Getting ' ValueError: Exception encountered when calling layer "normalization" Dimensions must be equal'

TensorFlow General Discussion
1

I am following Tensorflow’s regression tutorial and have created a multivariable linear regression and deep neural network however, when I am trying to collect the test set in test_results, I get the following error:

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_FLOAT](sequential/Cast, sequential/normalization/sub/y)' with input shapes: [?,7], [1,8].

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

Here is the code for the linear regression, the error appears on the last line, ‘test_results[‘linear_model’] = linear_model.evaluate(test_features, test_labels, verbose = 0)’

#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)

I am also able to generate the error plots and everything seems to work fine otherwise, so I’m not entirely sure what the error is.
Any help would be much appreciated!

3

Update: I managed to fix this by converting test_features to an ndarray as well (just like I did with train_features