在尝试获取自定义度量回调以使用Tensorflow时遇到问题。我在下面创建了一个最低限度的工作示例来帮助进行故障排除。我正在运行：
Windows 10
Python 3.6
scikit-learn==0.23.2
pandas==0.25.3
numpy==1.18.5
tensorflow==2.3.0
使用癌症二进制数据集，我试图调用此处显示的自定义指标作为解决方案，但遇到了上述错误，可能是因为我没有正确使用它。
此代码。。。
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.metrics import precision_score, recall_score, f1_score
import pandas as pd
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import Callback
# Get binary classification dataset
data = load_breast_cancer(as_frame=True)
print(data)
df = data['data']
df['target'] = data['target']
# Train Test split
train, test = train_test_split(data, test_size = 0.10, shuffle=False)
# Define features and labels
x_train = train.iloc[:, :-1]
y_train = train.iloc[:, -1]
x_test = test.iloc[:, :-1]
y_test = test.iloc[:, -1]
# https://github.com/keras-team/keras/issues/10472#issuecomment-472543538
class Metrics(Callback):
def __init__(self, val_data, batch_size=20):
super().__init__()
self.validation_data = val_data
self.batch_size = batch_size
def on_train_begin(self, logs={}):
# print(self.validation_data)
self.val_f1s = []
self.val_recalls = []
self.val_precisions = []
def on_epoch_end(self, epoch, logs={}):
batches = len(self.validation_data)
total = batches * self.batch_size
val_pred = np.zeros((total,1))
val_true = np.zeros((total))
for batch in range(batches):
xVal, yVal = next(self.validation_data)
val_pred[batch * self.batch_size : (batch+1) * self.batch_size] = np.asarray(self.model.predict(xVal)).round()
val_true[batch * self.batch_size : (batch+1) * self.batch_size] = yVal
val_pred = np.squeeze(val_pred)
_val_f1 = f1_score(val_true, val_pred)
_val_precision = precision_score(val_true, val_pred)
_val_recall = recall_score(val_true, val_pred)
self.val_f1s.append(_val_f1)
self.val_recalls.append(_val_recall)
self.val_precisions.append(_val_precision)
return
# Define a function that creates a basic model
def make_deep_learning_classifier():
model = Sequential()
model.add(Dense(64, activation='relu', input_dim=x_train.shape[1], kernel_initializer='normal'))
model.add(Dense(32, activation='relu', input_dim=x_train.shape[1], kernel_initializer='normal'))
model.add(Dense(1, kernel_initializer='normal', activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer=Adam(), metrics=['accuracy'])
return model
# Get our model
model = make_deep_learning_classifier()
print(model.summary())
# Define some params
batch_size = 32
# Call our custom callback
callback = [Metrics(val_data=[x_test, y_test], batch_size=batch_size)] # < Issue here?
# Start training
model.fit(x_train, y_train, epochs=1000, batch_size=batch_size, verbose=1, callbacks=callback, validation_data=(x_test, y_test))
print(Metrics.val_precisions) # < Issue here?
生成此回溯。。。
File "test.py", line 91, in <module>
model.fit(x_train, y_train, epochs=1000, batch_size=batch_size, verbose=1, callbacks=callback, validation_data=(x_test, y_test))
File "C:Users...AppDataLocalProgramsPythonPython36libsite-packagestensorflowpythonkerasenginetraining.py", line 108, in _method_wrapper
return method(self, *args, **kwargs)
File "C:Users...AppDataLocalProgramsPythonPython36libsite-packagestensorflowpythonkerasenginetraining.py", line 1137, in fit
callbacks.on_epoch_end(epoch, epoch_logs)
File "C:Users...AppDataLocalProgramsPythonPython36libsite-packagestensorflowpythonkerascallbacks.py", line 416, in on_epoch_end
callback.on_epoch_end(epoch, numpy_logs)
File "test.py", line 54, in on_epoch_end
xVal, yVal = next(self.validation_data)
TypeError: 'list' object is not an iterator
当我在callback变量中将val_data=[x_test, y_test]更改为val_data=(x_test, y_test)时，我得到。。。
TypeError: 'tuple' object is not an iterator
提出这个回调解决方案的用户提到了一些关于生成器的内容，但我不确定它们是如何工作的。只是想为Tensorflow/Keras定义我自己的自定义度量。我不会使用这个确切的回调，但一旦我运行了这个回调，我就可以将其修改为自己的回调。只是在GitHub的帖子中提供了一个似乎有效的例子，我希望有人能够指出我做错了什么。
谢谢！
更新
使用下面的解决方案，我尝试使用在val_data上正确调用迭代器函数
iter_val_data = iter(self.validation_data)
for batch in range(batches):
xVal, yVal = next(iter_val_data)
但后来我得到了太多的值来解包错误，所以我把它改为：
iter_val_data = iter(self.validation_data)
for batch in range(batches):
xVal = next(iter_val_data)
yVal = next(iter_val_data)
然后我得到错误：
Traceback (most recent call last):
File "test.py", line 89, in <module>
model.fit(x_train, y_train, epochs=1000, batch_size=batch_size, verbose=1, callbacks=callback, validation_data=(x_test, y_test))
File "C:Users...AppDataLocalProgramsPythonPython36libsite-packagestensorflowpythonkerasenginetraining.py", line 108, in _method_wrapper
return method(self, *args, **kwargs)
File "C:Users...AppDataLocalProgramsPythonPython36libsite-packagestensorflowpythonkerasenginetraining.py", line 1137, in fit
callbacks.on_epoch_end(epoch, epoch_logs)
File "C:Users...AppDataLocalProgramsPythonPython36libsite-packagestensorflowpythonkerascallbacks.py", line 416, in on_epoch_end
callback.on_epoch_end(epoch, numpy_logs)
File "test.py", line 53, in on_epoch_end
val_pred[batch * self.batch_size : (batch+1) * self.batch_size] = np.asarray(self.model.predict(xVal)).round()
ValueError: could not broadcast input array from shape (57,1) into shape (32,1)
来自这里的想法？如果可以的话，请尝试在与上述相同的环境中运行代码。谢谢
正如您在这里看到的以及您的错误消息所述，您需要将next((与迭代器一起使用。你在列表中调用next()，next()应该如何知道下一个元素是什么？为此，您需要一个迭代器来保存该状态。所以这应该可以解决你的问题：
iter_val_data = iter(self.validation_data)
for batch in range(batches):
xVal, yVal = next(iter_val_data)