写一维胶囊网络的代码keras并训练

好的，以下是一维胶囊网络的Keras代码及其训练过程：

# 导入必要的库
import keras
from keras import layers, models

# 定义一维胶囊网络
class Capsule(layers.Layer):
    def __init__(self, num_capsules, dim_capsule, routings=3, **kwargs):
        super(Capsule, self).__init__(**kwargs)
        self.num_capsules = num_capsules
        self.dim_capsule = dim_capsule
        self.routings = routings

    def build(self, input_shape):
        self.input_num_capsules = input_shape[1]
        self.input_dim_capsule = input_shape[2]

        # 初始化W_ij和b_i
        self.W = self.add_weight(shape=[self.input_num_capsules, self.num_capsules, self.input_dim_capsule, self.dim_capsule], initializer='glorot_uniform', name='W')
        self.b = self.add_weight(shape=[1, self.input_num_capsules, self.num_capsules, 1, 1], initializer='zeros', name='b')

        super(Capsule, self).build(input_shape)

    def call(self, inputs, training=None):
        inputs_expand = keras.backend.expand_dims(inputs, 2)

        inputs_tiled = keras.backend.tile(inputs_expand, [1, 1, self.num_capsules, 1, 1])

        inputs_hat = keras.backend.map_fn(lambda x: keras.backend.batch_dot(x, self.W, [3, 2]), elems=inputs_tiled)

        for i in range(self.routings):
            c = keras.backend.softmax(self.b, axis=2)

            outputs = keras.backend.batch_dot(c, inputs_hat, [2, 3])

            if i != self.routings - 1:
                outputs = keras.backend.map_fn(lambda x: keras.backend.l2_normalize(x, axis=-1), elems=outputs)

                b += keras.backend.batch_dot(outputs, inputs_hat, [3, 4])

        return keras.backend.reshape(outputs, [-1, self.num_capsules, self.dim_capsule])

    def compute_output_shape(self, input_shape):
        return tuple([None, self.num_capsules, self.dim_capsule])


# 定义模型
def CapsNet(input_shape, n_class, num_capsules, dim_capsule, routings):
    x = layers.Input(shape=input_shape)

    conv1 = layers.Conv1D(filters=256, kernel_size=9, strides=1, padding='valid', activation='relu', name='conv1')(x)

    primary_caps = layers.Conv1D(filters=num_capsules*dim_capsule, kernel_size=9, strides=2, padding='valid', name='primary_caps')(conv1)

    primary_caps_reshaped = layers.Reshape(target_shape=[-1, dim_capsule], name='primary_caps_reshaped')(primary_caps)

    digit_caps = Capsule(num_capsules=num_capsules, dim_capsule=dim_capsule, routings=routings, name='digit_caps')(primary_caps_reshaped)

    out_caps = layers.Lambda(lambda x: keras.backend.sqrt(keras.backend.sum(keras.backend.square(x), 2)), name='out_caps')(digit_caps)

    y = layers.Input(shape=(n_class,))

    masked = layers.Multiply(name='masked')([out_caps, y])

    masked_out = layers.Dense(units=512, activation='relu', name='masked_out')(masked)

    model = models.Model(inputs=[x, y], outputs=masked_out)

    return model


# 训练模型
def train(model, data, epoch_size_frac=1.0, epochs=1, batch_size=128):
    (X_train, Y_train), (X_test, Y_test) = data

    model.compile(optimizer=keras.optimizers.Adam(lr=0.001), loss='mse', metrics=['accuracy'])

    history = model.fit([X_train, Y_train], Y_train, batch_size=batch_size, epochs=epochs, validation_data=[[X_test, Y_test], Y_test])

    return history

以上是一维胶囊网络的Keras代码，接下来给出一个训练样例：

# 加载数据
(X_train, Y_train), (X_test, Y_test) = keras.datasets.mnist.load_data()

# 数据预处理
X_train = X_train.astype('float32') / 255.0
X_test = X_test.astype('float32') / 255.0
Y_train = keras.utils.to_categorical(Y_train.astype('float32'))
Y_test = keras.utils.to_categorical(Y_test.astype('float32'))

# 定义模型参数
input_shape = (X_train.shape[1], 1)
n_class = Y_train.shape[1]
num_capsules = 10
dim_capsule = 16
routings = 3

# 构建模型
model = CapsNet(input_shape, n_class, num_capsules, dim_capsule, routings)

# 训练模型
history = train(model=model, data=((X_train, Y_train), (X_test, Y_test)), epochs=10)

# 打印训练过程中的准确率和损失
print(history.history['accuracy'])
print(history.history['loss'])

以上就是一维胶囊网络的Keras代码及其训练过程。