_x,borrow=True),theano.shared(test_set_y,borrow=True)
n_train_batches = train_set_x.get_value(borrow=True).shape[0]
n_train_batches /= batch_size
np_rng = np.random.RandomState()
print '... building the model'
sda = SdA(
numpy_rng = np_rng,
n_ins = sample_size**2,
n_outs = sample_size**2,
hidden_layers_sizes = hidden_layers_sizes
)
print '... Initializing pretraining functions'
pretraining_fns, output_fn = sda.pretraining_functions(train_set_x=train_set_x,
batch_size=batch_size)
print '... Layer-wise training of model'
start_time = time.clock()
for i in xrange(sda.n_layers):
for epoch in xrange(pretraining_epochs):
c = []
for batch_index in xrange(n_train_batches):
c.append(pretraining_fns[i](index = batch_index,
corruption = corruption_levels[i],
lr = pretrain_lr))
print 'Layer %i, epoch %d, cost ' % (i, epoch),
print np.mean(c)
end_time = time.clock()
print >> sys.stderr, ('Layer-wise training ran for %.2fm' % ((end_time - start_time) / 60.))
########################
# FINETUNING THE MODEL #
########################
datasets = [(train_set_x,train_set_y),(valid_set_x,valid_set_y),(test_set_x,test_set_y)]
# get the training, validation and testing function for the model
print '... getting the finetuning functions'
train_fn, validate_model, test_model,output_fn = sda.build_finetune_functions(
datasets=datasets,
batch_size=batch_size,
learning_rate=finetune_lr
)
print '... finetuning of model'
for n in xrange(training_epochs):
costs = []
for i in xrange(n_train_batches):
costs.append(train_fn(i))
cost = np.mean(costs)
#val_cost = validate_model()
print "Epoch:",n,"Cost:",cost #,"Validation cost:",val_cost
print "Test cost:", test_model()
out = np.zeros((0,sample_size**2))
for batch_index in xrange(train_set_x.get_value().shape[0]):
out = np.vstack((out,output_fn(batch_index)))
img_output = process.post_process(out, table, sample_size,img_shape=img_size_test)
plt.figure()
plt.imshow(test_set[0],cmap=plt.cm.gray)
plt.figure()
plt.imshow(img_output[0],cmap=plt.cm.gray)
xz = process.xz_stack(img_input)
for m in xrange(xz.shape[0]):
for n in xrange(xz.shape[1]):
xz[m,n] = (xz[m,n]-xz[m,n].mean())/xz[m,n].std()
xz_train, table =process.generate_set(xz, sample_size = sample_size, stride = sample_size, img_size = img_size_test)
xz_train = xz_train.astype(np.float32)
test_set_x.set_value(xz_train)
out = np.zeros((0,sample_size**2))
for batch_index in xrange(train_set_x.get_value().shape[0]):
out = np.vstack((out,output_fn(batch_index)))
img_output = process.post_process(out, table, sample_size,img_shape=img_size_test)
plt.figure()
plt.imshow(xz[0],cmap=plt.cm.gray)
plt.figure()
plt.imshow(img_output[0],cmap=plt.cm.gray)
plt.show()
