我正在使用youtube上的教程。我把我自己的旋转它，虽然代码基本上是相同的，我得到一个错误消息说，找到的形状不匹配输入形状

ValueError: Input 0 of layer "sequential" is incompatible with the layer: expected shape=(None, 621, 17), found shape=(None, 17)

导致这一点的代码如下所示

import pandas as pd
from sklearn.model_selection import train_test_split
import tensorflow as tf

datasetFull = pd.read_csv('College_Data.csv')
dataset = datasetFull.drop("Name", axis=1)

x = dataset.drop("Private", axis=1)

y = dataset["Private"]
y.replace('Yes', 1)
y.replace('No', 0)

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)

print(x_train.shape)

model = tf.keras.models.Sequential()

model.add(tf.keras.layers.Dense(256, input_shape=x_train.shape, activation='sigmoid'))
model.add(tf.keras.layers.Dense(256, activation='sigmoid'))
model.add(tf.keras.layers.Dense(1, activation='sigmoid'))

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

tf.reshape(x_train, [621, 17])

print(x_train.shape)

model.fit(x_train, y_train, epochs=1000)

不确定这是否相关，但我也收到了类似这样的错误消息，这些消息似乎并没有真正停止进程。

This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: SSE SSE2 SSE3 SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.

如您所见，在将输入数据的形状作为输入形状并将其提交给fit之前，我尝试print输入数据的形状
我还尝试在使用tf.reshape(x_train, [621, 17])将输入数据提交给Keras之前对其进行整形
不管我做了什么，总是会出现同样的错误：

ValueError: Input 0 of layer "sequential" is incompatible with the layer: expected shape=(None, 621, 17), found shape=(None, 17)