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

问题描述

我是Tensorflow领域的新手，并且正在研究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，准确性，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)

由于我仅获得准确性和损失，因此如何获得其他指标? 预先谢谢您，如果是一个简单的问题，或者如果某个地方已经回答了我，我们将感到抱歉.

解决方案

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(从2020年3月22日开始)的最简洁的方法.

您需要做的第一件事是创建一个自定义回调，在其中发送测试数据:

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)              
           

在您的主目录中，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)

         

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)

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.

解决方案

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).

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)              
           

In your main, where you load your dataset and add the callbacks:

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)

         

这篇关于如何在Tensorflow 2.0中获得其他指标(不仅准确性)?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！