问VGG16特征可视化

EN

from keras.models import Model
layer_outputs = [layer.output for layer in my_model_11.layers]
activation_model = Model(inputs=my_model_11.input, outputs=layer_outputs)
activations = activation_model.predict(X_train_224[359].reshape(1,224,224,1))

def display_activation(activations, col_size, row_size, act_index): 
    activation = activations[act_index]
    activation_index=0
    fig, ax = plt.subplots(row_size, col_size, figsize=(row_size*2,col_size*2))
    for row in range(0,row_size):
       for col in range(0,col_size):
           ax[row][col].imshow(activation[0, :, :, activation_index], cmap='gray')
           activation_index += 1
    plt.savefig('Displa12y.png',bbox_inches= 'tight')
display_activation(activations, 2, 2, 1)

ixs = [17,18] 
outputs = [model.layers[i].output for i in ixs]
model = Model(inputs=model.inputs, outputs=outputs)


feature_maps = model.predict(X_train_224[359].reshape(1,224,224,3))
# plot the output from each block
square = 8
for fmap in feature_maps:
    # plot all 64 maps in an 8x8 squares
    ix = 1
    for _ in range(square):
        plt.figure(figsize=(64,64))
        for _ in range(square):
           

            # specify subplot and turn of axis
            ax = pyplot.subplot(square, square, ix)
            ax.set_xticks([])
            ax.set_yticks([])
            
            # plot filter channel in grayscale
            plt.imshow(fmap[0, :, :, ix-1], cmap='gray')
            ix += 1
    # show the figure

        
    plt.show()