a Sic@sdZddlmZddlmZddlmZddlmZddlmZddlmZddlmZdd lm Z dd lm Z dd lm Z dd l m Z dd l mZddl mZddl mZddl mZddl mZddl mZddl mZddl mZddl mZddl mZddl mZddl mZddl mZddl mZddl mZddl mZddl mZddl mZddl m Z dd l m!Z!dd!l m"Z"dd"l m#Z#dd#l m$Z$dd$l m%Z%dd%l m&Z&dd&l m'Z'dd'l m(Z(dd(l m)Z)dd)l m*Z*dd*l m+Z+dd+l m,Z,dd,l m-Z-dd-l m.Z.dd.l m/Z/dd/l m0Z0dd0l m1Z1dd1l m2Z2dd2l m3Z3dd3l m4Z4dd4l m5Z5dd5l m6Z6dd6l m7Z7dd7l m8Z8dd8l m9Z9dd9l m:Z:dd:l m;Z;dd;l mZ>dd>l m?Z?dd?l m@Z@dd@l mAZAddAl mBZBddBl mCZCddCl mDZDddDl mEZEddEl mFZFddFl mGZGddGl mHZHddHl mIZIddIlJmKZKddJlJmLZLddKlMmNZNe6ZOZPe8ZQZReBZSZTe@ZUZVeAZWZXeCZYZZe?Z[e>> metric = tf.keras.metrics.get("categorical_crossentropy") >>> type(metric) >>> metric = tf.keras.metrics.get("CategoricalCrossentropy") >>> type(metric) You can also specify `config` of the metric to this function by passing dict containing `class_name` and `config` as an identifier. Also note that the `class_name` must map to a `Metric` class >>> identifier = {"class_name": "CategoricalCrossentropy", ... "config": {"from_logits": True}} >>> metric = tf.keras.metrics.get(identifier) >>> type(metric) Args: identifier: A metric identifier. One of None or string name of a metric function/class or metric configuration dictionary or a metric function or a metric class instance Returns: A Keras metric as a `function`/ `Metric` class instance. Raises: ValueError: If `identifier` cannot be interpreted. z'Could not interpret metric identifier: N) isinstancedictrVstrcallable ValueError) identifierrNrNrOgets"   r])N)`__doc__Zkeras.metrics.base_metricrrrrrrrr r r Zkeras.metrics.metricsr r rrrrrrrrrrrrrrrrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2r3r4r5r6r7r8r9r:r;r<r=r>r?r@rArBrCrDrErFrGrHkeras.utils.generic_utilsrIrK tensorflow.python.util.tf_exportrLaccACCbceBCEmseMSEmaeMAEmapeMAPEmsleMSLElog_coshcosine_proximityrPrVr]rNrNrNrOs