init = tf.global_variables_initializer()
costs = []
EPOCHS = 300
with tf.Session() as sess:
    sess.run(init)
    for i in range(EPOCHS):
        _, c, _ = sess.run([train_op, cost, prediction], feed_dict={X:X_train, y: y_train})
        costs.append(c)
        if i % 10 == 0:
            print('cost: {}, epoch: {}'.format(c, i))

            X_test, X_test_strings, y_test, y_test_strings = generate_data(100)
            p = sess.run(prediction, feed_dict={X: X_test, y: y_test})
            prediction_idxs = np.argmax(p, axis=1)
            prediction_vals = prediction_idxs + 1
correct = 0.0
            for i in range(len(y_test_strings)):
                string = X_test_strings[i]
                actual_val = y_test_strings[i]
                predicted_val = prediction_vals[i]
                # Print the first 5 examples
                if i < 5:
                    print('string: {}, pred: {}, actual: {}'.format(string, predicted_val, actual_val))

                if predicted_val == actual_val:
                    correct += 1
            print("{}% accuracy\n\n".format(correct * 100 / len(y_test_strings)))