attention

Author: whyboris

attention.py

@@ -23,5 +23,45 @@
 predictions = model.predict(x)
 print('predicted: ', decode_predictions(predictions, top=3)[0])
 
-print(np.argmax(predictions[0]))
+print(np.argmax(predictions[0])) # output of this needs to be on the next line 
+
+cat_output = model.output[:, 285] # this number comes from the output of previous line
+
+last_conv_layer = model.get_layer('block3_conv3')
+
+from keras import backend as K
+
+grads = K.gradients(cat_output, last_conv_layer.output)[0]
+
+pooled_grads = K.mean(grads, axis=(0, 1, 2))
+
+iterate = K.function([model.input], [pooled_grads, last_conv_layer.output[0]])
+
+pooled_grads_value, conv_layer_output_value = iterate([x])
+
+for i in range (255):
+    conv_layer_output_value[:, :, i] *= pooled_grads_value[i]
+
+heatmap = np.mean(conv_layer_output_value, axis=-1)
+
+import matplotlib.pyplot as plt
+
+heatmap = np.maximum(heatmap, 0)
+heatmap /= np.max(heatmap)
+plt.matshow(heatmap)
+plt.show()
+
+import cv2
+
+img = cv2.imread(img_path)
+
+heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))
+
+heatmap = np.uint8(255 * heatmap)
+
+heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)
+
+superimposed_img = heatmap * 0.4 + img
+
+cv2.imwrite('../heatmap.jpg', superimposed_img)
 

requirements.txt

@@ -1,4 +1,5 @@
 autopep8
+cv2
 jupyter
 keras
 matplotlib