TensorFlow 1.15 中间层 UMAP 降维实战：提升模型性能

import tensorflow as tf
import umap

# 定义一个中间层进行umap的函数
def umap_layer(x, n_components=2, n_neighbors=15, min_dist=0.1):
    # 将输入数据降维
    reducer = umap.UMAP(n_components=n_components, n_neighbors=n_neighbors, min_dist=min_dist)
    x_reduced = reducer.fit_transform(x)
    # 将降维后的数据转换为tensor
    x_reduced = tf.convert_to_tensor(x_reduced, dtype=tf.float32)
    # 返回降维后的数据
    return x_reduced

# 定义一个简单的神经网络模型
def simple_model():
    # 输入层
    inputs = tf.keras.Input(shape=(784,))
    # 中间层
    x = tf.keras.layers.Dense(256, activation='relu')(inputs)
    # umap层
    x = umap_layer(x)
    # 输出层
    outputs = tf.keras.layers.Dense(10, activation='softmax')(x)
    # 定义模型
    model = tf.keras.Model(inputs=inputs, outputs=outputs)
    return model

# 加载MNIST数据集
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
# 数据预处理
x_train = x_train.reshape((-1, 784)) / 255.0
x_test = x_test.reshape((-1, 784)) / 255.0
y_train = tf.keras.utils.to_categorical(y_train, num_classes=10)
y_test = tf.keras.utils.to_categorical(y_test, num_classes=10)

# 定义模型
model = simple_model()
# 编译模型
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
# 训练模型
model.fit(x_train, y_train, batch_size=128, epochs=10, validation_data=(x_test, y_test))