import tensorflow as tf import numpy as np # 创建数据集 x_data = np.random.rand(100).astype(np.float32) y_data = x_data * 0.1 + 0.3 # 构建模型 Weights = tf.Variable(tf.random.uniform([1], -1.0, 1.0)) biases = tf.Variable(tf.zeros([1])) y = Weights * x_data + biases # 定义损失函数和优化器 loss = tf.reduce_mean(tf.square(y - y_data)) optimizer = tf.optimizers.SGD(0.5) train = optimizer.minimize(loss) # 初始化变量 init = tf.global_variables_initializer() # 运行计算图 with tf.Session() as sess: sess.run(init) # 训练模型 for step in range(201): sess.run(train) if step % 20 == 0: print(step, sess.run(Weights), sess.run(biases))train = optimizer.minimize(loss) TypeError: _BaseOptimizer.minimize() missing 1 required positional argument: 'var_list'

这是因为 `optimizer.minimize()` 方法需要传入一个 `var_list` 参数，表示需要更新的变量列表。你可以将这个参数设置为 `None`，表示更新所有可训练的变量。修改代码如下：

import tensorflow as tf
import numpy as np

# 创建数据集
x_data = np.random.rand(100).astype(np.float32)
y_data = x_data * 0.1 + 0.3

# 构建模型
Weights = tf.Variable(tf.random.uniform([1], -1.0, 1.0))
biases = tf.Variable(tf.zeros([1]))
y = Weights * x_data + biases

# 定义损失函数和优化器
loss = tf.reduce_mean(tf.square(y - y_data))
optimizer = tf.optimizers.SGD(0.5)
train = optimizer.minimize(loss, var_list=None)  # 更新所有可训练的变量

# 初始化变量
init = tf.global_variables_initializer()

# 运行计算图
with tf.Session() as sess:
    sess.run(init)

    # 训练模型
    for step in range(201):
        sess.run(train)
        if step % 20 == 0:
            print(step, sess.run(Weights), sess.run(biases))