f_train = theano.function([input_var, target_var], [cost], updates=updates)
from confusionmatrix import ConfusionMatrix
batch_size = 250
num_epochs = 175
train_acc = []
valid_loss = []
valid_acc = []
train_loss = []
cur_loss = 0
loss = []
Train = DP.get_paths("/home/xvt131/Functions/Adhish_copy/Training-Rand")
Test = DP.get_paths("/home/xvt131/Functions/Adhish_copy/Validating-Rand")
for epoch in range(num_epochs):
cur_loss = 0
val_loss = 0
confusion_valid = ConfusionMatrix(2)
confusion_train = ConfusionMatrix(2)
# Train = np.random.choice(Train_all, 5)
for im in Train:
X_train, Y_train = DP.Patch_3D_para(im, PS)
num_samples_train = Y_train.shape[0]
num_batches_train = num_samples_train // batch_size
for i in range(num_batches_train):
f_vali = theano.function([input_var, input2_var, input3_var, target_var],
[costV])
import Evaluation as E
import DP1 as TD
import scipy.io as io
all_dice = np.array([])
with np.load(
'/home/xvt131/Biomediq/Functions/Adhish_copy/Exp101/cheat.npz') as f:
param_values = [f['arr_%d' % i] for i in range(len(f.files))]
lasagne.layers.set_all_param_values(output, param_values)
Dice = []
for img in DP.get_paths("/home/xvt131/Biomediq/Data/kneeData/Validating-Rand"):
A, B, C = TD.get_indeces(img)
B1 = B.reshape(np.prod(B.shape))
batch = 5000
num_batches = A.shape[0] / batch
Sha = B.shape
preds = np.zeros(shape=(len(B1), NC))
for i in range(num_batches):
idx = range(i * batch, (i + 1) * batch)
K = A[idx]
M, N, O = TD.Patch_gen(K, 29, C)
preds[idx] = f_eval(M, N, O)
if num_batches * batch < A.shape[0]:
tot = num_batches * batch
f_eval = theano.function([input_var, input2_var, input3_var, input4_var], eval_out)
f_train = theano.function([input_var, input2_var, input3_var, input4_var, target_var], [cost], updates=updates)
f_vali = theano.function([input_var, input2_var, input3_var,input4_var ,target_var], [costV])
import try_DP as TD
import scipy.io as io
import Evaluation as E
with np.load("/home/xvt131/Network_adapt/triplanar_Params_WI.npz") as f:
param_values = [f['arr_%d' % i] for i in range(len(f.files))]
lasagne.layers.set_all_param_values(output, param_values)
for img in DP.get_paths("/home/xvt131/Functions/Adhish_copy/Validating-Rand"):
A, B, C = TD.Tri_Image_Load(img)
B1 = B.reshape(np.prod(B.shape))
batch = 100
num_batches = A.shape[0] / batch
Sha = B.shape
print Sha
TibiaD = []
FemoralD = []
preds = np.zeros(shape = ( len(B1), 2 ))
for i in range(num_batches):
idx = range(i*batch, (i+1)*batch)
K = A[idx]
M, N, O, P= TD.Patch_gen(K, 29, C)
preds[idx] = f_eval(M,N,O, P)
Final_pred = np.argmax(preds, axis = -1)
f_eval = theano.function([input_var], eval_out)
f_vali = theano.function([input_var, target_var], [costV])
f_train = theano.function([input_var, target_var], [cost], updates=updates)
import Evaluation as E
import try_DP as TD
import scipy.io as io
with np.load("/home/xvt131/Functions/Adhish_copy/3D_params/3D_all_params11.npz"
) as f:
param_values = [f['arr_%d' % i] for i in range(len(f.files))]
lasagne.layers.set_all_param_values(output, param_values)
Dice = []
for img in DP.get_paths("/home/xvt131/Functions/Adhish_copy/vseries"):
print img
A, B, C = TD.Tri_Image_Load(img)
B1 = B.reshape(np.prod(B.shape))
batch = 5000
num_batches = A.shape[0] / batch
Sha = B.shape
preds = np.zeros(shape=(len(B1), 2))
for i in range(num_batches):
idx = range(i * batch, (i + 1) * batch)
K = A[idx]
M = TD.Patch_gen_three(K, PS, C)
M = M.reshape(batch, 1, PS, PS, PS)
preds[idx] = f_eval(M)
f_train = theano.function([input_var, input2_var, input3_var, target_var], [cost], updates=updates)
from confusionmatrix import ConfusionMatrix
batch_size = 15
num_epochs = 50
train_acc= []
train_loss = np.array([])
valid_acc, valid_loss = [], []
test_acc, test_loss = [], []
cur_loss = 0
loss = []
Train = DP.get_paths("/home/xvt131/Functions/Adhish_copy/Training")
import gc
for epoch in range(num_epochs):
cur_loss = 0
#Train = np.random.choice(Train_all, 5)
confusion_valid = ConfusionMatrix(3)
confusion_train = ConfusionMatrix(3)
for im in Train:
XY, XZ, YZ, Y_train = DP.Patch_triplanar_para(im, PS)
num_samples_train = Y_train.shape[0]
num_batches_train = num_samples_train // batch_size
for i in range(num_batches_train):
idx = range(i*batch_size, (i+1)*batch_size)
f_vali = theano.function([input_var, input2_var, input3_var, target_var],
[costV])
from confusionmatrix import ConfusionMatrix
batch_size = 250
num_epochs = 175
train_acc = []
valid_acc = []
cur_loss = 0
loss = []
valid_loss = []
Train = DP.get_paths("/home/xvt131/Functions/Adhish_copy/croppedT")
Test = DP.get_paths("/home/xvt131/Functions/Adhish_copy/croppedV")
import gc
for epoch in range(num_epochs):
cur_loss = 0
val_loss = 0
confusion_valid = ConfusionMatrix(2)
confusion_train = ConfusionMatrix(2)
for im in Train:
XY, XZ, YZ, Y_train = DP.Patch_triplanar_para(im, PS)
num_samples_train = Y_train.shape[0]
num_batches_train = num_samples_train // batch_size
for i in range(num_batches_train):
idx = range(i * batch_size, (i + 1) * batch_size)
