Почему мой обратный вызов не вызывается в Tensorflow?

Question

Ниже приведен мой код Tensorflow и Python, который завершит обучение, когда точность в 99% с функцией обратного вызова. Но обратный вызов не вызывает. Где проблема?

def train_mnist():

    class myCallback(tf.keras.callbacks.Callback):
        def on_epoc_end(self, epoch,logs={}):
            if (logs.get('accuracy')>0.99):
                print("Reached 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(path=path)

    x_train= x_train/255.0
    x_test= x_test/255.0
    callbacks=myCallback()

    model = tf.keras.models.Sequential([
        # YOUR CODE SHOULD START HERE
        tf.keras.layers.Flatten(input_shape=(28, 28)),
        tf.keras.layers.Dense(256, activation=tf.nn.relu),
        tf.keras.layers.Dense(10, activation=tf.nn.softmax)

    ])

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

    # model fitting
    history = model.fit(x_train,y_train, epochs=10,callbacks=[callbacks]) 
    # model fitting
    return history.epoch, history.history['acc'][-1]

Answer 1

Вы пишете орфографическую эпоху, а также вы должны вернуть accuracy не acc.

from tensorflow.keras.layers import Input, Dense, Add, Activation, Flatten
from tensorflow.keras.models import Model, Sequential
import tensorflow as tf
import numpy as np
import random


from tensorflow.python.keras.layers import Input, GaussianNoise, BatchNormalization

def train_mnist():
  class myCallback(tf.keras.callbacks.Callback):
      def on_epoch_end(self, epoch,logs={}):
          print(logs.get('accuracy'))
          if (logs.get('accuracy')>0.9):
              print("Reached 90% 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_train/255.0
  x_test= x_test/255.0

  callbacks=myCallback()

  model = tf.keras.models.Sequential([
      # YOUR CODE SHOULD START HERE
      tf.keras.layers.Flatten(input_shape=(28, 28)),
      tf.keras.layers.Dense(256, activation=tf.nn.relu),
      tf.keras.layers.Dense(10, activation=tf.nn.softmax)

  ])

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

  # model fitting
  history = model.fit(x_train,y_train, epochs=10,callbacks=[callbacks]) 
  # model fitting
  return history.epoch, history.history['accuracy'][-1]

train_mnist()

Epoch 1/10
1859/1875 [============================>.] - ETA: 0s - loss: 0.2273 - accuracy: 0.93580.93586665391922
Reached 90% accuracy so cancelling training!
1875/1875 [==============================] - 3s 2ms/step - loss: 0.2265 - accuracy: 0.9359

([0], 0.93586665391922)