Question

我设计了一个CNN网络，以便在keras中使用“ cifar10”数据集。这是我的代码：

input_layer = Input(shape=(32,32,3))
x = Conv3D(32,(5,5,3),activation='relu',padding='same')(input_layer)
x = Conv3D(32,(5,5,3),activation='relu',padding='same')(x)
x = MaxPool3D(pool_size=2, padding='same')(x)
x = Conv3D(32,(5,5,3),activation='relu',padding='same')(x)
x = Conv3D(32,(5,5,3),activation='relu',padding='same')(x)
x = MaxPool3D(pool_size=2, padding='same')(x)
x = Flatten()(x)
x = Dense(128,kernel_initializer='random_normal', bias_initializer='zeros')(x)
x = Dense(128,kernel_initializer='random_normal', bias_initializer='zeros')(x)
output_layer = Dense(10,activation='softmax',kernel_initializer='random_normal', bias_initializer='zeros')(x)
Cifar10_CNN = Model(input_layer, output_layer)

建立模型时出现此错误：

Input 0 is incompatible with layer conv3d_5: expected ndim=5, found ndim=4

我该如何解决？

Answer 1

您可能应该阅读有关differences between Conv2D, Conv3D的信息。尽管可能会造成混淆（给定的图像实际上是3维的），但它们仍被认为是2D的（考虑Keras中的卷积时，您无需考虑通道维。卷积总会发生在通道维上）。因此，您不需要Conv3D来拍摄图像，而需要Conv2D。

from tensorflow.keras.layers import Input, Dense, Conv2D, MaxPool2D, Flatten
from tensorflow.keras.models import Model

input_layer = Input(shape=(32,32,3))
x = Conv2D(32,(5,5),activation='relu',padding='same')(input_layer)
x = Conv2D(32,(5,5),activation='relu',padding='same')(x)
x = MaxPool2D(pool_size=2, padding='same')(x)
x = Conv2D(32,(5,5),activation='relu',padding='same')(x)
x = Conv2D(32,(5,5),activation='relu',padding='same')(x)
x = MaxPool2D(pool_size=2, padding='same')(x)
x = Flatten()(x)
x = Dense(128,kernel_initializer='random_normal', bias_initializer='zeros')(x)
x = Dense(128,kernel_initializer='random_normal', bias_initializer='zeros')(x)
output_layer = Dense(10,activation='softmax',kernel_initializer='random_normal', bias_initializer='zeros')(x)
Cifar10_CNN = Model(input_layer, output_layer)
print(Cifar10_CNN.summary())

使用3D CNN进行3D图像分类

1 个答案: