我想从头开始重新训练Keras模型Inception_v3。

模型在这里定义：https://github.com/keras-team/keras-applications/blob/master/keras_applications/inception_v3.py

我读了一些帖子，

列出的解决方案有：

1. 冻结层（这不是我想要的…）

   for layer in model.layers:    layer.trainable = False

2. https://stackoverflow.com/a/51727616/7748163

通过检查初始化器重置所有层：

def reset_weights(model):    session = K.get_session()    for layer in model.layers:         if hasattr(layer, 'kernel_initializer'):            layer.kernel_initializer.run(session=session)        if hasattr(layer, 'bias_initializer'):            layer.bias_initializer.run(session=session)     

3. 使用tf.variables_initializer

     model = InceptionV3()  for layer in model.layers:      sess.run(tf.variables_initializer(layer.weights))

   参考：https://stackoverflow.com/a/56634827/7748163

4. 我认为这是最好的方法，但它引发了错误。

   sess = tf.Session()for layer in model.layers:      for v in layer.__dict__:         v_arg = getattr(layer,v)         if hasattr(v_arg,'initializer'):             initializer_method = getattr(v_arg, 'initializer')             initializer_method.run(session=sess)             print('reinitializing layer {}.{}'.format(layer.name, v))

然而，这些方法对于Inception_v3都不起作用。

错误信息涉及BatchNorm层：

tensorflow.python.framework.errors_impl.FailedPreconditionError: Error while reading resource variable batch_normalization_9/moving_mean from Container: localhost. This could mean that the variable was uninitialized. Not found: Resource localhost/batch_normalization_9/moving_mean/N10tensorflow3VarE does not exist.     [[{{node batch_normalization_9_1/AssignMovingAvg/ReadVariableOp}}]]     [[metrics_1/categorical_accuracy/Identity/_469]]

那么，如何重新训练现有的Keras模型，并初始化变量？从Keras应用中重新训练模型的最佳实践是什么？

进一步讨论：

https://github.com/keras-team/keras/issues/341

回答：

为什么不简单地不请求权重呢？

model = Inception_V3(..., weights=None,...)

https://github.com/keras-team/keras-applications/blob/master/keras_applications/inception_v3.py/#L100