时间戳

首页 > 解决方案 > 当准确率达到大于 0.99 时停止训练模型

python - 当准确率达到大于 0.99 时停止训练模型

问题描述

我想阻止我的模型在达到某个阈值后接受训练。我已经为 Tensorflow 的回调编写了一个类。我正在训练 MNIST 数据集。对手写数字进行分类和识别。但由于某种原因，训练并没有停止。我找不到理由。这是我的代码。

import tensorflow as tf

class myCallback(tf.keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs={}):
    if(logs.get('accuracy')>0.99):
      print("\nReached 99% accuracy so cancelling training!")
      self.model.stop_training = True

mnist = tf.keras.datasets.mnist

(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

callbacks = myCallback()

model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(512, activation=tf.nn.relu),
  tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(x_train, y_train, epochs=10, callbacks=[callbacks])

标签： pythontensorflowmachine-learningkeras

解决方案

试试这个

class StopOnPoint(tf.keras.callbacks.Callback):
    def __init__(self, point):
        super(StopOnPoint, self).__init__()
        self.point = point

    def on_epoch_end(self, epoch, logs=None): 
        accuracy = logs["accuracy"]
        if accuracy >= self.point:
            self.model.stop_training = True

callbacks = [StopOnPoint(0.98)] # <- set optimal point

推荐阅读