Python 如何使用'；召回'；keras分类器中的as度量？

如何在keras分类器中使用“召回”和其他指标。下面的代码仅适用于准确性，但如果我更改度量以调用它，它将失败

版本 MWE

将numpy导入为np
作为pd进口熊猫
导入sklearn
导入tensorflow作为tf
进口干酪
从keras.models导入顺序
从keras.layers.core导入
种子=100
np.随机种子（100）
X=np.random.random（（20,3））
y=np.random.randint（0,2，大小=20）
印刷品（x形，y形）#（20，3）（20，）
n_输入=X.shape[1]
模型=顺序([
密集（n_输入，输入_形状=（n_输入，），激活='relu'），
稠密（32，活化='relu'），
密集型（2，激活='softmax'）
])
指标=‘召回’#召回失败
#公制=‘精度’#精度有效
model.compile（'adam'，loss='binary\u crossentropy'，metrics=[METRIC]）
模型拟合（X，y，验证分割=0.2，历元=1）

错误

---------------------------------------------------------------------------
ValueError回溯（最近一次调用上次）
在里面
38个时代=10，
39 shuffle=True，
--->40详细=5）
方法包装中的~/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py（self，*args，**kwargs）
106定义方法包装（self，*args，**kwargs）：
107如果不是self._处于_multi_worker_模式（）：#pylint:disable=受保护的访问
-->108返回方法（self、*args、**kwargs）
109
110#已经在“运行分配协调器”内部运行了。
~/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in-fit（self、x、y、批大小、历元、冗余、回调、验证拆分、验证数据、洗牌、类权重、样本权重、初始历元、每个历元的步骤、验证步骤、验证批次大小、验证频率、最大队列大小、工人、使用多处理）
1096批次大小=批次大小）：
1097回拨。列车上批次开始（步骤）
->1098 tmp_日志=训练函数（迭代器）
1099如果数据处理程序应同步：
1100 context.async_wait（）
~/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in_uuu调用（self，*args，**kwds）
778其他：
779 compiler=“nonXla”
-->780结果=自身调用（*args，**kwds）
781
782 new_tracing_count=self._get_tracing_count（）
~/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in_调用（self，*args，**kwds）
821#这是"调用"的第一个调用，因此我们必须初始化。
822个初始值设定项=[]
-->823自身初始化（参数、KWD、添加初始化器到=初始化器）
824最后：
825#此时我们知道初始化已完成（或更少）
~/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in_initialize（self、args、kwds、add_initializer_to）
695自具体状态fn=(
696 self._stateful_fn._get_concrete_function_internal_garbage_collected（#pylint:disable=protected access
-->697*args，**科威特第纳尔）
698
699 def无效的创建者范围（*未使用的参数，**未使用的参数）：
~/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/function.py in\u get\u concrete\u function\u internal\u garbage\u collected（self，*args，**kwargs）
2853 args，kwargs=None，None
2854带自锁：
->2855图形函数，可能定义函数（args，kwargs）
2856返回图函数
2857
函数（self、args、kwargs）中的~/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/function.py
3211
3212自.\u函数\u缓存.missed.add（调用上下文\u键）
->3213图形函数=self.\u创建图形函数（args，kwargs）
3214自.\u函数\u缓存.primary[缓存\u键]=图形\u函数
3215返回图_函数，args，kwargs
创建图形函数中的~/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/function.py（self、args、kwargs、override\u flat\u arg\u形状）
3073参数名称=参数名称，
3074覆盖平面形状=覆盖平面形状，
->3075按值捕获=自身。_按值捕获），
3076自我功能属性，
3077功能规格=自身功能规格，
~/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/framework/func\u graph.py从func\u py\u func（名称、python\u func、args、kwargs、签名、func\u图、自动签名、自动签名选项、添加控制依赖项、arg\u名称、op\u返回值、集合、按值捕获、覆盖平面arg\u形状）
984，original_func=tf_decorator.unwrap（python_func）
985
-->986 func_输出=python_func（*func_参数，**func_参数）
987
988#不变量：`func_outputs`只包含张量、复合传感器、，
包装中的~/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py（*args，**kwds）
598#uuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuu。我们给予
599#函数对自身进行弱引用以避免引用循环。
-->600返回弱_-wrapped_-fn（）
601弱包层=弱包层参考（包层）
602
包装器中的~/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py（*args，**kwargs）
971例外情况为e:#pylint:disable=broad except
972如果hasattr（e，“AGU错误元数据”）：
-->973将e.ag\u错误\u元数据引发到\u异常（e）
974其他：
975提高
ValueError:在用户代码中：
/Users/poudel/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tens
"""
[('numpy', '1.19.1'),
 ('pandas', '1.1.1'),
 ('sklearn', '0.23.2'),
 ('tensorflow', '2.3.0'),
 ('keras', '2.4.3')]

"""

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-17-b372311b0ed4> in <module>
     38             epochs=10,
     39             shuffle=True,
---> 40             verbose=5)

~/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
    106   def _method_wrapper(self, *args, **kwargs):
    107     if not self._in_multi_worker_mode():  # pylint: disable=protected-access
--> 108       return method(self, *args, **kwargs)
    109 
    110     # Running inside `run_distribute_coordinator` already.

~/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
   1096                 batch_size=batch_size):
   1097               callbacks.on_train_batch_begin(step)
-> 1098               tmp_logs = train_function(iterator)
   1099               if data_handler.should_sync:
   1100                 context.async_wait()

~/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
    778       else:
    779         compiler = "nonXla"
--> 780         result = self._call(*args, **kwds)
    781 
    782       new_tracing_count = self._get_tracing_count()

~/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
    821       # This is the first call of __call__, so we have to initialize.
    822       initializers = []
--> 823       self._initialize(args, kwds, add_initializers_to=initializers)
    824     finally:
    825       # At this point we know that the initialization is complete (or less

~/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
    695     self._concrete_stateful_fn = (
    696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
--> 697             *args, **kwds))
    698 
    699     def invalid_creator_scope(*unused_args, **unused_kwds):

~/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
   2853       args, kwargs = None, None
   2854     with self._lock:
-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)
   2856     return graph_function
   2857 

~/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
   3211 
   3212       self._function_cache.missed.add(call_context_key)
-> 3213       graph_function = self._create_graph_function(args, kwargs)
   3214       self._function_cache.primary[cache_key] = graph_function
   3215       return graph_function, args, kwargs

~/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
   3073             arg_names=arg_names,
   3074             override_flat_arg_shapes=override_flat_arg_shapes,
-> 3075             capture_by_value=self._capture_by_value),
   3076         self._function_attributes,
   3077         function_spec=self.function_spec,

~/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
    984         _, original_func = tf_decorator.unwrap(python_func)
    985 
--> 986       func_outputs = python_func(*func_args, **func_kwargs)
    987 
    988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

~/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
    598         # __wrapped__ allows AutoGraph to swap in a converted function. We give
    599         # the function a weak reference to itself to avoid a reference cycle.
--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)
    601     weak_wrapped_fn = weakref.ref(wrapped_fn)
    602 

~/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
    971           except Exception as e:  # pylint:disable=broad-except
    972             if hasattr(e, "ag_error_metadata"):
--> 973               raise e.ag_error_metadata.to_exception(e)
    974             else:
    975               raise

ValueError: in user code:

    /Users/poudel/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:806 train_function  *
        return step_function(self, iterator)
    /Users/poudel/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:796 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    /Users/poudel/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:1211 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /Users/poudel/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /Users/poudel/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica
        return fn(*args, **kwargs)
    /Users/poudel/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:789 run_step  **
        outputs = model.train_step(data)
    /Users/poudel/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:759 train_step
        self.compiled_metrics.update_state(y, y_pred, sample_weight)
    /Users/poudel/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/keras/engine/compile_utils.py:409 update_state
        metric_obj.update_state(y_t, y_p, sample_weight=mask)
    /Users/poudel/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/keras/utils/metrics_utils.py:90 decorated
        update_op = update_state_fn(*args, **kwargs)
    /Users/poudel/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/keras/metrics.py:176 update_state_fn
        return ag_update_state(*args, **kwargs)
    /Users/poudel/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/keras/metrics.py:1410 update_state  **
        sample_weight=sample_weight)
    /Users/poudel/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/keras/utils/metrics_utils.py:353 update_confusion_matrix_variables
        y_pred.shape.assert_is_compatible_with(y_true.shape)
    /Users/poudel/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/framework/tensor_shape.py:1134 assert_is_compatible_with
        raise ValueError("Shapes %s and %s are incompatible" % (self, other))

    ValueError: Shapes (None, 2) and (None, 1) are incompatible

import numpy as np
import pandas as pd
import sklearn
import tensorflow as tf
import keras
from keras.models import Sequential
from keras.layers.core import Dense

SEED = 100


np.random.seed(100)
X = np.random.random((20, 3))
y = np.random.randint(0,2,size=20)

print(X.shape, y.shape) # (20, 3) (20,)

n_inputs = X.shape[1]

model = Sequential([
    Dense(n_inputs, input_shape=(n_inputs, ), activation='relu'),
    Dense(32, activation='relu'),
    Dense(1, activation='relu')
])


METRIC = 'Recall' # Recall fails
# METRIC = 'accuracy' # accuracy works


model.compile('adam',loss='binary_crossentropy',metrics=[METRIC])
model.fit(X, y,validation_split=0.2,epochs=1)