Tf.reduce_mean(y_true,y_predicted)

with tf.name_scope("MSE"):
    y_true = tf.placeholder("float32", shape=(None,), name="y_true")
    y_predicted = tf.placeholder("float32", shape=(None,), name="y_predicted")
    # Your code goes here
    mse = tf.reduce_mean((y_predicted - y_true)**2)
    
def compute_mse(vector1, vector2):
    return mse.eval({y_true: vector1, y_predicted: vector2})

Source: aadimator.github.io

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