1aff940e60d7d62bd82ddf7469e666197832e212,src/classifiers/cnn_unittest.py,TestCNNModel,test_mnist,#TestCNNModel#,55

Before Change

        im_shape = X_train[0].shape

        x = tf.placeholder(tf.float32, shape=(None,im_shape[0],im_shape[1],im_shape[2]))
        y = tf.placeholder(tf.float32, shape=(None,NB_CLASSES))

        model = cnn.cnn_model(im_shape,act="relu")
        predictions = model(x)

        def evaluate():
            // Evaluate the accuracy of the MNIST model on legitimate test examples
            eval_params = {"batch_size": BATCH_SIZE}

After Change

        scores = model.evaluate(X_test,Y_test)

        print("\naccuracy: %.2f%%" % (scores[1] * 100))

if __name__ == "__main__":
    unittest.main()

Instances