keras 中的训练网络仅包含 conv2d 层

Question

我在 mnist 上的 keras 中训练了自己的模型。我只有 conv2d 层，因为我想在小图像 (mnist: 28x28 px) 上训练网络，然后在大图像 1920x1080 上进行推理。

我的身材（训练用）：

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv1 (Conv2D)               (None, 28, 28, 64)        640       
_________________________________________________________________
batch_normalization_117 (Bat (None, 28, 28, 64)        256       
_________________________________________________________________
leaky_re_lu_117 (LeakyReLU)  (None, 28, 28, 64)        0         
_________________________________________________________________
max_pooling2d_119 (MaxPoolin (None, 14, 14, 64)        0         
_________________________________________________________________
conv2 (Conv2D)               (None, 14, 14, 128)       73856     
_________________________________________________________________
batch_normalization_118 (Bat (None, 14, 14, 128)       512       
_________________________________________________________________
leaky_re_lu_118 (LeakyReLU)  (None, 14, 14, 128)       0         
_________________________________________________________________
max_pooling2d_120 (MaxPoolin (None, 7, 7, 128)         0         
_________________________________________________________________
conv3 (Conv2D)               (None, 7, 7, 256)         295168    
_________________________________________________________________
batch_normalization_119 (Bat (None, 7, 7, 256)         1024      
_________________________________________________________________
leaky_re_lu_119 (LeakyReLU)  (None, 7, 7, 256)         0         
_________________________________________________________________
max_pooling2d_121 (MaxPoolin (None, 4, 4, 256)         0         
_________________________________________________________________
conv4 (Conv2D)               (None, 4, 4, 128)         295040    
_________________________________________________________________
batch_normalization_120 (Bat (None, 4, 4, 128)         512       
_________________________________________________________________
leaky_re_lu_120 (LeakyReLU)  (None, 4, 4, 128)         0         
_________________________________________________________________
max_pooling2d_122 (MaxPoolin (None, 2, 2, 128)         0         
_________________________________________________________________
conv5 (Conv2D)               (None, 1, 1, 10)          5130      
=================================================================
Total params: 672,138
Trainable params: 670,986
Non-trainable params: 1,152

推理形状：

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv1 (Conv2D)               (None, 1920, 1080, 64)    640       
_________________________________________________________________
batch_normalization_113 (Bat (None, 1920, 1080, 64)    256       
_________________________________________________________________
leaky_re_lu_113 (LeakyReLU)  (None, 1920, 1080, 64)    0         
_________________________________________________________________
max_pooling2d_115 (MaxPoolin (None, 960, 540, 64)      0         
_________________________________________________________________
conv2 (Conv2D)               (None, 960, 540, 128)     73856     
_________________________________________________________________
batch_normalization_114 (Bat (None, 960, 540, 128)     512       
_________________________________________________________________
leaky_re_lu_114 (LeakyReLU)  (None, 960, 540, 128)     0         
_________________________________________________________________
max_pooling2d_116 (MaxPoolin (None, 480, 270, 128)     0         
_________________________________________________________________
conv3 (Conv2D)               (None, 480, 270, 256)     295168    
_________________________________________________________________
batch_normalization_115 (Bat (None, 480, 270, 256)     1024      
_________________________________________________________________
leaky_re_lu_115 (LeakyReLU)  (None, 480, 270, 256)     0         
_________________________________________________________________
max_pooling2d_117 (MaxPoolin (None, 240, 135, 256)     0         
_________________________________________________________________
conv4 (Conv2D)               (None, 240, 135, 128)     295040    
_________________________________________________________________
batch_normalization_116 (Bat (None, 240, 135, 128)     512       
_________________________________________________________________
leaky_re_lu_116 (LeakyReLU)  (None, 240, 135, 128)     0         
_________________________________________________________________
max_pooling2d_118 (MaxPoolin (None, 120, 68, 128)      0         
_________________________________________________________________
conv5 (Conv2D)               (None, 119, 67, 10)       5130      
=================================================================
Total params: 672,138
Trainable params: 670,986
Non-trainable params: 1,152

这里的目标是用我的输出类的尺寸创建一个卷积图像，它代表我的大图像中的滑动 windows 以供推理。

但是keras不会让我训练，因为在最后一层它会减少我的previos层输出的形状（从（batch，x，y，channels）到（batch，channels））：

ValueError: Error when checking target: expected conv5 to have 4 dimensions, but got array with shape (48000, 10)

形状必须是 (48000, 1, 1, 10) !!!我能做些什么来防止这种情况？当我引入 flatten 和 dense 时，我以后不能用它来推断大图像吗？

感谢您的宝贵时间和帮助。

Answer 1

为了能够训练和测试不同的输入大小，您应该做两件事：

引入None作为输入维度。
使用 GlobalAveragePooling2D 和 Conv2D 层，过滤器大小等于类别数。

下面的示例代码可以创建一个模型来训练和推理任何输入大小的图像（假定最大池化和步幅不会导致负维度）。

from keras import layers, Model

my_input = layers.Input(shape=(None, None, 1))

x = layers.Conv2D(filters=32, kernel_size=3, strides=1)(my_input)
x = layers.BatchNormalization()(x)
x = layers.MaxPooling2D()(x)
x = layers.Conv2D(filters=64, kernel_size=3, strides=1)(x)
x = layers.BatchNormalization()(x)
x = layers.MaxPooling2D()(x)

out = layers.Conv2D(filters=10, kernel_size=1, strides=1)(x)
out = layers.GlobalAveragePooling2D()(out)
out = layers.Activation('softmax')(out)
model = Model(my_input, out)
model.summary()

模型摘要打印如下：

Model: "model_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         (None, None, None, 1)     0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, None, None, 32)    320       
_________________________________________________________________
batch_normalization_1 (Batch (None, None, None, 32)    128       
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, None, None, 32)    0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, None, None, 64)    18496     
_________________________________________________________________
batch_normalization_2 (Batch (None, None, None, 64)    256       
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, None, None, 64)    0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, None, None, 10)    650       
_________________________________________________________________
global_average_pooling2d_1 ( (None, 10)                0         
_________________________________________________________________
activation_1 (Activation)    (None, 10)                0         
=================================================================
Total params: 19,850
Trainable params: 19,658
Non-trainable params: 192

keras 中的训练网络仅包含 conv2d 层

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