00c8b2f2e51117a91bcb52170fbcd98e6ef145e5,INCEPTION/image_retraining/retrain.py,,variable_summaries,#Any#Any#,699

Before Change

    tf.scalar_summary("sttdev/" + name, stddev)
    tf.scalar_summary("max/" + name, tf.reduce_max(var))
    tf.scalar_summary("min/" + name, tf.reduce_min(var))
    tf.histogram_summary(name, var)


def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor):
  Adds a new softmax and fully-connected layer for training.

After Change

    scalar_summary("sttdev/" + name, stddev)
    scalar_summary("max/" + name, tf.reduce_max(var))
    scalar_summary("min/" + name, tf.reduce_min(var))
    if(int(tf.__version__.split(".")[0])<1): ////// For tf v<1.0
      tf.histogram_summary(name, var)
    else: ////// For tf v>=1.0 
      tf.summary.histogram(name, var) 


def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor):
  Adds a new softmax and fully-connected layer for training.

  We need to retrain the top layer to identify our new classes, so this function

Instances