filter visualization

Author: whyboris

filter-visualization.py

@@ -0,0 +1,89 @@
+print('vizualize filters')
+
+from hack import hack
+hack()
+
+from keras.applications import VGG16
+from keras import backend as K
+
+model = VGG16(weights='imagenet', include_top=False)
+
+# model.summary()
+
+layer_name = 'block3_conv1'
+
+filter_index = 0
+
+layer_output = model.get_layer(layer_name).output 
+loss = K.mean(layer_output[:, :, :, filter_index])
+
+grads = K.gradients(loss, model.input)[0]
+
+grads /= (K.sqrt(K.mean(K.square(grads)))) + 1e-5
+
+iterate = K.function([model.input], [loss, grads])
+
+import numpy as np 
+
+loss_value, grads_value = iterate([np.zeros((1, 150, 150, 3))])
+
+input_img_data = np.random.random((1, 150, 150, 0)) * 20 + 128. 
+
+def deprocess_image(x):
+    x -= x.mean()
+    x /= (x.std() + 1e-5)
+    x *= 0.1
+    x += 0.5
+    x = np.clip(x, 0, 1)
+    x *= 255
+    x = np.clip(x, 0, 255).astype('uint8')
+    return x
+
+def generate_pattern(layer_name, filter_index, size=150):
+    layer_output = model.get_layer(layer_name).output
+    loss = K.mean(layer_output[:, :, :, filter_index])
+
+    grads = K.gradients(loss, model.input)[0]
+
+    grads /= (K.sqrt(K.mean(K.square(grads))) + 1e-5)
+
+    iterate = K.function([model.input], [loss, grads])
+
+    input_img_data = np.random.random((1, size, size, 3)) * 25 + 128.
+
+    step = 1.
+    for i in range(40):
+        loss_value, grads_value = iterate([input_img_data])
+
+        input_img_data += grads_value * step
+
+    img = input_img_data[0]
+    return deprocess_image(img)
+
+import matplotlib.pyplot as plt 
+
+# plt.imshow(generate_pattern('block3_conv1', 0))
+
+# plt.show()
+
+# layer_name = 'block1_conv1'
+layer_name = 'block4_conv1'
+size = 64
+margin = 5
+
+results = np.zeros((8 * size + 7 * margin, 8 * size + 7 * margin, 3))
+
+for i in range(8):
+    for j in range(8):
+        filter_img = generate_pattern(layer_name, i + (j * 8), size=size)
+
+        horizontal_start = i * size + i * margin
+        horizontal_end = horizontal_start + size
+        vertical_start = j * size + j * margin
+        vertical_end = vertical_start + size
+        results[horizontal_start: horizontal_end, vertical_start: vertical_end, :] = filter_img
+
+plt.figure(figsize=(20, 20))
+plt.imshow(results)
+
+plt.show()