minor updates

app.py

@@ -43,31 +43,37 @@ def load_model(model: str, activation: bool=True):
     return ort_session
 
 def get_activations(intermediate_model, image: list,
-                               layer=None, vmax=2.5, sub_mean=True):
+                    layer=None, vmax=2.5, sub_mean=True):
     
     '''Gets activations for a given input image'''
     
-    
+    # run model
     input_name = intermediate_model.get_inputs()[0].name
     outputs = intermediate_model.run(None, {input_name: image})
     
+    # get activations
     output_1 = outputs[1]
     output_2 = outputs[2]
     
+    # get prediction
     output = outputs[0][0]
     output = special.softmax(output)
     
+    # sum over velocity channels
     in_image = np.sum(image[0, :, :, :], axis=0)
     in_image = normalize_array(in_image)
 
     if layer is None:
+        # sum over all velocity channels
         activation_1 = np.sum(output_1[0, :, :, :], axis=0)
         activation_2 = np.sum(output_2[0, :, :, :], axis=0)
     else:
+        # select a single channel
         activation_1 = output_1[0, layer, :, :]
         activation_2 = output_2[0, layer, :, :]
     
     if sub_mean:
+        # y = |x - <x>|
         activation_1 -= np.mean(activation_1)
         activation_1 = np.abs(activation_1)
         
@@ -123,7 +129,6 @@ def predict_and_analyze(model_name, num_channels, dim, image):
     
     origin = 'lower'
     
-    
     ##### Make the activation figure ######
     plt.rcParams['xtick.labelsize'] = ticks
     plt.rcParams['ytick.labelsize'] = ticks
@@ -157,7 +162,6 @@ def predict_and_analyze(model_name, num_channels, dim, image):
     
     input_fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(9, 8))
     
-    
     im0 = ax.imshow(input_image, cmap=cmap,
                      origin=origin)