Moving on to the classifier model in ML using the famous iris data as example. After reading in the data, you need to create the input function, which is a function that returns a tf.data.Dataset object which outputs the following two-element tuple containing of dict like features and value label, below snippet of function accomplish so:
def input_evaluation_set():
features = {'SepalLength': np.array([6.4, 5.0]),
'SepalWidth': np.array([2.8, 2.3]),
'PetalLength': np.array([5.6, 3.3]),
'PetalWidth': np.array([2.2, 1.0])}
labels = np.array([2, 1])
return features, labels
Then using this input function to prepare for ml operation:
def input_fn(features, labels, training=True, batch_size=256):
"""An input function for training or evaluating"""
# Convert the inputs to a Dataset.
dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))
# Shuffle and repeat if you are in training mode.
if training:
dataset = dataset.shuffle(1000).repeat()
return dataset.batch(batch_size)
Now, it’s time to launch the three key steps: build model, train model, evaluate model, then you can use the model to perform prediction.
step 1,
# Build a DNN with 2 hidden layers with 30 and 10 hidden nodes each.
classifier = tf.estimator.DNNClassifier(
feature_columns=my_feature_columns,
# Two hidden layers of 30 and 10 nodes respectively.
hidden_units=[30, 10],
# The model must choose between 3 classes.
n_classes=3)
step2,
# Train the Model.
classifier.train(
input_fn=lambda: input_fn(train, train_y, training=True),
steps=5000)
step3 evaluate,
eval_result = classifier.evaluate(
input_fn=lambda: input_fn(test, test_y, training=False))
print('\nTest set accuracy: {accuracy:0.3f}\n'.format(**eval_result))
If the accuracy is satisfactory, you can use this trained model to predict:
# Generate predictions from the model
expected = ['Setosa', 'Versicolor', 'Virginica']
predict_x = {
'SepalLength': [5.1, 5.9, 6.9],
'SepalWidth': [3.3, 3.0, 3.1],
'PetalLength': [1.7, 4.2, 5.4],
'PetalWidth': [0.5, 1.5, 2.1],
}
def input_fn(features, batch_size=256):
"""An input function for prediction."""
# Convert the inputs to a Dataset without labels.
return tf.data.Dataset.from_tensor_slices(dict(features)).batch(batch_size)
predictions = classifier.predict(
input_fn=lambda: input_fn(predict_x))
for pred_dict, expec in zip(predictions, expected):
class_id = pred_dict['class_ids'][0]
probability = pred_dict['probabilities'][class_id]
print('Prediction is "{}" ({:.1f}%), expected "{}"'.format(
SPECIES[class_id], 100 * probability, expec))
The upshot looks like this
Naixian Zhang 