如果使用Sequential型号，则应将输入设置为model.layers[0].input。

• • 首先**，扩展测试输入的维度，以包括Batch_Size：

layer_input = test_images[0]    
plt.imshow(layer_input)   # Plot Test Image
layer_input = tf.expand_dims(layer_input,0)   # Add prefix of Batch Size 
print(layer_input.shape)  # Prints  : (1, 32, 32, 3)

• • 输出**：

• • 修改的绘图代码：**

for i in range(len(model.layers)):
    get_layer_output = K.function(inputs = model.layers[0].input, outputs = model.layers[i].output)

    get_1_output = get_layer_output(layer_input)
    # print(get_1_output.shape) << Use this to check if the Output shape matches the shape of Model.summary()

    if get_1_output.ndim == 4:             # Check for Dimensionality to plot ONE feature map (Batch size, Length, Width
      plt.imshow(get_1_output[0][:,:,:3])  # Plots the output of Conv2D and MaxPooling
    else:
      print(get_1_output)   # If not Image, ie. Array, print the Values

    plt.show()

• • 输出**：

希望我回答了你的问题。

你可以使用下面的python脚本来绘制神经网络的特征图。

import tensorflow as tf
import  cv2
import numpy as np

# use 'model' as your model name
# change 224 image size to your model input image size 

layer_names = [layer.name for layer in model.layers]

layer_outputs = [layer.output for layer in model.layers[1:]]

feature_map_model = tf.keras.models.Model(inputs=model.input, outputs=layer_outputs)

img = cv2.imread("path/to/image/image.jpg")
img = cv2.resize(img, (224,224))
img = np.array([img])/255.0

feature_maps = feature_map_model.predict(img, verbose=0)

for layer_name, feature_map in zip(layer_names, feature_maps):

    if len(feature_map.shape) == 4:
    k = feature_map.shape[-1]
    size=feature_map.shape[1]

    image_belt = np.zeros((size, size * k))

    for i in range(k):
        feature_image = feature_map[0, :, :, i]
        feature_image-= feature_image.mean()
        feature_image/= feature_image.std ()
        feature_image*=  64
        feature_image+= 128
        feature_image= np.clip(feature_image, 0, 255).astype('uint8')
        image_belt[:, i * size : (i + 1) * size] = feature_image

    scale = 20. / k
    plt.figure( figsize=(scale * k, scale) )
    plt.title ( layer_name )
    plt.grid  ( False )
    plt.imshow( image_belt, aspect='auto')

参考：https://www.analyticsvidhya.com/blog/2020/11/tutorial-how-to-visualize-feature-maps-directly-from-cnn-layers/