Tensorflow得到'ValueError：调用层“规范化”时遇到异常尺寸必须相等'

Question

我正在关注 Tensorflow 的回归教程并创建了一个多变量线性回归和深度神经网络，但是，当我尝试在test_results中收集测试集时，出现以下错误：

    ValueError: Exception encountered when calling layer "normalization" (type Normalization).

    Dimensions must be equal, but are 7 and 8 for '{{node sequential/normalization/sub}} = Sub[T=DT_FL    Dimensions must be equal, but are 7 and     Dimensions must be equal, but are 7 and 8 for '{{node sequential/normalization/sub}} = Sub[T=DT_FLOAT](sequential/Cast, sequential/normalizati
on/sub/y)' with input shapes: [?,7], [1,8].

    Call arguments received by layer "normalization" (type Normalization):
      • inputs=tf.Tensor(shape=(None, 7), dtype=float32)

这是线性回归的一些代码，从拆分标签开始，错误出现在最后一行， test_results['linear_model'] = linear_model.evaluate(test_features, test_labels, verbose = 0)但是，我能够生成错误图和一切似乎都正常工作，所以我不完全确定获得测试结果的错误是什么。 任何帮助将非常感激！

#Split labels
train_features = train_dataset.copy()
test_features = test_dataset.copy()

train_labels = train_features.pop('HCO3')
test_labels = test_features.pop('HCO3')

train_features = np.asarray(train_dataset.copy()).astype('float32')
#print(train_features.tail())

#Normalization
normalizer = tf.keras.layers.Normalization(axis=-1)
normalizer.adapt(np.array(train_features))
first = np.array(train_features[:1])

linear_model = tf.keras.Sequential([
    normalizer,
    layers.Dense(units=1)
])
#Compilation
linear_model.compile(
    optimizer=tf.keras.optimizers.Adam(learning_rate=0.1),
    loss='mean_absolute_error'
)
history = linear_model.fit(
    train_features,
    train_labels,
    epochs=100,
    # Suppress logging.
    verbose=0,
    # Calculate validation results on 20% of the training data.
    validation_split = 0.2)

#Track error for later
test_results = {}
test_results['linear_model'] = linear_model.evaluate(test_features, test_labels, verbose = 0)

Answer 1

由于弹出，您丢失了数据框中的outcome列。 尝试使用提取该列

train_labels = train_features['HC03']
test_labels = test_features['HC03']