如何使用一个numpy数组在Keras中设置权重? [英] How to set weights in Keras with a numpy array?

问题描述

我在设置值的Keras后端功能上遇到麻烦.我试图将模型从PyTorch转换为Keras，并试图设置Keras模型的权重，但是权重似乎没有被设置.注意:实际上，我并没有使用np.ones进行设置.

I am having trouble with the Keras backend functions for setting values. I am trying to convert a model from PyTorch to Keras and am trying to set the weights of the Keras model, but the weights do not appear to be getting set. Note: I am not actually setting with np.ones just using that for an example.

我尝试过...

加载现有模型

import keras
from keras.models import load_model, Model
model = load_model(model_dir+file_name)
keras_layer = [layer for layer in model.layers if layer.name=='conv2d_1'][0]

创建一个简单的模型

img_input = keras.layers.Input(shape=(3,3,3))
x = keras.layers.Conv2D(1, kernel_size=1, strides=1, padding="valid", 
use_bias=False, name='conv1')(img_input)
model = Model(img_input, x)
keras_layer = [layer for layer in model.layers if layer.name=='conv1'][0]

然后使用set_weights或set_value

Then using set_weights or set_value

keras_layer.set_weights([np.ones((1, 1, 3, 1))])

或...

K.batch_set_value([(weight,np.ones((1, 1, 3, 1))) for weight in keras_layer.weights])

此后，我致电以下其中之一:

afterwards I call either one of the following:

K.batch_get_value([weight for weight in keras_layer.weights])
keras_layer.get_weights()

并且似乎没有设置任何权重.返回与以前相同的值.

And None of the weights appear to have been set. The same values as before are returned.

[array([[[[  1.61547325e-06],
      [  2.97779252e-06],
      [  1.50160542e-06]]]], dtype=float32)]

我如何在keras中使用数值数组来设置图层的权重?

How do I set the weights of a layer in Keras with a numpy array of values?

推荐答案

代码中的keras_layer是什么?

您可以通过以下方式设置权重:

You can set weights these ways:

model.layers[i].set_weights(listOfNumpyArrays)    
model.get_layer(layerName).set_weights(...)
model.set_weights(listOfNumpyArrays)

其中model是现有模型的实例. 您可以从上面的相同实例中使用方法get_weights()来查看列表的预期长度及其数组形状.

Where model is an instance of an existing model. You can see the expected length of the list and its array shapes using the method get_weights() from the same instances above.

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