tf.metrics.accuracy not working as intended

I have linear regression model that seems to be working fine, but I want to display the accuracy of the model.

First, I initialize the variables and placeholders...

X_train, X_test, Y_train, Y_test = train_test_split(
    X_data, 
    Y_data, 
    test_size=0.2
)

n_rows = X_train.shape[0]

X = tf.placeholder(tf.float32, [None, 89])
Y = tf.placeholder(tf.float32, [None, 1])

W_shape = tf.TensorShape([89, 1])
b_shape = tf.TensorShape([1])

W = tf.Variable(tf.random_normal(W_shape))
b = tf.Variable(tf.random_normal(b_shape))

pred = tf.add(tf.matmul(X, W), b)

cost = tf.reduce_sum(tf.pow(pred-Y, 2)/(2*n_rows-1))

optimizer = tf.train.GradientDescentOptimizer(FLAGS.learning_rate).minimize(cost)

X_train has shape (6702, 89) and Y_train has shape (6702, 1). Next I run the session and I display the cost per epoch as well as the total MSE...

init = tf.global_variables_initializer()

with tf.Session() as sess:

    sess.run(init)

    for epoch in range(FLAGS.training_epochs):

        avg_cost = 0

        for (x, y) in zip(X_train, Y_train):

            x = np.reshape(x, (1, 89))
            y = np.reshape(y, (1,1))
            sess.run(optimizer, feed_dict={X:x, Y:y})

        # display logs per epoch step
        if (epoch + 1) % FLAGS.display_step == 0:

            c = sess.run(
                cost, 
                feed_dict={X:X_train, Y:Y_train}
            )

            y_pred = sess.run(pred, feed_dict={X:X_test})
            test_error = r2_score(Y_test, y_pred)
            print(test_error)

            print("Epoch:", '%04d' % (epoch + 1), "cost=", "{:.9f}".format(c))

    print("Optimization Finished!")

    pred_y = sess.run(pred, feed_dict={X:X_test})
    mse = tf.reduce_mean(tf.square(pred_y - Y_test))

    print("MSE: %4f" % sess.run(mse))

This all seems to work correctly. However, now I want to see the accuracy of my model, so I want to implement tf.metrics.accuracy. The documentation says it has 2 arguments, labels and predictions. I added the following next...

accuracy, accuracy_op = tf.metrics.accuracy(labels=Y_test, predictions=pred)

init_local = tf.local_variables_initializer()

sess.run(init_local)

print(sess.run(accuracy))

Apparently I need to initialize local variales, however I think I am doing something wrong because the accuracy result that gets printed out is 0.0.

I searched everywhere for a working example but I cannot get it to work for my model, what is the proper way to implement it?

I think you are learning a regression model. The tf.metrics.accuracy is supposed to run for a classification model.

When your model predicts 1.2 but your target value is 1.15, it does not make sense to use accuracy to measure whether this is a correct prediction. accuracy is for classification problems (e.g., mnist), when your model predicts a digit to be '9' and your target image is also '9': this is a correct prediction and you get full credit; Or when your model predicts a digit to be '9' but your target image is '6': this is a wrong prediction and you get no credit.

For your regression problem, we measure the difference between prediction and target value either by absolute error - |target - prediction| or mean squared error - the one you used in your MSE calculation. Thus tf.metrics.mean_squared_error or tf.metrics.mean_absolute_error is the one you should use to measure the prediction error for regression models.