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Classify Iris Dataset Using DNNClassifer

This tutorial demonstrates how to

  • Train a DNNClassifer on the Iris flower dataset.
  • Use the trained DNNClassifer to predict the three species of Iris(Iris setosa, Iris virginica and Iris versicolor).

The Dataset

The Iris data set contains four features and one label. The four features identify the botanical characteristics of individual Iris flowers. Each feature is stored as a single float number. The label indicates the species of individual Iris flowers. The label is stored as a integer and has possible value of 0, 1, 2.

We have prepared the Iris dataset in table iris.train and iris.test. We will use them as training data and test data respectively.

We can have a quick peek of the data by running the following standard SQL statements.

%%sqlflow
describe iris.train;
%%sqlflow
select * from iris.train limit 5;

Train

Let’s train a ternary DNNClassifier, which has two hidden layers with ten hidden units each. This can be done by specifying the training clause for SQLFlow’s extended syntax.

TO TRAIN DNNClassifier
WITH
  model.n_classes = 3,
  model.hidden_units = [10, 10]

To specify the training data, we use standard SQL statements like SELECT * FROM iris.train.

We explicit specify which column is used for features and which column is used for the label by writing

COLUMN sepal_length, sepal_width, petal_length, petal_width
LABEL class

At the end of the training process, we save the trained DNN model into table sqlflow_models.my_dnn_model by writing INTO sqlflow_models.my_dnn_model.

Putting it all together, we have our first SQLFlow training statement.

%%sqlflow
SELECT * FROM iris.train TO TRAIN DNNClassifier WITH
  model.n_classes = 3,
  model.hidden_units = [10, 10],
  train.epoch = 10
COLUMN sepal_length, sepal_width, petal_length, petal_width
LABEL class
INTO sqlflow_models.my_dnn_model;

The above training statement usually takes a few minutes to run, and the outputs look like the following:

{'accuracy': 0.4, 'average_loss': 1.0920922, 'loss': 1.0920922, 'global_step': 1100}

As we’ve seen, the average loss of the above training statement doesn’t look very good; an ideal value for the Iris flower dataset should be less 0.4. Let us see what we can do to improve model quality.

Tune

In order to improve the model performance, we can tune the hyperparameters manually.

In machine learning, a hyperparameter is a parameter whose value is set before the learning process begins. By contrast, the values of other parameters are derived via training.

According to the Universal approximation theorem, the architecture of a multilayer feed-forward network (such as our DNNClassifier) gives the neural network the potential of being a universal approximator.

Our first performance improvement trial is to tune the architecture of our model by increasing the hidden_units of each layer to 100 because the width of feed-forward networks matters in the theorem.

%%sqlflow
SELECT * FROM iris.train TO TRAIN DNNClassifier WITH
  model.n_classes = 3,
  model.hidden_units = [100, 100],
  train.epoch = 10
COLUMN sepal_length, sepal_width, petal_length, petal_width
LABEL class
INTO sqlflow_models.my_dnn_model;

The above statement will give a better result like:

{'accuracy': 0.72, 'average_loss': 0.5695601, 'loss': 0.5695601, 'global_step': 1100}

However, DNNs are highly expressive models, for our tiny dataset, we still have a lot of room for improvement.

Our second performance improvement trial is to enlarge the learning rate of the underlying optimizer of DNNClassifier to speed up the learning process. Optimizers and the learning rate are the the most important hyperparameters in DNNs. The default optimizer of DNNClassifier is AdaGrad with a default learning rate of 0.001.

Theoretically speaking, the learning rate of AdaGrad should be set as large as possible, but no larger. Practically speaking, a slightly larger learning rate always makes AdaGrad perform slightly better as long as the dying neuron problem doesn’t arise. Let us increase the learning rate by 10 times:

%%sqlflow
SELECT * FROM iris.train TO TRAIN DNNClassifier WITH
  model.n_classes = 3,
  model.hidden_units = [100, 100],
  optimizer.learning_rate=0.1,
  train.epoch = 10
COLUMN sepal_length, sepal_width, petal_length, petal_width
LABEL class
INTO sqlflow_models.my_dnn_model;

The above statement will give a decent result like:

{'accuracy': 0.98, 'average_loss': 0.10286382, 'loss': 0.10286382, 'global_step': 1100}

That’s all you have to know about tuning models in this tutorial. In fact, tuning is very crucial to make machine learning work and usually takes a large fraction of the working hours of data scientists and machine learning engineers.

Automated tuning

If you feel that tuning models manually is time-consuming and tedious (it is indeed), automated machine learning (AutoML) can be a fine alternative.

SQLFlow supports automated neural architecture search (NAS) via specific estimators. To improve model performance, we can use sqlflow_models.AutoClassifier instead of DNNClassifier. We don’t need to specify the hidden_units in the WITH clause in the above example because sqlflow_models.AutoClassifier will automatically search for the architecture.

%%sqlflow
SELECT * FROM iris.train TO TRAIN sqlflow_models.AutoClassifier WITH
  model.n_classes = 3,
  train.epoch = 10
COLUMN sepal_length, sepal_width, petal_length, petal_width
LABEL class
INTO sqlflow_models.my_dnn_model;

The above training statement will take longer to run because the AutoClassifier has to search for the most appropriate neural architectures. The statement will give a result like:

{'accuracy': 0.98, 'average_loss': 0.08678584, 'loss': 0.08678584, 'global_step': 1000}

Although this seems to be very close to the manually tuned version in the last section, because the training process of DNNClassifier and AutoClassifier is somewhat stochastic, it may give an average loss slightly large or smaller than the manually tuned version.

The SQLFlow team plans to support more NAS models as well as other AutoML technics like automatic hyperparameter tuning in the near future.

Predict

SQLFlow also supports prediction out-of-the-box.

To specify the prediction data, we use standard SQL statements like SELECT * FROM iris.test.

Say we want the model, previously stored at sqlflow_models.my_dnn_model, to read the prediction data and write the predicted result into table iris.predict column class. We can write the following SQLFlow prediction statement.

%%sqlflow
SELECT * FROM iris.test TO PREDICT iris.predict.class USING sqlflow_models.my_dnn_model;

After the prediction, we can checkout the prediction result by

%%sqlflow
SELECT * FROM iris.predict LIMIT 5;