fix r2 metric

tflearn/metrics.py

@@ -163,14 +163,43 @@ def __init__(self, name=None):
         super(R2, self).__init__(name)
         self.name = "R2" if not name else name
 
-    def build(self, predictions, targets, inputs):
+    def build(self, predictions, targets, inputs=None):
         """ Build standard error tensor. """
         self.built = True
-        self.tensor = r2_op(predictions, targets, inputs)
+        self.tensor = r2_op(predictions, targets)
         # Add a special name to that tensor, to be used by monitors
         self.tensor.m_name = self.name
 
 
+class WeightedR2(Metric):
+    """ Weighted Standard Error.
+
+    Computes coefficient of determination. Useful to evaluate a linear
+    regression.
+
+    Examples:
+        ```python
+        # To be used with TFLearn estimators
+        r2 = R2()
+        regression = regression(net, metric=r2)
+        ```
+
+    Arguments:
+        name: The name to display.
+
+    """
+
+    def __init__(self, name=None):
+        super(WeightedR2, self).__init__(name)
+        self.name = "R2" if not name else name
+
+    def build(self, predictions, targets, inputs):
+        """ Build standard error tensor. """
+        self.built = True
+        self.tensor = weighted_r2_op(predictions, targets, inputs)
+        # Add a special name to that tensor, to be used by monitors
+        self.tensor.m_name = self.name
+
 
 class Prediction_Counts(Metric):
     """ Prints the count of each category of prediction that is present in the predictions.
@@ -200,7 +229,6 @@ def build(self, predictions, targets, inputs=None):
 prediction_counts = Prediction_Counts
 
 
-
 # ----------
 # Metric ops
 # ----------
@@ -309,7 +337,37 @@ def top_k_op(predictions, targets, k=1):
     return acc
 
 
-def r2_op(predictions, targets, inputs):
+def r2_op(predictions, targets):
+    """ r2_op.
+
+    An op that calculates the standard error.
+
+    Examples:
+        ```python
+        input_data = placeholder(shape=[None, 784])
+        y_pred = my_network(input_data) # Apply some ops
+        y_true = placeholder(shape=[None, 10]) # Labels
+        stderr_op = r2_op(y_pred, y_true)
+
+        # Calculate standard error by feeding data X and labels Y
+        std_error = sess.run(stderr_op, feed_dict={input_data: X, y_true: Y})
+        ```
+
+    Arguments:
+        predictions: `Tensor`.
+        targets: `Tensor`.
+
+    Returns:
+        `Float`. The standard error.
+
+    """
+    with tf.name_scope('StandardError'):
+        a = tf.reduce_sum(tf.square(predictions))
+        b = tf.reduce_sum(tf.square(targets))
+        return tf.div(a, b)
+
+
+def weighted_r2_op(predictions, targets, inputs):
     """ r2_op.
 
     An op that calculates the standard error.
@@ -334,12 +392,12 @@ def r2_op(predictions, targets, inputs):
         `Float`. The standard error.
 
     """
-    with tf.name_scope('StandardError'):
+    with tf.name_scope('WeightedStandardError'):
         if hasattr(inputs, '__len__'):
             inputs = tf.add_n(inputs)
         if inputs.get_shape().as_list() != targets.get_shape().as_list():
-            raise Exception("R2 metric requires Inputs and Targets to have "
-                            "same shape.")
+            raise Exception("Weighted R2 metric requires Inputs and Targets to "
+                            "have same shape.")
         a = tf.reduce_sum(tf.square(predictions - inputs))
         b = tf.reduce_sum(tf.square(targets - inputs))
         return tf.div(a, b)