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python - 如何在 Tensorflow 2.0 中获得其他指标(不仅仅是准确性)?

转载 作者:行者123 更新时间:2023-12-04 01:32:38 24 4
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我是 Tensorflow 世界的新手,我正在研究 mnist 数据集分类的简单示例。我想知道除了准确性和损失(并可能显示它们)之外,我如何获得其他指标(例如精度、召回率等)。这是我的代码:

from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.callbacks import TensorBoard
import os

#load mnist dataset
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()

#create and compile the model
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation='softmax')
])
model.summary()

model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])

#model checkpoint (only if there is an improvement)

checkpoint_path = "logs/weights-improvement-{epoch:02d}-{accuracy:.2f}.hdf5"

cp_callback = ModelCheckpoint(checkpoint_path, monitor='accuracy',save_best_only=True,verbose=1, mode='max')

#Tensorboard
NAME = "tensorboard_{}".format(int(time.time())) #name of the model with timestamp
tensorboard = TensorBoard(log_dir="logs/{}".format(NAME))

#train the model
model.fit(x_train, y_train, callbacks = [cp_callback, tensorboard], epochs=5)

#evaluate the model
model.evaluate(x_test, y_test, verbose=2)

由于我只获得准确性和损失,我如何获得其他指标?
预先感谢您,如果这是一个简单的问题,或者如果已经在某处回答,我很抱歉。

最佳答案

我要添加另一个答案,因为这是在测试集上正确计算这些指标的最简洁方法(截至 2020 年 3 月 22 日)。
您需要做的第一件事是创建一个自定义回调,在其中发送您的测试数据:

import tensorflow as tf
from tensorflow.keras.callbacks import Callback
from sklearn.metrics import classification_report

class MetricsCallback(Callback):
def __init__(self, test_data, y_true):
# Should be the label encoding of your classes
self.y_true = y_true
self.test_data = test_data

def on_epoch_end(self, epoch, logs=None):
# Here we get the probabilities
y_pred = self.model.predict(self.test_data))
# Here we get the actual classes
y_pred = tf.argmax(y_pred,axis=1)
# Actual dictionary
report_dictionary = classification_report(self.y_true, y_pred, output_dict = True)
# Only printing the report
print(classification_report(self.y_true,y_pred,output_dict=False)

在 main 中,加载数据集并添加回调:
metrics_callback = MetricsCallback(test_data = my_test_data, y_true = my_y_true)
...
...
#train the model
model.fit(x_train, y_train, callbacks = [cp_callback, metrics_callback,tensorboard], epochs=5)


关于python - 如何在 Tensorflow 2.0 中获得其他指标(不仅仅是准确性)?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/60616842/

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