CODEWITHSUNDEEP
pythontensorflowlinear-regressionvalueerror
T D Nguyen
T D Nguyen

Reputation: 7613

ValueError: Items of feature_columns must be either a DenseColumn or CategoricalColumn

The code is simple:

    x_data = np.linspace(0, 10.0, 1000000)
    y_true = (0.5 * x_data) + 5 
    x_train, x_eval, y_train, y_eval = train_test_split(x_data, y_true, test_size = 0.25, random_state=101)
    input_func = tf.estimator.inputs.numpy_input_fn({'x':x_train}, y_train, 
                                                batch_size=8, num_epochs=None, shuffle= True)
    estimator = tf.estimator.LinearRegressor(feature_columns=feat_cols)
    estimator.train(input_fn=input_func, steps=1000)

The error:

INFO:tensorflow:Calling model_fn. --------------------------------------------------------------------------- ValueError Traceback (most recent call last) in () ----> 1 estimator.train(input_fn=input_func, steps=1000) 2 #eval_metrics = estimator.evaluate(input_fn=eval_input_func, steps=1000)

8 frames /usr/local/lib/python3.6/dist-packages/tensorflow/python/feature_column/feature_column_v2.py in init(self, feature_columns, units, sparse_combiner, trainable, name, **kwargs) 498 raise ValueError( 499 'Items of feature_columns must be either a ' --> 500 'DenseColumn or CategoricalColumn. Given: {}'.format(column)) 501 502 self._units = units

ValueError: Items of feature_columns must be either a DenseColumn or CategoricalColumn. Given: SequenceNumericColumn(key='x', shape=(1,), default_value=0.0, dtype=tf.float32, normalizer_fn=None)

Upvotes: 0

Views: 209

Answers (1)

Kaushik Roy
Kaushik Roy

Reputation: 1685

The following code works fine for training and prediction.

x_data = np.linspace(0, 10.0, 1000)
print(x_data.shape)
y_true = (0.5 * x_data) + 5
print(y_true.shape)
x_train, x_eval, y_train, y_eval = train_test_split(x_data, y_true, test_size=0.25, random_state=101)
train_func = tf.estimator.inputs.numpy_input_fn({'x': x_train}, y_train, batch_size=8, num_epochs=10, shuffle=True)

features = [tf.contrib.layers.real_valued_column("x", dimension=1)]
estimator = tf.estimator.LinearRegressor(feature_columns=features)

estimator.train(input_fn=train_func, steps=100) # Fit the model to training data.

eval_func = tf.estimator.inputs.numpy_input_fn({'x': x_eval}, batch_size=1, num_epochs=1, shuffle=False)

result = estimator.predict(eval_func) # Predict scores

print("predict_scores", list(result))

Upvotes: 1

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