我试图了解有关喀拉拉邦的upsampling2d。我在下面写了一个简单的代码
model = Sequential()
(trainX, trainY), (testX, testY) = mnist.load_data()
input_shape = (trainX.shape[1], trainX.shape[2], 1)
layer1 = Conv2D(64, 3,input_shape=input_shape, padding='same', activation='relu')
layer2 = UpSampling2D ()
layer3 = Conv2D(128, 3, padding='same', activation='relu')
model.add(layer1)
model.add(layer2)
model.add(layer3)
model.compile(loss='binary_crossentropy', optimizer = 'adam')
print (model.summary())
我的输出是
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 28, 28, 64) 640
_________________________________________________________________
up_sampling2d_1 (UpSampling2 (None, 56, 56, 64) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 56, 56, 128) 73856
=================================================================
Total params: 74,496
Trainable params: 74,496
Non-trainable params: 0
_________________________________________________________________
问题：
我了解如何计算640和73856个参数，但是如果我看到上采样层，我会看到学习参数为零。在这种情况下，将26 * 26放大到56 * 56。相对于26 * 26，数据如何在56 * 56内部？