VGG卷积操作可视化

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.applications.vgg16 import VGG16
from matplotlib import pyplot as plt
model = VGG16(weights='imagenet')
model.summary()
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.vgg16 import preprocess_input, decode_predictions
import numpy as np
img_path = r'C:\Users\1\Desktop\34.png'
#缩放
img = image.load_img(img_path,target_size=(224,224))
# #显示图片
# plt.imshow(img)
# plt.show()
x = image.img_to_array(img)
#维度扩展
x = np.expand_dims(x,axis=0)
#图片预处理
x = preprocess_input(x)
preds = model.predict(x)
print('predicted', decode_predictions(preds, top=3)[0])
layer_names = ['block1_conv1','block3_conv1','block5_conv1']
#获取指定层的输出
layer_outputs = [model.get_layer(layer_name).output for layer_name in layer_names]
#获得模型指定层的输出
activation_model = keras.models.Model(inputs=model.input,outputs=layer_outputs)
#获得输出
activations = activation_model.predict(x)
first_layer_activation = activations[0]

plt.matshow(first_layer_activation[0,:,:,1],cmap='viridis')
plt.show()

import numpy as np

images_per_row = 8

for layer_name, layer_activation in zip(layer_names, activations):
    # 获取卷积核的个数
    n_features = layer_activation.shape[-1]
    # 特征图的形状(1,size,size,n_features)
    size = layer_activation.shape[1]

    # n_cols = n_features // images_per_row
    n_cols = 8
    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]
            # 归一化
            channel_image -= channel_image.mean()
            channel_image /= channel_image.std()
            channel_image *= 128
            channel_image += 128
            channel_image = np.clip(channel_image, 0, 255).astype(np.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.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(display_grid, aspect='auto', cmap='viridis')
plt.show()