1.net_work.py
line 221: summaries = tf.get_collection('%smutiloss' % scope, scope)
line 239: summaries.append(tf.summary.histogram(var.op.name + '/gradients', grad))
line 246: summaries.append(tf.summary.histogram(var.op.name, var))
u just made a summaries list, but didn't use it later. Also, what's the meaning of:'%smutiloss' % scope, does there have var named '%smutiloss' % scope?(I guess you originally wanted to use：for var in summaries: do tf.summary.histogram() && self.summaries.append()??)
2.net_work.py
line 254: train_op = tf.group(apply_gradient_op, variables_averages_op, *bn_update_ops)
What is the specific execution order？ because in my mind, the bn_update_ops should executed before apply_gradient_op(for example: use the with tf.control_dependencies...), i don't know the specific mechanism.
1.net_work.py line 221: summaries = tf.get_collection('%smutiloss' % scope, scope) line 239: summaries.append(tf.summary.histogram(var.op.name + '/gradients', grad)) line 246: summaries.append(tf.summary.histogram(var.op.name, var))
u just made a summaries list, but didn't use it later. Also, what's the meaning of:'%smutiloss' % scope, does there have var named '%smutiloss' % scope?(I guess you originally wanted to use：for var in summaries: do tf.summary.histogram() && self.summaries.append()??)
2.net_work.py line 254: train_op = tf.group(apply_gradient_op, variables_averages_op, *bn_update_ops)
What is the specific execution order？ because in my mind, the bn_update_ops should executed before apply_gradient_op(for example: use the with tf.control_dependencies...), i don't know the specific mechanism.
Thanks!