A neural network can be applied to the classification problem. Given this example, determine the class.

Tensorflow has an implementation for the neural network included, which we’ll use to on csv data (the iris dataset).

Related Course:
Deep Learning with TensorFlow 2 and Keras

Iris Dataset

The iris dataset is split in two files: the training set and the test set. The network has a training phase. After training is completed it can be used to predict.

What does the iris dataset contain?

It 3 contains classes of plants (0,1,2) which is the last parameter of the file.
It has 4 attributes:

  • sepal length in cm
  • sepal width in cm
  • petal length in cm
  • petal width in cm

In short: grabbed a bunch of plants of different types and measured. This is then stored in text files.
You can download the iris dataset on github.

The traning set is a simple file that looks like this:

6.4,2.8,5.6,2.2,2
5.0,2.3,3.3,1.0,1
4.9,2.5,4.5,1.7,2
4.9,3.1,1.5,0.1,0
5.7,3.8,1.7,0.3,0
...

The test set looks similar

5.9,3.0,4.2,1.5,1
6.9,3.1,5.4,2.1,2
5.1,3.3,1.7,0.5,0
6.0,3.4,4.5,1.6,1
...

The files have a header, that we’ll ignore.

Neural network on csv data

The csv files can be loaded with these two lines:

training_set = tf.contrib.learn.datasets.base.load_csv_with_header(
        filename=IRIS_TRAINING,
        target_dtype=np.int,
        features_dtype=np.float32)

test_set = tf.contrib.learn.datasets.base.load_csv_with_header(
        filename=IRIS_TEST,
        target_dtype=np.int,
        features_dtype=np.float32)

The capital letters are the file names. Load type as integer and features as float.
Specify that all features have real data

feature_columns = [tf.contrib.layers.real_valued_column("", dimension=4)]

Create the neural network with one line of code. As second parameter the number of hidden units per layers are told. All layers are fully connected. [5,10] means the first layer has 5 nodes, the second layer has 10 nodes.

Then specify the number of possible classes with n_classes. In our dataset we have only 3 types of flowers (0,1,2). 

classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,
                                            hidden_units=[5,10,5],
            			            n_classes=3)

Fit the model.

classifier.fit(input_fn=get_train_inputs, steps=2000)

# Define the test inputs
def get_test_inputs():
    x = tf.constant(test_set.data)
    y = tf.constant(test_set.target)

    return x, y

Then you can evaluate the classifier

# Define the test inputs
def get_test_inputs():
    x = tf.constant(test_set.data)
    y = tf.constant(test_set.target)

    return x, y

# Evaluate accuracy.
accuracy_score = classifier.evaluate(input_fn=get_test_inputs,
                                     steps=1)["accuracy"]

print("\nTest Accuracy: {0:f}\n".format(accuracy_score))

Then given 4 new samples, you can predict the type (class) of flower:

# Classify new flower
def new_samples():
    return np.array([[6.4, 2.7, 5.6, 2.1]], dtype=np.float32)

predictions = list(classifier.predict(input_fn=new_samples))

print("Predicted class: {}\n".format(predictions))

Neural Network on CSV sample

The example below summarizes what we talked about. You can copy this code and run it. Don’t forget to get the iris dataset (train and test).

# DNNClassifier on CSV input dataset.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import urllib

import numpy as np
import tensorflow as tf

# Data sets
IRIS_TRAINING = "iris_training.csv"
IRIS_TEST = "iris_test.csv"

def main():
  # Load datasets.
  training_set = tf.contrib.learn.datasets.base.load_csv_with_header(
      filename=IRIS_TRAINING,
      target_dtype=np.int,
      features_dtype=np.float32)
  
  test_set = tf.contrib.learn.datasets.base.load_csv_with_header(
      filename=IRIS_TEST,
      target_dtype=np.int,
      features_dtype=np.float32)

  # Specify that all features have real-value data
  feature_columns = [tf.contrib.layers.real_valued_column("", dimension=4)]

  # Build 3 layer DNN
  classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,
                                              hidden_units=[5,10,5],
                                              n_classes=3)
  # Define the training inputs
  def get_train_inputs():
    x = tf.constant(training_set.data)
    y = tf.constant(training_set.target)

    return x, y

  # Fit model.
  classifier.fit(input_fn=get_train_inputs, steps=2000)

  # Define the test inputs
  def get_test_inputs():
    x = tf.constant(test_set.data)
    y = tf.constant(test_set.target)

    return x, y

  # Evaluate accuracy.
  accuracy_score = classifier.evaluate(input_fn=get_test_inputs,
                                       steps=1)["accuracy"]

  print("\nTest Accuracy: {0:f}\n".format(accuracy_score))

  # Classify new flower
  def new_samples():
    return np.array([[6.4, 2.7, 5.6, 2.1]], dtype=np.float32)

  predictions = list(classifier.predict(input_fn=new_samples))

  print("Predicted class: {}\n".format(predictions))

if __name__ == "__main__":
    main()