AVhn&dZddlmZddlmZddlmZddlmZddlmZddlm Z ddlm Z dd lm Z dd lm Z dd l mZdd lmZddZdZej&dZedgej*j,fdZedgd ej*j,fdZedgddZedgddZedg ddZy ) z0Utilities for manipulating the loss collections.)context) constant_op)dtypes)ops) array_ops) check_opscond)confusion_matrix)math_ops) tf_contextlib) tf_exportNc j}|j}ىj}|j}|,|*||z dk7s|ddk(rtj\nt j t j z fdt jdt jd fd}tjt jd|\fSj}|j} | dk(rfS|F| D| |z dk(rt jdgn|| z dk(rt jdgfSt j } | t j z fdfd fd} tjt j| dfd | fS) aSqueeze or expand last dimension if needed. 1. Squeezes last dim of `y_pred` or `y_true` if their rank differs by 1 (using `confusion_matrix.remove_squeezable_dimensions`). 2. Squeezes or expands last dim of `sample_weight` if its rank differs by 1 from the new rank of `y_pred`. If `sample_weight` is scalar, it is kept scalar. This will use static shape if available. Otherwise, it will add graph operations, which could result in a performance hit. Args: y_pred: Predicted values, a `Tensor` of arbitrary dimensions. y_true: Optional label `Tensor` whose dimensions match `y_pred`. sample_weight: Optional weight scalar or `Tensor` whose dimensions match `y_pred`. Returns: Tuple of `y_pred`, `y_true` and `sample_weight`. Each of them possibly has the last dimension squeezed, `sample_weight` could be extended by one dimension. 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Returns: a list of loss tensors. )rget_collection)scoperTs r get_lossesrYs  OU 33rz losses.get_regularization_lossesc^tjtjj|S)zGets the list of regularization losses. Args: scope: An optional scope name for filtering the losses to return. Returns: A list of regularization losses as Tensors. )rrW GraphKeysREGULARIZATION_LOSSESrXs rget_regularization_lossesr^s!  CMM?? GGrzlosses.get_regularization_lossctt|}|rtj||StjdS)zGets the total regularization loss. Args: scope: An optional scope name for filtering the losses to return. name: The name of the returned tensor. Returns: A scalar regularization loss. nameg)r^r add_nrconstant)rXrarBs rget_regularization_lossrds3 %U +& >>&t ,,    $$rzlosses.get_total_losscjt|}|r|t|z }tj||S)a~Returns a tensor whose value represents the total loss. In particular, this adds any losses you have added with `tf.add_loss()` to any regularization losses that have been added by regularization parameters on layers constructors e.g. `tf.layers`. Be very sure to use this if you are constructing a loss_op manually. Otherwise regularization arguments on `tf.layers` methods will not function. Args: add_regularization_losses: A boolean indicating whether or not to use the regularization losses in the sum. name: The name of the returned tensor. scope: An optional scope name for filtering the losses to return. Note that this filters the losses added with `tf.add_loss()` as well as the regularization losses to that scope. Returns: A `Tensor` whose value represents the total loss. Raises: ValueError: if `losses` is not iterable. r]r`)rYr^r rb)add_regularization_lossesrarXrBs rget_total_lossrgs24 E "& 'e44F T **r)NNr)Ntotal_regularization_loss)T total_lossN)__doc__tensorflow.python.eagerrtensorflow.python.frameworkrrrtensorflow.python.opsrrr r r tensorflow.python.utilr tensorflow.python.util.tf_exportrr=rDcontextmanagerrNr[LOSSESrUrYr^rdrgrrrrrs 7+3.+++&2*06V'r20  <  !"#&==#7#7 1# 1 "#$3==+?+? 4% 4 123 H4 H /01%2%" &'(-1$+)+r