在python中为plceholder定义feed_dict

tf.placeholder(dtype, shape=None, name=None)

import tensorflow as tf

# 定义placeholder
x = tf.placeholder(tf.float32, shape=(None, 10), name='x')
y = tf.placeholder(tf.float32, shape=(None, 1), name='y')

# 定义模型
w = tf.Variable(tf.random_normal((10, 1)), name='w')
b = tf.Variable(tf.zeros((1,)), name='b')
output = tf.matmul(x, w) + b

# 定义损失函数和优化器
loss = tf.reduce_mean(tf.square(output - y))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1)
train_op = optimizer.minimize(loss)

# 准备输入数据
x_data = ...
y_data = ...

# 运行模型
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range(num_iterations):
        # 通过feed_dict传递输入数据
        sess.run(train_op, feed_dict={x: x_data, y: y_data})