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!