def predict_on_fullimage(test_set_x,params):
model= model_provider.get_model_pretrained(params)
# learning parameters
batch_size =params["batch_size"]
n_test_batches = len(test_set_x)
ash=n_test_batches%batch_size
if(ash>0):
test_set_x=np.vstack((test_set_x,np.tile(test_set_x[-1],(batch_size-ash,1))))
n_test_batches = len(test_set_x)
n_test_batches /= batch_size
y_pred=[]
print("Number of parameters: %s"%(model.count_params()))
print "Prediction on test images"
patch_loc=u.get_patch_loc(params) #we are not using this for image
map_index=0
for i in xrange(n_test_batches):
Fx = test_set_x[i * batch_size: (i + 1) * batch_size]
argu= [(params,"F", Fx,patch_loc,map_index),(params,"S", Fx,patch_loc,map_index)]
results = du.asyn_load_batch_images(argu)
data_Fx = results[0]
data_Sx = results[1]
if(params["model_type"]==4):
data=data_Sx-data_Fx
res =model.predict(data)
else:
res=model.predict([data_Fx, data_Sx])
if(len(y_pred)==0):
y_pred= res
else:
y_pred=np.concatenate((y_pred,res))
if(ash>0):
y_pred= y_pred[0:-(batch_size-ash)]
return y_pred
def predict_on_multi_input(test_set_x,params):
model= model_provider.get_model_pretrained(params)
# learning parameters
batch_size =params["batch_size"]
n_test_batches = len(test_set_x)
ash=n_test_batches%batch_size
if(ash>0):
test_set_x=np.vstack((test_set_x,np.tile(test_set_x[-1],(batch_size-ash,1))))
n_test_batches = len(test_set_x)
n_test_batches /= batch_size
y_pred=[]
pred_mat={}
print "Prediction on test images"
n_patch=params["n_patch"]
n_repeat=params["n_repeat"]
map_list=range(batch_size*n_repeat)
n=n_patch*n_repeat
for i in xrange(n_test_batches):
map_index=map_list[i]%n_repeat
Fx = test_set_x[i * batch_size: (i + 1) * batch_size]
pred=np.zeros((batch_size,params['n_output']))
pred_mat[0]=np.zeros((n,params['n_output']))
for k in range(batch_size): pred_mat[k+i]=np.zeros((n,params['n_output']))
for patch_index in xrange(n):
patch_loc=u.get_patch_loc(params)
argu= [(params,"F", Fx,patch_loc,map_index),(params,"S", Fx,patch_loc,map_index)]
results = du.asyn_load_batch_images(argu)
data_Fx = results[0]
data_Sx = results[1]
prd=model.predict([data_Fx, data_Sx])
for j in range(len(prd)):
pred_mat[i+j][patch_index]=prd[j]
pred=np.add(pred, prd)
pred/=n
if(len(y_pred)==0):
y_pred= pred
else:
y_pred=np.concatenate((y_pred,pred))
pd.plot_patch(pred_mat)
if(ash>0):
y_pred= y_pred[0:-(batch_size-ash)]
return y_pred
paddedw = imshape[1] + border
for i in xrange(nimgs):
row = int(np.floor(i / ncols))
col = i % ncols
mosaic[row * paddedh : row * paddedh + imshape[0], col * paddedw : col * paddedw + imshape[1]] = imgs[i]
return mosaic
# pl.imshow(make_mosaic(np.random.random((9, 10, 10)), 3, 3, border=1))
params = config.get_params()
params["model_name"] = "more_noreg_data2_46_gray.hdf5"
params["model"] = "kcnnr"
model = mp.get_model_pretrained(params)
for i in range(2):
print("Lef or right: %s th" % (i))
for k in range(3):
print("Layer param:of %s th" % (i))
# Visualize weights
W = np.squeeze(model.layers[0].layers[i].layers[k * 3].W.get_value(borrow=True))
print("W shape : ", W.shape)
pl.figure(figsize=(15, 15))
pl.title("conv1 weights (i,k)(%s,%s)" % (i, k))
if len(W.shape) > 3:
X = W.reshape(W.sahpe[0], 20, 30)
mosaic = make_mosaic(X, 6, 6)
nice_imshow(pl.gca(), mosaic, cmap=cm.binary)
