Keras：使用model.train_on_batch（）和model.fit（）获得不同的精度。可能是什么原因以及如何解决？

debugcn 发表于 Dev

乌迈尔Javaid

我有两个具有相同参数的相同模型。这两个都是在MNIST数据集上训练的。第一个使用model.fit（）进行训练，第二个使用model.train_on_batch（）进行训练。第二种模型给出的准确性较低。我想知道这可能是什么原因以及如何解决？

数据准备：

batch_size = 150
num_classes = 10
epochs = 12

# input image dimensions
img_rows, img_cols = 28, 28

# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()

if K.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

模型1：

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
                 activation='relu',
                 input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(Conv2D(256, (3, 3), activation='relu'))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adadelta(),
              metrics=['accuracy'])

model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

模型1准确性：

测试损失：0.023489486496470636测试准确度：0.9924

模式2：

model2 = Sequential()
model2.add(Conv2D(32, kernel_size=(3, 3),
                 activation='relu',
                 input_shape=input_shape))
model2.add(Conv2D(64, (3, 3), activation='relu'))
model2.add(Conv2D(128, (3, 3), activation='relu'))
model2.add(Conv2D(256, (3, 3), activation='relu'))
model2.add(Conv2D(128, (3, 3), activation='relu'))
model2.add(Conv2D(64, (3, 3), activation='relu'))
model2.add(Conv2D(64, (3, 3), activation='relu'))
model2.add(Conv2D(32, (3, 3), activation='relu'))
model2.add(MaxPooling2D(pool_size=(2, 2)))
model2.add(Dropout(0.25))
model2.add(Flatten())
model2.add(Dense(128, activation='relu'))
model2.add(Dropout(0.5))
model2.add(Dense(num_classes, activation='softmax'))

model2.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adadelta(),
              metrics=['accuracy'])

batch_size2 = 150
epochs2 = 12
step_epoch = x_train.shape[0] // batch_size2

def next_batch_train(i):
  return x_train[i:i+batch_size2,:,:,:], y_train[i:i+batch_size2,:]

iter_num = 0
epoch_num = 0
model_outputs = []
loss_history  = []

while epoch_num < epochs2:
  while iter_num < step_epoch:
    x,y = next_batch_train(iter_num)
    loss_history += model2.train_on_batch(x,y)

    iter_num += 1

  print("EPOCH {} FINISHED".format(epoch_num + 1))
  epoch_num += 1
  iter_num = 0 # reset counter


score = model2.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

模型2的准确性：

测试损失：0.5577236003954947测试准确度：0.9387

霸王龙

差异的四个来源：

fit()shuffle=True默认使用，这包括第一个时期（及随后的时期）
您不使用随机种子；在这里看到我的答案
您有step_epoch批次数量，但是要遍历step_epoch - 1；更改<为<=
您的next_batch_train切片已经完成；这是它在做什么与需要做的事情：
- x_train[0:128] --> x_train[1:129] --> x_train[2:130] --> ...
- x_train[0:128] --> x_train[128:256] --> x_train[256:384] --> ...

为了解决这个问题，您应该在model2火车循环中包含一个改组步骤-或fit与shuffle=False（不推荐）一起使用。另外，提示：64, 128, 256, 128, 64Conv2D过滤器的布置非常糟糕；从某种意义上讲，您正在做的事情是大幅上采样，即“虚构数据”-如果您要使用更多的过滤器，则还应按strides比例增加它们，以便各层之间的总张量大小保持相同（或更小）。

下面提到的所有修复程序和更新的种子功能；将其运行1个时间段，需要12个时间太长-如果1个可以工作，则将需要12个时间。可以保留您的原始模型，但是我建议您在下面的模型中进行测试，因为它的速度明显更快。

import tensorflow as tf
import numpy as np
import random

def reset_seeds():
    np.random.seed(1)
    random.seed(2)
    if tf.__version__[0] == '2':
        tf.random.set_seed(3)
    else:
        tf.set_random_seed(3)
    print("RANDOM SEEDS RESET")

reset_seeds()
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
                 activation='relu',
                 input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adadelta(),
              metrics=['accuracy'])

def next_batch_train(i):
  return (x_train[i*batch_size2:(i+1)*batch_size2,:,:,:], 
          y_train[i*batch_size2:(i+1)*batch_size2,:])

iter_num = 0
epoch_num = 0
model_outputs = []
loss_history  = []

while epoch_num < epochs2:
  while iter_num < step_epoch:
    x,y = next_batch_train(iter_num)
    loss_history += model2.train_on_batch(x,y)

    iter_num += 1

  print("EPOCH {} FINISHED".format(epoch_num + 1))
  epoch_num += 1
  iter_num = 0 # reset counter

score = model2.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

更好的选择：使用改组

class TrainBatches():
    def __init__(self, x_train, y_train, batch_size):
        self.x_train=x_train
        self.y_train=y_train
        self.batch_size=batch_size

        self.indices = [i for i in range(len(x_train))]
        self.counter = 0

    def get_next(self):
        start = self.indices[self.counter] * self.batch_size
        end   = start + self.batch_size
        self.counter += 1
        return self.x_train[start:end], self.y_train[start:end]

    def shuffle(self):
        np.random.shuffle(self.indices)
        print("BATCHES SHUFFLED")

train_batches = TrainBatches(x_train, y_train, batch_size)

while epoch_num < epochs2:
  while iter_num <= step_epoch:
    x, y = train_batches.get_next()
    loss_history += model2.train_on_batch(x,y)

    iter_num += 1

  train_batches.shuffle()
  train_batches.counter = 0
  print("EPOCH {} FINISHED".format(epoch_num + 1))
  epoch_num += 1
  iter_num = 0 # reset counter

请注意，这不能保证您的结果会与一致fit()，因为fit()可能会以不同的方式（甚至使用随机种子）随机播放-但实际上实现是正确的。上面的内容在第一个时期也不会洗牌（易于更改）。

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编辑于2021-04-1

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