验证精度始终高于Keras的训练精度

我正在尝试使用mnist数据集训练一个简单的神经网络.出于某种原因,当我得到历史记录(从model.fit返回的参数)时,验证准确性高于训练准确度,这真的很奇怪,但如果我在评估模型时检查分数,我会得到更高的训练精度高于测试精度.

无论模型的参数如何,每次都会发生这种情况.此外,如果我使用自定义回调并访问参数'acc'和'val_acc',我会发现同样的问题(数字与历史记录中返回的数字相同).

请帮我!我究竟做错了什么？为什么验证准确度高于训练准确度(您可以看到我在查看损失时遇到同样的问题).

这是我的代码:

#!/usr/bin/env python3.5

from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
import numpy as np
from keras import backend
from keras.utils import np_utils
from keras import losses
from keras import optimizers
from keras.datasets import mnist
from keras.models import Sequential
from matplotlib import pyplot as plt

# get train and test data (minst) and reduce volume to speed up (for testing)
(x_train, y_train), (x_test, y_test) = mnist.load_data()
data_reduction = 20
x_train = x_train[:x_train.shape[0] // data_reduction]
y_train = y_train[:y_train.shape[0] // data_reduction]
x_test = x_test[:x_test.shape[0] // data_reduction]
y_test = y_test[:y_test.shape[0] // data_reduction]
try:
    IMG_DEPTH = x_train.shape[3]
except IndexError:
    IMG_DEPTH = 1  # B/W
labels = np.unique(y_train)
N_LABELS = len(labels)
# reshape input data
if backend.image_data_format() == 'channels_first':
    X_train = x_train.reshape(x_train.shape[0], IMG_DEPTH, x_train.shape[1], x_train.shape[2])
    X_test = x_test.reshape(x_test.shape[0], IMG_DEPTH, x_train.shape[1], x_train.shape[2])
    input_shape = (IMG_DEPTH, x_train.shape[1], x_train.shape[2])
else:
    X_train = x_train.reshape(x_train.shape[0], x_train.shape[1], x_train.shape[2], IMG_DEPTH)
    X_test = x_test.reshape(x_test.shape[0], x_train.shape[1], x_train.shape[2], IMG_DEPTH)
    input_shape = (x_train.shape[1], x_train.shape[2], IMG_DEPTH)
# convert data type to float32 and normalize data values to range [0, 1]
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
# reshape input labels
Y_train = np_utils.to_categorical(y_train, N_LABELS)
Y_test = np_utils.to_categorical(y_test, N_LABELS)

# create model
opt = optimizers.Adam()
loss = losses.categorical_crossentropy
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape))
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(len(labels), activation='softmax'))
model.compile(optimizer=optimizers.Adam(), loss=losses.categorical_crossentropy, metrics=['accuracy'])
# fit model
history = model.fit(X_train, Y_train, batch_size=64, epochs=50, verbose=True,
                    validation_data=(X_test, Y_test))
# evaluate model
train_score = model.evaluate(X_train, Y_train, verbose=True)
test_score = model.evaluate(X_test, Y_test, verbose=True)

print("Validation:", test_score[1])
print("Training:  ", train_score[1])
print("--------------------")
print("First 5 samples validation:", history.history["val_acc"][0:5])
print("First 5 samples training:", history.history["acc"][0:5])
print("--------------------")
print("Last 5 samples validation:", history.history["val_acc"][-5:])
print("Last 5 samples training:", history.history["acc"][-5:])

# plot history
plt.ion()
fig = plt.figure()
subfig = fig.add_subplot(122)
subfig.plot(history.history['acc'], label="training")
if history.history['val_acc'] is not None:
    subfig.plot(history.history['val_acc'], label="validation")
subfig.set_title('Model Accuracy')
subfig.set_xlabel('Epoch')
subfig.legend(loc='upper left')
subfig = fig.add_subplot(121)
subfig.plot(history.history['loss'], label="training")
if history.history['val_loss'] is not None:
    subfig.plot(history.history['val_loss'], label="validation")
subfig.set_title('Model Loss')
subfig.set_xlabel('Epoch')
subfig.legend(loc='upper left')
plt.ioff()

input("Press ENTER to close the plots...")

我得到的输出如下:

Validation accuracy: 0.97599999999999998
Training accuracy:   1.0
--------------------
First 5 samples validation: [0.83400000286102294, 0.89200000095367427, 0.91599999904632567, 0.9279999976158142, 0.9399999990463257]
First 5 samples training: [0.47133333333333333, 0.70566666682561241, 0.76933333285649619, 0.81133333333333335, 0.82366666714350378]
--------------------
Last 5 samples validation: [0.9820000019073486, 0.9860000019073486, 0.97800000190734859, 0.98399999713897701, 0.975999997138977]
Last 5 samples training: [0.9540000001589457, 0.95766666698455816, 0.95600000031789145, 0.95100000031789145, 0.95033333381017049]

在这里,您可以看到我得到的图表: 培训和验证准确性和损失图

我不确定这是否相关,但我使用的是python 3.5和keras 2.0.4.

来自Keras FAQ:

为什么培训损失远高于测试损失？

Keras模型有两种模式:训练和测试.在测试时关闭正常化机制,例如Dropout和L1/L2权重正则化.

此外,培训损失是每批培训数据的平均损失.因为您的模型随着时间的推移而变化,所以第一批时期的损失通常高于最后一批.另一方面,使用模型计算时期的测试损失,因为它在时期结束时,导致较低的损失.

所以你看到的行为并不像阅读ML理论后看起来那么不寻常.这也解释了当您在同一模型上评估训练和测试集时,您突然得到预期的行为(train acc> val acc).我猜想在你的情况下,辍学的存在尤其会妨碍准确性在训练期间达到1.0,同时它在评估(测试)期间实现了这一点.

您可以通过添加在每个时期保存模型的回调来进一步调查.然后,您可以使用两个集合评估每个已保存的模型,以重新创建绘图.