keras model.get_weight没有以预期的尺寸返回结果 [英] keras model.get_weight is not returning results in expected dimensions

问题描述

我正在使用keras对mnist数据集进行分类.我有兴趣对训练后生成的权重矩阵进行一些操作，但某些层的权重矩阵看起来好像没有完全连接.

I am doing classification over mnist dataset using keras. I am interested in doing some operation on weight matrix generated after the training but some layers weight matrix looks like they are not fully connected.

model = Sequential()
model.add(Dense(1000, input_shape = (train_x.shape[1],), activation='relu' ))
model.add(Dense(1000, activation='relu'))
model.add(Dense(500, activation='relu'))
model.add(Dense(200, activation='relu'))
model.add(Dense(10, activation='softmax'))

model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics = ['accuracy'])
model.fit(train_x,train_y, epochs=10, validation_data= (test_x,test_y))

w = model.get_weights()

for i in range(5):
        print(w[i].shape)

现在，当我打印每一层的权重矩阵的尺寸时，我得到以下结果

now, when I print the dimensions of the weight matrix of each layer I get the following result

(784, 1000)
(1000,)
(1000, 1000)
(1000,)
(1000, 500)

第二个为什么有(1000，)而没有(1000,1000)?

why 2nd has (1000,) and not (1000,1000)?

推荐答案

因为它有偏见.不要忘记该图层是由(有时也写为).

Because it is bias. Don't forget that layer is defined by (sometimes also written as ).

假设x的形状为(None, 784)，权重w的形状为(784, 1000). matmul(x, w)操作的结果为形状(None, 1000).向此形状的结果张量中，添加形状(1000, )的偏差，该偏差沿None维度传播.

Suppose the shape of x is (None, 784) and the shape of weights w is (784, 1000). The matmul(x, w) operation results in the shape (None, 1000). To the resulted tensor of this shape you're adding bias of shape (1000, ) which is broadcasted along the None dimension.

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