Visualizing Intermediate Layer Activations about visualizing_convnets HOT 3 OPEN

n_features = layer_activation.shape[-1]
size = layer_activation.shape[1]
n_cols = n_features // images_per_row
display_grid = np.zeros((size * n_cols, images_per_row * size))

inp = Input((224, 224, 3))

inception_model = InceptionV3(include_top=False, input_tensor=inp, pooling='avg')
x = Dense(2)(inception_model.output)

model = Model(inp, x)

layer_outputs = [layer.output for layer in model.layers[:50]]

test_image = "image_path"
img = image.load_img(test_image, target_size=(224, 224))
img_tensor = image.img_to_array(img)
img_tensor = np.expand_dims(img_tensor, axis=0)
img_tensor /= 255.

activation_model = Model(inputs=model.input, outputs=layer_outputs)
activations = activation_model.predict(img_tensor)

layer_names = ['conv2d_1', 'activation_1', 'conv2d_4', 'activation_4', 'conv2d_9', 'activation_9']
activ_list = [activations[1], activations[3], activations[11], activations[13], activations[18], activations[20]]

images_per_row=16

for layer_name, layer_activation in zip(layer_names, activ_list):
    n_features = layer_activation.shape[-1]
    size = layer_activation.shape[1]
    n_cols = n_features // images_per_row
    display_grid = np.zeros((size * n_cols, images_per_row * size))
    
    for col in range(n_cols):
        for row in range(images_per_row):
            channel_image = layer_activation[0, :, :, col * images_per_row + row]
            channel_image -= channel_image.mean()
            channel_image /= channel_image.std()
            channel_image *= 64
            channel_image += 128
            channel_image = np.clip(channel_image, 0, 255).astype('uint8')
            display_grid[col * size : (col + 1) * size, row * size : (row + 1) * size] = channel_image

    scale = 1. / size
    plt.figure(figsize=(scale * display_grid.shape[1], scale * display_grid.shape[0]))
    plt.title(layer_name)
    plt.grid(False)
    plt.imshow(display_grid, aspect='auto', cmap='plasma')