Keras:使用模型的一阶和二阶导数之和作为最终输出 [英] Keras: using the sum of the first and second derivatives of a model as final output

问题描述

假设我使用Keras创建了此模型:

Suppose that I created this model using Keras:

model = Sequential()
model.add(Dense(32, activation='tanh', input_dim=1))
model.add(Dense(10, activation='tanh'))
model.add(Dense(1, activation='linear'))

此模型的输入和输出维度均为1.现在，假设我想再增加一层，即上面模型的一阶导数和二阶导数的总和(相对于输入)，并且将其用作我的新输出层.在Keras可能吗?我做了很多谷歌搜索，但找不到任何东西.

The input and output dimensions of this model are both 1. Now, suppose I want to add one more layer, which is the sum of the first derivative and second derivative (with respect to the input) of the model above, and use it as my new output layer. Is that possible in Keras? I did quite a lot of Googling but couldn't find anything.

推荐答案

您可以使用 tf.gradients ，并在Keras中使用K.gradients:

first = K.gradients(model.outputs, model.inputs)
second = K.gradients(first, model.inputs)

以下是用于计算模型中的梯度的代码:

Here is the code that calculates the gradient in your model:

from tensorflow.python.keras import Model, Input
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.layers import Dense, Lambda

def deriative(inps):
    i, o = inps[0], inps[1]
    grad1 = K.gradients(o, i)[0]
    grad2 = K.gradients(grad1, i)[0]
    return K.concatenate([grad1, grad2])


inps = Input(shape=(1,))
dense1 = Dense(32, activation='tanh')(inps)
dense2 = Dense(10, activation='tanh')(dense1)
dense3 = Dense(1, activation='linear')(dense2)

output = Lambda(deriative)([inps, dense3])

new_model = Model(inputs=inps, outputs=output)
new_model.compile('adam', 'mse')
print(new_model.summary())

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