def weighted_r2_op(predictions, targets, inputs):
""" weighted_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 = weighted_r2_op(y_pred, y_true, input_data)
# 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`.
inputs: `Tensor`.
Returns:
`Float`. The standard error.
"""
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("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.divide(a, b)```
