Visualiser le traitement qui se produit dans chaque couche de réseaux de neurones convolutifs

# First import Keras layers needed for the model
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.preprocessing import image # for image loading and manipulation
import numpy as np # for additional processing needed by Keras
import matplotlib.pyplot as plt # for plotting the images
 
dog = image.load_img('dog.jpg')
plt.imshow(dog)
plt.show()
 
dog = image.load_img('dog.jpg',target_size=(64,64))
plt.imshow(dog)
plt.show()
 
def show_features(classifier,dog):
    img = np.expand_dims(dog,axis=0)
    conv = classifier.predict(img)
    conv = np.squeeze(conv, axis=0)
    conv = conv.reshape(conv.shape[:2])
    plt.imshow(conv)
    plt.show()
 
dog = image.img_to_array(dog)
classifier = Sequential()
classifier.add(Conv2D(1,(3,3), input_shape=dog.shape, activation='relu'))
show_features(classifier,dog)
 
classifier = Sequential()
classifier.add(Conv2D(1,(3,3), input_shape=dog.shape, activation='relu'))
classifier.add(MaxPooling2D(pool_size=(2,2)))
show_features(classifier,dog)