从Tensorflow 示例中获取必要的代码来对结构化数据进行分类，以便我可以学习在数字列上进行训练;我收到以下错误：
值错误： 尝试转换具有不受支持的类型的值 (63( (( 到张量。
虽然我想我可以尝试将数据帧中的特定值转换为使用张量(如果这甚至有效的话(，但当代码在 Colab 中工作但在 PyCharm 中抛出错误时，必须发生其他事情。
import pandas as pd
import tensorflow as tf
from tensorflow import feature_column
from tensorflow.python.keras import layers
from sklearn.model_selection import train_test_split
URL = 'https://storage.googleapis.com/applied-dl/heart.csv'
dataframe = pd.read_csv(URL)
dataframe.head()
train, test = train_test_split(dataframe, test_size=0.2)
train, val = train_test_split(train, test_size=0.2)
print(len(train), 'train examples')
print(len(val), 'validation examples')
print(len(test), 'test examples')
# A utility method to create a tf.data dataset from a Pandas Dataframe
def df_to_dataset(dataframe, shuffle=True, batch_size=32):
dataframe = dataframe.copy()
labels = dataframe.pop('target')
ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))
if shuffle:
ds = ds.shuffle(buffer_size=len(dataframe))
ds = ds.batch(batch_size)
return ds
feature_columns = []
# numeric cols
for header in ['age', 'trestbps', 'chol', 'thalach', 'oldpeak', 'slope', 'ca']:
feature_columns.append(feature_column.numeric_column(header))
feature_layer = tf.keras.layers.DenseFeatures(feature_columns)
batch_size = 32
train_ds = df_to_dataset(train, batch_size=batch_size)
val_ds = df_to_dataset(val, shuffle=False, batch_size=batch_size)
test_ds = df_to_dataset(test, shuffle=False, batch_size=batch_size)
"""## Create, compile, and train the model"""
model = tf.keras.Sequential([
feature_layer,
layers.Dense(128, activation='relu'),
layers.Dense(128, activation='relu'),
layers.Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'],
run_eagerly=True)
model.fit(train_ds,
validation_data=val_ds,
epochs=5)
loss, accuracy = model.evaluate(test_ds)
print("Accuracy", accuracy)
似乎 Numpy 已损坏。卸载 Numpy 并重新安装后，该程序运行正常。