parameters: ()
return_statement: return float(num_correct) / float(num_examples)
docstring: Gets the model's classification accuracy on test data.
docstring_summary: Gets the model's classification accuracy on test data.
docstring_tokens: Gets the model s classification accuracy on test data .
function: def get_test_accuracy():
    """Gets the model's classification accuracy on test data."""
    num_examples = data.test.num_examples
    test_images = np.split(data.test.images, num_examples/BATCH_SIZE)
    test_labels = np.split(data.test.labels.astype(np.int32),
                           num_examples/BATCH_SIZE)
    num_correct = 0
    for _, (images, labels) in enumerate(zip(test_images, test_labels)):
      with tf.new_step():
        logits = model(images)
        predictions = tf.argmax(tf.nn.softmax(logits), axis=1)
        num_correct += np.sum(predictions.value == labels)
    return float(num_correct) / float(num_examples)
function_tokens: def get_test_accuracy ( ) : num_examples = data . test . num_examples test_images = np . split ( data . test . images , num_examples / BATCH_SIZE ) test_labels = np . split ( data . test . labels . astype ( np . int32 ) , num_examples / BATCH_SIZE ) num_correct = 0 for _ , ( images , labels ) in enumerate ( zip ( test_images , test_labels ) ) : with tf . new_step ( ) : logits = model ( images ) predictions = tf . argmax ( tf . nn . softmax ( logits ) , axis = 1 ) num_correct += np . sum ( predictions . value == labels ) return float ( num_correct ) / float ( num_examples )