将Keras模型wrt的导数作为输入将返回全零 [英] Taking derivative of Keras model wrt to inputs is returning all zeros

问题描述

所以我有一个Keras模型.我想将模型wrt的梯度引入其输入.这就是我要做的

So I have a Keras model. I want to take the gradient of the model wrt to its inputs. Here's what I do

import tensorflow as tf
from keras.models import Sequential
from keras.layers import Dense
from keras import backend as K

num_features = 5

model = Sequential()
model.add(Dense(60, input_shape=(num_features,), activation='relu'))
model.add(Dense(50, activation='relu'))
model.add(Dense(1, activation='softmax'))
model.compile(optimizer='adam', loss='binary_crossentropy')
#Run predict to initialize weights
model.predict(np.random.rand(1, num_features))

x = tf.random_uniform(shape=(1, num_features))
model_grad = tf.gradients(model(x), x)[0]

但是，当我打印出dmodel_dx的值时，我得到的全为0.

However when I print out the value of dmodel_dx I get all 0's.

sess = K.get_session()
print( model_grad.eval(session=sess) )
>>>array([[ 0.,  0.,  0.,  0.,  0.]], dtype=float32)

有人知道我在做什么错吗?

Anyone know what I'm doing wrong?

推荐答案

检查softmax是否饱和，从而为您提供非常小的渐变-试试

Check if the softmax is saturated and therefore giving you very small gradients--try

model_grad = K.gradients(K.dot(model.layers[-1].input,model.layers[-1].kernel)+model.layers[-1].bias, model.input)

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