一维堆叠降噪自编码器与一维som结合代码keras

以下是一维堆叠降噪自编码器与一维SOM结合的Keras代码示例：

from keras.layers import Input, Dense, Dropout, Conv1D, MaxPooling1D, UpSampling1D, Reshape, Flatten
from keras.models import Model
from keras import backend as K
import numpy as np

# 自定义Keras层，实现SOM
class SOMLayer(object):

    def __init__(self, output_dim, beta=1.0, **kwargs):
        self.output_dim = output_dim
        self.beta = beta
        super(SOMLayer, self).__init__(**kwargs)

    def build(self, input_shape):
        self.input_dim = input_shape[-1]
        self.codebook = K.variable(np.random.randn(self.output_dim, self.input_dim))
        super(SOMLayer, self).build(input_shape)

    def call(self, x):
        # 计算输入向量和所有码字之间的距离
        x = K.expand_dims(x, axis=1)
        dists = K.sum(K.square(x - self.codebook), axis=-1)

        # 计算获胜节点的索引
        winner = K.argmin(dists, axis=-1)

        # 计算获胜节点的邻居节点
        sigma = K.cast(K.shape(self.codebook)[0], dtype='float32') / 2.0
        winner_coords = K.stack([winner // self.output_dim, winner % self.output_dim])
        distances_from_winner = K.sum(K.square(K.cast(K.stack([
            K.arange(0, self.output_dim),
            K.arange(0, self.output_dim)], axis=0), dtype='float32') - K.expand_dims(winner_coords, axis=-1)), axis=0)
        neighbours = K.exp(- distances_from_winner / (2 * sigma ** 2))

        # 更新码字向量
        lr = self.beta
        delta = lr * K.expand_dims(neighbours, axis=-1) * (x - self.codebook)
        self.updates = [K.update_add(self.codebook, delta)]
        return winner

    def compute_output_shape(self, input_shape):
        return (input_shape[0],)

# 堆叠自编码器模型
def stacked_autoencoder(input_dim, encoding_dim, hidden_dims):
    # 编码器
    input_layer = Input(shape=(input_dim,))
    encoded = input_layer
    for h_dim in hidden_dims:
        encoded = Dense(h_dim, activation='relu')(encoded)
    encoded = Dense(encoding_dim, activation='relu')(encoded)

    # 解码器
    decoded = encoded
    for h_dim in reversed(hidden_dims):
        decoded = Dense(h_dim, activation='relu')(decoded)
    decoded = Dense(input_dim, activation='linear')(decoded)

    # 堆叠自编码器模型
    autoencoder = Model(inputs=input_layer, outputs=decoded)
    encoder = Model(inputs=input_layer, outputs=encoded)

    return autoencoder, encoder

# 一维堆叠降噪自编码器与一维SOM结合模型
def som_stacked_autoencoder(input_dim, encoding_dim, hidden_dims, som_output_dim):
    # 堆叠自编码器模型
    autoencoder, encoder = stacked_autoencoder(input_dim, encoding_dim, hidden_dims)

    # SOM层
    input_layer = Input(shape=(encoding_dim,))
    som_layer = SOMLayer(som_output_dim)(input_layer)
    som_encoder = Model(inputs=input_layer, outputs=som_layer)

    # 合并自编码器和SOM
    input_layer = Input(shape=(input_dim,))
    encoded = encoder(input_layer)
    som_output = som_encoder(encoded)
    autoencoder_with_som = Model(inputs=input_layer, outputs=[autoencoder(input_layer), som_output])

    return autoencoder_with_som

在上述代码中，我们首先定义了一个自定义Keras层，实现了SOM功能。然后，我们定义了一个堆叠自编码器模型，该模型由多个Dense层组成。接下来，我们定义了一个将自编码器和SOM结合起来的模型。在这个模型中，我们将自编码器的编码层的输出作为SOM层的输入，并将SOM层的输出连接到自编码器的输出中。

最后，我们可以使用以下代码训练模型：

# 加载数据
x_train = ...
x_test = ...

# 构建模型
ae_som = som_stacked_autoencoder(input_dim=x_train.shape[1], encoding_dim=64, hidden_dims=[128, 256, 512], som_output_dim=10)
ae_som.compile(optimizer='adam', loss=['mse', 'sparse_categorical_crossentropy'], loss_weights=[0.5, 0.5])

# 训练模型
ae_som.fit(x_train, [x_train, np.zeros((x_train.shape[0],))], epochs=100, batch_size=128, shuffle=True, validation_data=(x_test, [x_test, np.zeros((x_test.shape[0],))]))

在训练模型时，我们需要将SOM层的输出设置为零，因为我们只关心自编码器的重构损失和SOM层的输出。