为什么我无法获得经过训练的模型的内部输出? [英] Why I can't get the internal output of a trained model?

问题描述

import tensorflow.keras as keras
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

if __name__ == '__main__':
    model = keras.models.load_model('model/model_test_0.99408.h5', custom_objects={'leaky_relu': tf.nn.leaky_relu})
    model.summary()
    inputs = keras.layers.Input(shape=(28, 28, 1))
    y = model(inputs)
    feature = model.get_layer('conv2d_4').output
    model = keras.Model(inputs=inputs, outputs=[y, feature])
    model.summary()

为什么我无法获得作为模型内部层的conv2d_4"的输出?我收到以下错误.

why i can't get the output of 'conv2d_4' that is the internal layer of the model? And i get the following error.

Graph disconnected: cannot obtain value for tensor Tensor("input_1:0", shape=(None, 28, 28, 1), dtype=float32) at layer "conv2d". The following previous layers were accessed without issue: []

推荐答案

我们可以尝试重新堆叠模型，将feature分配给需要的层，

We can try restacking the model, assigning feature to the required layer,

import tensorflow.keras as keras
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

if __name__ == '__main__':
    model = keras.models.load_model('model/model_test_0.99408.h5', custom_objects={'leaky_relu': tf.nn.leaky_relu})
    model.summary()
    inputs = keras.layers.Input(shape=(28, 28, 1))
    y = inputs
    for layer in vgg.layers:
        if layer.name == 'conv2d_4':
            feature = y
        y = layer( y )
    model = keras.Model(inputs=inputs, outputs=[y, feature])
    model.summary()

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