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add avg_stones commands #900

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add avg_stones commands #900

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f194b0b
add avg_stones commands
sethtroisi Sep 25, 2019
65f873a
Change avg_stones to histogram
sethtroisi Sep 28, 2019
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27 changes: 21 additions & 6 deletions dual_net.py
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Original file line number Diff line number Diff line change
Expand Up @@ -284,10 +284,13 @@ 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,
value_tensor, policy_cost, value_cost,
l2_cost, combined_cost, step,
est_mode=tf.estimator.ModeKeys.TRAIN):
def eval_metrics_host_call_fn(
avg_stones, avg_stones_delta,
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))
# pi_tensor is one_hot when generated from sgfs (for supervised learning)
Expand Down Expand Up @@ -315,13 +318,16 @@ def eval_metrics_host_call_fn(policy_output, value_output, pi_tensor,
'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),
'value_confidence': tf.metrics.mean(tf.abs(value_output)),

# Metrics about input data
'policy_target_top_1_confidence': tf.metrics.mean(
policy_target_top_1_confidence),
'value_confidence': tf.metrics.mean(tf.abs(value_output)),
'avg_value_observed': tf.metrics.mean(avg_value_observed),
'avg_stones': tf.metrics.mean(tf.reduce_mean(avg_stones)),
}

if est_mode == tf.estimator.ModeKeys.EVAL:
Expand All @@ -339,6 +345,8 @@ def eval_metrics_host_call_fn(policy_output, value_output, pi_tensor,
for metric_name, metric_op in metric_ops.items():
summary.scalar(metric_name, metric_op[1], step=eval_step)

summary.histogram("avg_stones_delta", avg_stones_delta)

# Reset metrics occasionally so that they are mean of recent batches.
reset_op = tf.variables_initializer(tf.local_variables('metrics'))
cond_reset_op = tf.cond(
Expand All @@ -348,7 +356,14 @@ def eval_metrics_host_call_fn(policy_output, value_output, pi_tensor,

return summary.all_summary_ops() + [cond_reset_op]

# compute here to avoid sending all of features to cpu.
avg_stones_black = tf.reduce_sum(features[:,:,:,1], [1,2])
avg_stones_white = tf.reduce_sum(features[:,:,:,0], [1,2])
avg_stones = avg_stones_black + avg_stones_white
avg_stones_delta = avg_stones_black - avg_stones_white

metric_args = [
avg_stones, avg_stones_delta,
policy_output,
value_output,
labels['pi_tensor'],
Expand Down