Neural Network Programming with TensorFlow

Finding the accuracy of the model

Accuracy of the model is found by computing mean accuracy over the test set. It is implemented in the following method:

def score(self, test_X, test_Y):  ...

Here, the parameters are as follows:

• test_X: array_like, shape (n_samples, n_features), Test data
• test_Y: array_like, shape (n_samples, n_features), Test labels
• return float: mean accuracy over the test set

def score(self, test_X, test_Y):    with self.tf_graph.as_default():        with tf.Session() as self.tf_session:            self.tf_saver.restore(self.tf_session, self.model_path)            feed = {                self.input_data: test_X,                self.input_labels: test_Y,                self.keep_prob: 1            }            return self.accuracy.eval(feed)

In the next section, we will look at how DBN implementation can be used on ...