如何使用 Keras 手动更新权重

Posted 2023-02-16

【中文标题】如何使用 Keras 手动更新权重

【英文标题】：How to update weights manually with Keras

【发布时间】：2018-12-23 13:12:36

【问题描述】：

我正在使用 Keras 构建 LSTM，并通过使用外部成本函数进行梯度下降来调整它。所以权重更新为：

weights := weights + alpha* gradient(cost)

我知道我可以使用keras.getweights() 获得权重，但是如何进行梯度下降并更新所有权重并相应地更新权重。我尝试使用initializer，但我仍然没有弄明白。我只找到了一些与 tensorflow 相关的代码，但我不知道如何将其转换为 Keras。

任何帮助、提示或建议将不胜感激！

【参考方案1】：

keras.layer.set_weights() 就是你要找的东西：

import numpy as np
from keras.layers import Dense
from keras.models import Sequential

model = Sequential()
model.add(Dense(10, activation='relu', input_shape=(10,)))
model.add(Dense(5, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='adam', loss='categorical_crossentropy')

a = np.array(model.get_weights())         # save weights in a np.array of np.arrays
model.set_weights(a + 1)                  # add 1 to all weights in the neural network
b = np.array(model.get_weights())         # save weights a second time in a np.array of np.arrays
print(b - a)                              # print changes in weights

查看 keras 文档here 的相应页面。

【讨论】：

更正：get_weights() 返回np.arrays 的列表，而不是np.array。

还要注意 .assign 或 .assign_add 函数，它们为变量赋值（例如，你通过 model.trainable_weights 获得的那些）

【参考方案2】：

您需要一些 TensorFlow 来计算符号梯度。这是一个使用 Keras 的玩具示例，然后深入挖掘以在 TensorFlow 中手动执行逐步下降。

from keras.models import Sequential
from keras.layers import Dense, Activation
from keras import backend as k
from keras import losses
import numpy as np
import tensorflow as tf
from sklearn.metrics import mean_squared_error
from math import sqrt

model = Sequential()
model.add(Dense(12, input_dim=8, kernel_initializer='uniform', activation='relu'))
model.add(Dense(8, kernel_initializer='uniform', activation='relu'))
model.add(Dense(8, kernel_initializer='uniform', activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

inputs = np.random.random((1, 8))
outputs = model.predict(inputs)
targets = np.random.random((1, 8))
rmse = sqrt(mean_squared_error(targets, outputs))

print("===BEFORE WALKING DOWN GRADIENT===")
print("outputs:\n", outputs)
print("targets:\n", targets)
print("RMSE:", rmse)


def descend(steps=40, learning_rate=100.0, learning_decay=0.95):
    for s in range(steps):

        # If your target changes, you need to update the loss
        loss = losses.mean_squared_error(targets, model.output)

        #  ===== Symbolic Gradient =====
        # Tensorflow Tensor Object
        gradients = k.gradients(loss, model.trainable_weights)

        # ===== Numerical gradient =====
        # Numpy ndarray Objcet
        evaluated_gradients = sess.run(gradients, feed_dict=model.input: inputs)

        # For every trainable layer in the network
        for i in range(len(model.trainable_weights)):

            layer = model.trainable_weights[i]  # Select the layer

            # And modify it explicitly in TensorFlow
            sess.run(tf.assign_sub(layer, learning_rate * evaluated_gradients[i]))

        # decrease the learning rate
        learning_rate *= learning_decay

        outputs = model.predict(inputs)
        rmse = sqrt(mean_squared_error(targets, outputs))

        print("RMSE:", rmse)


if __name__ == "__main__":
    # Begin TensorFlow
    sess = tf.InteractiveSession()
    sess.run(tf.initialize_all_variables())

    descend(steps=5)

    final_outputs = model.predict(inputs)
    final_rmse = sqrt(mean_squared_error(targets, final_outputs))

    print("===AFTER STEPPING DOWN GRADIENT===")
    print("outputs:\n", final_outputs)
    print("targets:\n", targets)

结果：

===BEFORE WALKING DOWN GRADIENT===
outputs:
 [[0.49995303 0.5000101  0.50001436 0.50001544 0.49998832 0.49991882
  0.49994195 0.4999649 ]]
targets:
 [[0.60111501 0.70807258 0.02058449 0.96990985 0.83244264 0.21233911
  0.18182497 0.18340451]]
RMSE: 0.33518919408969455
RMSE: 0.05748867468895
RMSE: 0.03369414290610595
RMSE: 0.021872132066183464
RMSE: 0.015070048653579693
RMSE: 0.01164369828903875
===AFTER STEPPING DOWN GRADIENT===
outputs:
 [[0.601743   0.707857   0.04268148 0.9536494  0.8448022  0.20864952
  0.17241994 0.17464897]]
targets:
 [[0.60111501 0.70807258 0.02058449 0.96990985 0.83244264 0.21233911
  0.18182497 0.18340451]]