具有高级计算功能的Keras自定义图层 [英] Keras Custom Layer with advanced calculations

问题描述

我想编写一些自定义的Keras图层并在该图层中进行一些高级计算，例如使用Numpy，Scikit，OpenCV ...

I want to write some custom Keras Layers and do some advanced calculations in the layer, for example with Numpy, Scikit, OpenCV...

我知道keras.backend中有一些数学函数可以对张量进行运算，但是我需要一些更高级的函数.

I know there are some math functions in keras.backend that can operate on tensors, but i need some more advanced functions.

但是，我不知道如何正确执行此操作，我收到错误消息:
You must feed a value for placeholder tensor 'input_1' with dtype float and shape [...]

However, i have no clue how to implement this correctly, i get the error message:
You must feed a value for placeholder tensor 'input_1' with dtype float and shape [...]

这是我的自定义图层:

class MyCustomLayer(Layer):
    def __init__(self, **kwargs):
        super(MyCustomLayer, self).__init__(**kwargs)

    def call(self, inputs):
        """
        How to implement this correctly in Keras?
        """
        nparray = K.eval(inputs)  # <-- does not work
        # do some calculations here with nparray
        # for example with Numpy, Scipy, Scikit, OpenCV...
        result = K.variable(nparray, dtype='float32')
        return result

    def compute_output_shape(self, input_shape):
        output_shape = tuple([input_shape[0], 256, input_shape[3]])
        return output_shape  # (batch, 256, channels)

此虚拟模型中的错误出现在这里:

The error appears here in this dummy model:

inputs = Input(shape=(96, 96, 3))
x = MyCustomLayer()(inputs)
x = Flatten()(x)
x = Activation("relu")(x)
x = Dense(1)(x)    
predictions = Activation("sigmoid")(x)
model = Model(inputs=inputs, outputs=predictions)

感谢所有提示...

推荐答案

我认为这种过程应在模型之前应用，因为该过程不包含变量，因此无法进行优化.

i think that this kinda process should apply before the model because the process does not contain variables so it cant be optimized.

K.eval(inputs)不起作用，因为您正在尝试评估一个占位符，而不是变量占位符没有用于评估的值.如果要获取值，则应该输入它，或者可以使用tf.unstack()一张一张地从张量中列出一个列表

K.eval(inputs) does not work beacuse you are trying to evaluate a placeholder not variable placeholders has not values for evaluate. if you want get values you should feed it or you can make a list from tensors one by one with tf.unstack()

nparray = tf.unstack(tf.unstack(tf.unstack(inputs,96,0),96,0),3,0)

您的调用函数是错误的，因为返回变量，您应该返回常量:

your call function is wrong because returns a variable you should return a constant:

result = K.constant(nparray, dtype='float32')
return result

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