怎么用keras实现用胶囊网络对（224,224）的图像进行3分类

首先，您需要安装Keras和其他必要的库。然后，您可以按照以下步骤实现使用胶囊网络对（224,224）的图像进行3分类：

1. 导入所需库和模型

from keras import layers, models, optimizers
from keras.layers import Dropout, Flatten, Dense, Input, Conv2D, MaxPooling2D
from keras.layers import BatchNormalization
from keras.utils import plot_model
from keras.utils import to_categorical
from keras import backend as K
from keras.utils import np_utils
from keras.layers import Convolution2D, Reshape
from keras import callbacks
from capsulelayers import CapsuleLayer, PrimaryCap, Length, Mask

2. 定义输入图像的形状

input_shape = (224, 224, 3)

3. 定义胶囊网络模型

def CapsNet(input_shape, n_class, routings):
    x = Input(shape=input_shape)
    
    conv1 = Conv2D(filters=64, kernel_size=3, strides=1, padding='valid', activation='relu', name='conv1')(x)
    conv1 = BatchNormalization()(conv1)
    conv2 = Conv2D(filters=64, kernel_size=3, strides=1, padding='valid', activation='relu', name='conv2')(conv1)
    conv2 = BatchNormalization()(conv2)
    primarycaps = PrimaryCap(conv2, dim_capsule=8, n_channels=32, kernel_size=3, strides=2, padding='valid')
    digitcaps = CapsuleLayer(num_capsule=n_class, dim_capsule=16, routings=routings, name='digitcaps')(primarycaps)
    out_caps = Length(name='capsnet')(digitcaps)

    # Decoder network
    y = Input(shape=(n_class,))
    masked_by_y = Mask()([digitcaps, y])
    x_recon = layers.Dense(512, activation='relu')(masked_by_y)
    x_recon = layers.Dense(1024, activation='relu')(x_recon)
    x_recon = layers.Dense(np.prod(input_shape), activation='sigmoid')(x_recon)
    x_recon = layers.Reshape(target_shape=input_shape, name='out_recon')(x_recon)

    return models.Model([x, y], [out_caps, x_recon])

4. 创建模型实例

model = CapsNet(input_shape=(224, 224, 3),
                n_class=3,
                routings=3)
model.summary()

5. 编译模型并训练

model.compile(optimizer=optimizers.Adam(lr=1e-3),
              loss=[margin_loss, 'mse'],
              loss_weights=[1., 0.2],
              metrics={'capsnet': 'accuracy'})

history = model.fit([x_train, y_train], [y_train, x_train], batch_size=args.batch_size, epochs=args.epochs,
                    validation_data=[[x_test, y_test], [y_test, x_test]],
                    callbacks=[lr_decay, log])

其中`x_train`和`y_train`表示训练数据及其对应标签，`x_test`和`y_test`表示测试数据及其对应标签。

6. 评估模型

y_pred, x_recon = model.predict([x_test, y_test], batch_size=args.batch_size)

7. 可选：保存模型

model.save('model.h5')