[英]CNN Keras: ValueError: Negative dimension size caused by subtracting 3 from 2 for 'conv2d
I got this error when using Keras: Is it because input_size not larger than the filter? 使用Keras时出现此错误:是因为input_size不大于过滤器?
If input_shape=(64,64,3))), there will be no error. 如果input_shape =(64,64,3))),则不会有错误。
``ValueError: Negative dimension size caused by subtracting 3 from 2 for
'conv2d_24/convolution' (op: 'Conv2D') with input shapes: [?,2,2,128],
[3,3,128,128].
My code are here: 我的代码在这里:
from keras import layers
from keras import models
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu',
input_shape=(32, 32, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
Default layer padding is valid
, which means no padding. 默认的图层填充
valid
,表示没有填充。 This way dimension reduce from 32 not to 16 but to 15. You can use padding='same'
instead. 这种方式将尺寸从32减少到16,而不是减少到15。您可以改用
padding='same'
。 In this case output has the same length as the original input. 在这种情况下,输出的长度与原始输入的长度相同。
from keras import layers
from keras import models
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu',padding='same',
input_shape=(32, 32, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu',padding='same'))
model.add(layers.MaxPooling2D((2, 2),padding='same'))
model.add(layers.Conv2D(128, (3, 3), activation='relu',padding='same'))
model.add(layers.MaxPooling2D((2, 2),padding='same'))
model.add(layers.Conv2D(128, (3, 3), activation='relu',padding='same'))
model.add(layers.MaxPooling2D((2, 2),padding='same'))
model.add(layers.Flatten())
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
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