def get_output_layer(model, layer_name, n):
# get the symbolic outputs of each "key" layer (we gave them unique names).
layer_dict = dict([(layer.name, layer) for layer in model.layers])
layer = layer_dict[layer_name].get_output_at(n)
return layer
#%%
x_train = []
in_E = np.zeros((1, 6, im_size[0], im_size[1]))
labels = np.zeros((1, 6, im_size[0], im_size[1]))
print '\n---lead dataset Image \n'
data_dir = './image/move_circle/'
im_dir, dir_num = myl.ListDir(data_dir)
print dir_num, im_dir
im_sum = 0
for name in im_dir:
print data_dir + name
im = Image.open(data_dir + name)
im = im.resize((im_size[1], im_size[0]))
im = np.asarray(im.convert('RGB'))
#im = ImageOps.grayscale(im)
im = np.asarray(im)
x_train.append(im)
if len(x_train) > use_im_num:
break
print '\n---Image Nomarization & Reduction'
