Question

从Tensorflow示例中获取必要的代码以对结构化数据here进行分类，这样我就可以学习在Numeric列上进行训练；我收到以下错误：

ValueError：尝试转换类型不受支持的值（63）（）到张量。

虽然我想我可以尝试转换数据帧中的特定值以使用Tensors（如果该方法甚至可以工作），但由于代码在Colab中有效，但是在PyCharm中引发了错误，因此必须进行其他操作。

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)

Answer 1

似乎Numpy已损坏。卸载并重新安装Numpy之后，该程序即可正常工作。

Tensorflow 2“试图将不支持的类型（<class'numpy.int64'>）的值（63）转换为Tensor”

1 个答案: