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[英]Exception encountered when calling layer "dense_6" (type Dense). Dimensions must be equal
[英]Tensorflow getting ' ValueError: Exception encountered when calling layer "normalization" Dimensions must be equal'
我正在關注 Tensorflow 的回歸教程並創建了一個多變量線性回歸和深度神經網絡,但是,當我嘗試在test_results
中收集測試集時,出現以下錯誤:
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)
這是線性回歸的一些代碼,從拆分標簽開始,錯誤出現在最后一行, test_results['linear_model'] = linear_model.evaluate(test_features, test_labels, verbose = 0)
但是,我能夠生成錯誤圖和一切似乎都正常工作,所以我不完全確定獲得測試結果的錯誤是什么。 任何幫助將非常感激!
#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)
由於彈出,您丟失了數據框中的outcome
列。 嘗試使用提取該列
train_labels = train_features['HC03']
test_labels = test_features['HC03']
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