Dfyjkt

I'm trying to write a hook that will allow me to compute some global metrics (rather than batch-wise metrics). To prototype, I thought I'd get a simple hook up and running that would capture and remember true positives. It looks like this:

class TPHook(tf.train.SessionRunHook):

 def after_create_session(self, session, coord):
 print("Starting Hook")

 tp_name = 'metrics/f1_macro/TP'
 self.tp = 
 self.args = session.graph.get_operation_by_name(tp_name)
 print(f"Got Args: self.args")

 def before_run(self, run_context):
 print("Starting Before Run")
 return tf.train.SessionRunArgs(self.args)

 def after_run(self, run_context, run_values):
 print("After Run")
 print(f"Got Values: run_values.results")

However, the values returned in the "after_run" part of the hook are always None. I tested this in both the train and evaluation phase. Am I misunderstanding something about how the SessionRunHooks are supposed to work?

Maybe relevant information:
The model was build in keras and converted to an estimator with the keras.estimator.model_to_estimator() function. The model has been tested and works fine, and the op that I'm trying to retrieve in the hook is defined in this code block:

def _f1_macro_vector(y_true, y_pred):
 """Computes the F1-score with Macro averaging.

 Arguments:
 y_true tf.Tensor -- Ground-truth labels
 y_pred tf.Tensor -- Predicted labels

 Returns:
 tf.Tensor -- The computed F1-Score
 """
 y_true = K.cast(y_true, tf.float64)
 y_pred = K.cast(y_pred, tf.float64)

 TP = tf.reduce_sum(y_true * K.round(y_pred), axis=0, name='TP')
 FN = tf.reduce_sum(y_true * (1 - K.round(y_pred)), axis=0, name='FN')
 FP = tf.reduce_sum((1 - y_true) * K.round(y_pred), axis=0, name='FP')

 prec = TP / (TP + FP)
 rec = TP / (TP + FN)

 # Convert NaNs to Zero
 prec = tf.where(tf.is_nan(prec), tf.zeros_like(prec), prec)
 rec = tf.where(tf.is_nan(rec), tf.zeros_like(rec), rec)

 f1 = 2 * (prec * rec) / (prec + rec)

 # Convert NaN to Zero
 f1 = tf.where(tf.is_nan(f1), tf.zeros_like(f1), f1)

 return f1

In case anyone runs into the same problem, I found out how to restructure the program so that it worked. Although the documentation makes it sound like I can pass raw ops into the SessionRunArgs, it seems like it requires actual tensors (maybe this is a misreading on my part).
This is pretty easy to accomplish - I just changed the after_create_session code to what's shown below.

def after_create_session(self, session, coord):

 tp_name = 'metrics/f1_macro/TP'
 self.tp = 
 tp_tensor = session.graph.get_tensor_by_name(tp_name+':0')

 self.args = [tp_tensor]

And this successfully runs.