Python 我无法找出编译器报告错误的原因？

错误：InvalidArgumentError（回溯请参见上文）：形状不兼容：[2]与[4]

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[[Node:mul=mul[T=DT\u FLOAT，\u device=“/job:localhost/replica:0/task:0/cpu:0”]（变量/读取，\u recv\u占位符\u 0）]

我想问题就在这里 而不是在W中使用float32

import tensorflow as tf 
import numpy as np 

W = tf.Variable([0,3], dtype = tf.float32)
b = tf.Variable([-0.3], dtype = tf.float32)

x = tf.placeholder(tf.float32)
linear_model = W * x + b
y = tf.placeholder(tf.float32)

loss = tf.reduce_sum(tf.square(linear_model - y))

optimizer = tf.train.GradientDescentOptimizer(0.01)
train = optimizer.minimize(loss)

x_train = [1,2,3,4]
y_train = [0,-1,-2,-3]

init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)

for i in range(1000):
    sess.run(train, {x: x_train, y: y_train})

curr_W, curr_b, curr_loss = sess.run([W,b,loss], {x: [1,2,3,4], y: [0,-1,-2,-3]})
print("w: %s, b: %s, loss: %s", curr_W, curr_b, curr_loss)

会的

W = tf.Variable([0,3], dtype = tf.float32)
b = tf.Variable([-0.3], dtype = tf.float32)

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你的意思是[0.3]而不是[0,3]（有一个逗号）吗？哦，那真的很有用！天哪！这真是一个详细的错误，伙计！

W = tf.Variable([0.3], dtype = tf.float32)
b = tf.Variable([-0.3], dtype = tf.float32)