使用tape.gradient计算梯度的时候报错

Question

使用tape.gradient计算梯度的时候报错

import keras
import numpy as np
import tensorflow as tf
from keras.applications import vgg16
from keras import backend as K

model=vgg16.VGG16(weights='imagenet',include_top=False)

with tf.GradientTape() as tape:
    layer_dict=dict([(layer.name,layer) for layer in model.layers])
    layer_output=layer_dict[layer_name].output
    loss=K.mean(layer_output[:,:,:,filter_index])
grads=tape.gradient(loss,model.input)[0]

运行的时候提示以下错误信息，该怎么处理

AttributeError                            Traceback (most recent call last)
<ipython-input-17-c356f312b286> in <module>
     13     layer_output=layer_dict[layer_name].output
     14     loss=K.mean(layer_output[:,:,:,filter_index])
---> 15 grads=tape.gradient(loss,model.input)[0]
     16 
     17 iterate=K.function([model.input],[loss,grads])

~\anaconda3\envs\py3.7\lib\site-packages\tensorflow\python\eager\backprop.py in gradient(self, target, sources, output_gradients, unconnected_gradients)
   1088         output_gradients=output_gradients,
   1089         sources_raw=flat_sources_raw,
-> 1090         unconnected_gradients=unconnected_gradients)
   1091 
   1092     if not self._persistent:

~\anaconda3\envs\py3.7\lib\site-packages\tensorflow\python\eager\imperative_grad.py in imperative_grad(tape, target, sources, output_gradients, sources_raw, unconnected_gradients)
     75       output_gradients,
     76       sources_raw,
---> 77       compat.as_str(unconnected_gradients.value))

AttributeError: 'KerasTensor' object has no attribute '_id'

Answer 1

这个错误是由于你使用了旧版本的Keras与新版本的TensorFlow结合。在新版本的TensorFlow中，KerasTensor对象已经替换了旧的Tensor对象。为了解决这个问题，你需要确保你在整个代码中使用TensorFlow的Keras模块，而不是单独的Keras库。

首先，请确保已安装了TensorFlow 2.x。然后，根据下面的建议修改代码：

import numpy as np
import tensorflow as tf
from tensorflow.keras.applications import VGG16
from tensorflow.keras import backend as K

model = VGG16(weights='imagenet', include_top=False)

# Define layer_name and filter_index here
layer_name = 'block1_conv1'
filter_index = 0

layer_dict = dict([(layer.name, layer) for layer in model.layers])
layer_output = layer_dict[layer_name].output

with tf.GradientTape() as tape:
    tape.watch(model.input)  # Add this line to watch the model input
    loss = K.mean(layer_output[:, :, :, filter_index])

grads = tape.gradient(loss, model.input)[0]

这个修改的代码使用了tensorflow.keras而不是keras，因此你不会遇到与KerasTensor相关的问题。请注意，你需要定义layer_name和filter_index变量。在这个示例中，我将layer_name设置为block1_conv1，filter_index设置为0，你可以根据自己的需求进行更改。