小编Tyr*_*nde的帖子

model.save('path_to_my_model.h5')
del model
model = tf.keras.models.load_model('path_to_my_model.h5', custom_objects={'Wraparound2D': Wraparound2D})

import tensorflow.keras.backend as K

inp = model.input                                           # input placeholder
outputs = [layer.output for layer in model.layers]          # all layer outputs
functor = K.function(inp, outputs)   # evaluation function

layer_outs = functor([X_test, 1.])



# Plot activations of different neurons in different layers 
all_layer_activations = list()

min_max_scaler = lambda x : (x - np.min(x))/(np.max(x) - np.min(x))
# min_max_scaler = lambda x : (x - np.mean(x))
for j in range(1, 5):
    if j==1:
        layer_im …