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Add an average_winrate_observed metric to tensorboard #896

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Sep 24, 2019
Merged

Add an average_winrate_observed metric to tensorboard #896

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69f819b
add an average_winrate_observed metric to tensorboard
Sep 23, 2019
9103ae5
remove unused num_gpu code
Sep 23, 2019
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9 changes: 6 additions & 3 deletions dual_net.py
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Original file line number Diff line number Diff line change
Expand Up @@ -279,8 +279,9 @@ def model_fn(features, labels, mode, params):
train_op = optimizer.minimize(combined_cost, global_step=global_step)

# Computations to be executed on CPU, outside of the main TPU queues.
def eval_metrics_host_call_fn(policy_output, value_output, pi_tensor, policy_cost,
value_cost, l2_cost, combined_cost, step,
def eval_metrics_host_call_fn(policy_output, value_output, pi_tensor,
value_tensor, policy_cost, value_cost,
l2_cost, combined_cost, step,
est_mode=tf.estimator.ModeKeys.TRAIN):
policy_entropy = -tf.reduce_mean(tf.reduce_sum(
policy_output * tf.log(policy_output), axis=1))
Expand All @@ -299,6 +300,7 @@ def eval_metrics_host_call_fn(policy_output, value_output, pi_tensor, policy_cos
tf.one_hot(policy_target_top_1, tf.shape(policy_output)[1]))

value_cost_normalized = value_cost / params['value_cost_weight']
avg_value_observed = tf.reduce_mean(value_tensor)

with tf.variable_scope('metrics'):
metric_ops = {
Expand All @@ -308,7 +310,7 @@ def eval_metrics_host_call_fn(policy_output, value_output, pi_tensor, policy_cos
'l2_cost': tf.metrics.mean(l2_cost),
'policy_entropy': tf.metrics.mean(policy_entropy),
'combined_cost': tf.metrics.mean(combined_cost),

'avg_value_observed': tf.metrics.mean(avg_value_observed),
'policy_accuracy_top_1': tf.metrics.mean(policy_output_in_top1),
'policy_accuracy_top_3': tf.metrics.mean(policy_output_in_top3),
'policy_top_1_confidence': tf.metrics.mean(policy_top_1_confidence),
Expand Down Expand Up @@ -345,6 +347,7 @@ def eval_metrics_host_call_fn(policy_output, value_output, pi_tensor, policy_cos
policy_output,
value_output,
labels['pi_tensor'],
labels['value_tensor'],
tf.reshape(policy_cost, [1]),
tf.reshape(value_cost, [1]),
tf.reshape(l2_cost, [1]),
Expand Down