root = '../../varghese/10_1_brain_mean/'

# patch_size = 11

recon_flag = False

batch_size = 100

if recon_flag == True:

n_ins = patch_size * patch_size * 5

else:

n_ins = patch_size * patch_size * 4

n_outs = 5

# hidden_layers_sizes = [1000,1000,1000]

# corruption_levels = [0.01,0.01,0.01]

# noise_type = 1 #1- Gaussian, 0 - masking

test_path = root + 'testing'

# prefix = 'yyy'

print 'Extracting training patches...'

Patch_Preprocess_recon_2D(patch_size,patch_size, prefix,root+'training',root+'BRATS_training_patches/',False)

print 'Training patches extracted!'

print 'Extracting validation patches...'

Patch_Preprocess_recon_2D(patch_size,patch_size,prefix,root+'validation',

root+'BRATS_validation_patches/',False)

print 'Validation patches extracted!'

path = '../results/'

for subdir, dirs, files in os.walk(path):

test_num = len(dirs)+1

break

# print test_num

##########---------SET PREFIX--------##########

os.mkdir('../results/test2D'+str(test_num)+'_'+prefix)

#

test_root = '../results/test2D'+str(test_num)+'_'+prefix+'/'

print 'Calling test_SdA...'

finetune_lr = 0.1

pretraining_epochs = 1

pretrain_lr = 0.001

training_epochs = 1

test_SdA(finetune_lr, pretraining_epochs,

pretrain_lr, training_epochs,

root+'BRATS_training_patches/trainpatch_2D_'+prefix+'_.npy',

root+'BRATS_training_patches/trainlabel_2D_'+prefix+'_.npy',

root+'BRATS_validation_patches/validpatch_2D_'+prefix+'_.npy',

root+'BRATS_validation_patches/validlabel_2D_'+prefix+'_.npy',batch_size, n_ins, n_outs, hidden_layers_sizes, test_root + prefix, corruption_levels)

print 'Network Trained and Saved!'

test_network(test_root , prefix, test_path, patch_size, patch_size, patch_size, recon_flag, 2)