一维som代码keras

以下是使用Keras实现一维SOM的示例代码：

from keras.layers import Layer
import tensorflow as tf
import numpy as np

class SOMLayer(Layer):
    def __init__(self, output_dim, **kwargs):
        self.output_dim = output_dim
        super(SOMLayer, self).__init__(**kwargs)

    def build(self, input_shape):
        self.input_dim = input_shape[-1]
        self.weight = self.add_weight(name='SOM_weight', shape=(self.output_dim, self.input_dim), initializer='uniform', trainable=True)
        super(SOMLayer, self).build(input_shape)

    def call(self, inputs):
        # Calculate distances between weight vectors and inputs
        # using euclidean distance
        euclidean_distances = tf.reduce_sum(tf.square(tf.subtract(tf.expand_dims(inputs, 1), tf.expand_dims(self.weight, 0))), axis=-1)
        # Get the index of the closest weight vector
        winner_indices = tf.argmin(euclidean_distances, axis=-1)
        # Get the coordinates of the closest weight vector in the SOM grid
        winner_coords = tf.stack([tf.mod(winner_indices, self.output_dim), tf.div(winner_indices, self.output_dim)], axis=-1)
        return winner_coords

    def compute_output_shape(self, input_shape):
        return (None, 2) # Return the coordinates of the closest weight vector in the SOM grid

这个代码实现了一个Keras的自定义层，它将输入映射到SOM网络中最接近的神经元。在这个例子中，我们使用欧几里得距离作为权重向量和输入之间的距离度量。在该层的实现中，我们首先计算输入与权重向量之间的欧几里得距离，然后获取最接近的权重向量的索引和其在SOM网格中的坐标并返回。