重置Keras模型的所有权重

如何解决重置Keras模型的所有权重

我希望能够重设整个Keras模型的权重，这样就不必再次编译它。当前，编译模型是我代码的主要瓶颈。这是我的意思的示例：

import tensorflow as tf  

model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=(28,28)),tf.keras.layers.Dense(16,activation='relu'),tf.keras.layers.Dense(10)
])

model.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=0.001),loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),metrics=['accuracy'])
   
data = tf.keras.datasets.mnist
(x_train,y_train),(x_test,y_test) = data.load_data()

model.fit(x=x_train,y=y_train,epochs=10)

# Reset all weights of model here
# model.reset_all_weights() <----- something like that

model.fit(x=x_train,epochs=10)

解决方法

您可以使用此循环：

for ix,layer in enumerate(model.layers):
    if hasattr(model.layers[ix],'kernel_initializer') and \
            hasattr(model.layers[ix],'bias_initializer'):
        weight_initializer = model.layers[ix].kernel_initializer
        bias_initializer = model.layers[ix].bias_initializer

        old_weights,old_biases = model.layers[ix].get_weights()

        model.layers[ix].set_weights([
            weight_initializer(shape=old_weights.shape),bias_initializer(shape=len(old_biases))])

原始重量：

model.layers[1].get_weights()[0][0]

array([ 0.4450057,-0.13564804,0.35884023,0.41411972,0.24866664,0.07641453,0.45726687,-0.04410008,0.33194816,-0.1965386,-0.38438258,-0.13263905,-0.23807487,0.40130925,-0.07339832,0.20535922],dtype=float32)

新的权重：

model.layers[1].get_weights()[0][0]

array([-0.4607593,-0.13104361,-0.0372932,-0.34242013,0.12066692,-0.39146423,0.3247317,0.2635846,-0.10496247,-0.40134245,0.19276887,0.2652442,-0.18802321,-0.18488845,0.0826562,-0.23322225],dtype=float32)