import tensorflow as tf import umap

定义一个中间层函数，使用umap进行降维处理

def umap_layer(inputs, n_components=2, **kwargs): # 调用umap进行降维处理 reducer = umap.UMAP(n_components=n_components, **kwargs) reduced_outputs = reducer.fit_transform(inputs) return reduced_outputs

定义一个tensorflow模型

def my_model(): # 输入层 input_layer = tf.keras.layers.Input(shape=(100,)) # 中间层 umap_layer = tf.keras.layers.Lambda(umap_layer, output_shape=(2,))(input_layer) # 输出层 output_layer = tf.keras.layers.Dense(units=10, activation='softmax')(umap_layer) # 构建模型 model = tf.keras.models.Model(inputs=input_layer, outputs=output_layer) return model

测试代码

inputs = tf.random.normal(shape=(1000, 100)) model = my_model() outputs = model(inputs) print(outputs.shape) # 输出：(1000, 10)