SGD代码实现python

SGD（Stochastic Gradient Descent，随机梯度下降）是一种常用的优化算法，用于训练机器学习模型。在Python中，我们可以使用深度学习库如[tensor](https://geek.csdn.net/educolumn/0ebc891269ff76b86c4b41f64bffd5db?spm=1055.2569.3001.10083)Flow或PyTorch来实现SGD。以下是一个简单的使用Python和[tensor](https://geek.csdn.net/educolumn/0ebc891269ff76b86c4b41f64bffd5db?spm=1055.2569.3001.10083)Flow实现SGD的示例： ```python import [tensor](https://geek.csdn.net/educolumn/0ebc891269ff76b86c4b41f64bffd5db?spm=1055.2569.3001.10083)flow as tf # 假设我们有一个简单的线性回归模型 model = tf.keras.models.Sequential([ tf.keras.layers.Dense(units=1, input_shape=) # 定义损失[函数](https://geek.csdn.net/educolumn/ba94496e6cfa8630df5d047358ad9719?dp_token=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpZCI6NDQ0MDg2MiwiZXhwIjoxNzA3MzcxOTM4LCJpYXQiOjE3MDY3NjcxMzgsInVzZXJuYW1lIjoid2VpeGluXzY4NjQ1NjQ1In0.RrTYEnMNYPC7AQdoij4SBb0kKEgHoyvF-bZOG2eGQvc&spm=1055.2569.3001.10083)（如均方误差）和优化器（SGD） loss_fn = tf.keras.losses.MeanSquaredError() optimizer = tf.keras.optimizers.SGD([learn](https://geek.csdn.net/educolumn/00b6a1b1fad077eafad2b50b0230c4b0?spm=1055.2569.3001.10083)ing_rate=0.01) # 学习率设为0.01 # 假设我们有一个数据集x_data和y_data x_data = tf.constant([, , , ]) y_data = tf.constant([, , , ]) # 模型训练 @tf.function def train_step(x, y): with tf.GradientTape() as tape: predictions = model(x) loss = loss_fn(y, predictions) gradients = tape.gradient(loss ```