如何在 Tensorflow 2.0 中获得其他指标(不仅仅是准确性)? [英] How to get other metrics in Tensorflow 2.0 (not only accuracy)?

问题描述

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:

I'm new in the world of Tensorflow and I'm working on the simple example of mnist dataset classification. I would like to know how can I obtain other metrics (e.g precision, recall etc) in addition to accuracy and loss (and possibly to show them). Here's my code:

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)

由于我只得到准确度和损失，我如何才能得到其他指标?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.

Since I get only accuracy and loss, how can i get other metrics? Thank you in advance, I'm sorry if it is a simple question or If was already answered somewhere.

推荐答案

我添加了另一个答案，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).

I am adding another answer because this is the cleanest way in order to compute these metrics correctly on your test set (as of 22nd of March 2020).

>

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:

The first thing you need to do is to create a custom callback, in which you send your test data:

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)

         

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