数据基数不明确 sklearn.train

Question

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

我正在尝试制作一个人工智能，但我的代码给出了一个错误，我不知道如何修复它？

这是错误

ValueError: Data cardinality is ambiguous:
  x sizes: 455
  y sizes: 114
Make sure all arrays contain the same number of samples.

这是全部代码

import pandas as pd
        dataset = pd.read_csv('cancer.csv')

x = dataset.drop(columns=["diagnosis(1=m, 0=b)"])

y = dataset["diagnosis(1=m, 0=b)"]

from sklearn.model_selection import train_test_split
        x_train, x_test, y_test, y_train = train_test_split(x, y, test_size=0.2)

import tensorflow as tf       
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_crossentrop', metrics=['accuracy'])

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

Answer 1

我认为你错误地解压了你的训练测试数据集。

• （错）

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

• （正确）

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