验证的准确性总是大于Keras中的训练准确性

Question

我想用mnist数据集训练一个简单的神经网络。出于某种原因，当我获得历史记录（从model.fit返回的参数）时，验证准确性高于训练准确性，这非常奇怪，但是如果在评估模型时检查得分，则得到更高训练的准确性比测试准确。

出现这种情况，每次，不管模型的参数。另外，如果我使用自定义回调并访问参数'acc'和'val_acc'，则会发现相同的问题（数字与历史记录中返回的数字相同）。

请帮帮我！我究竟做错了什么？为什么验证的准确度高于培训准确度（您可以看到我在查看损失时有同样的问题）。

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...") 

输出我得到的是以下几点：

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] 

在这里你可以看到我得到的情节： Training and Validation accuracy and loss plots

我不知道如果这是相关的，但我使用python 3.5和keras 2.0.4。

Answer 1

从Keras FAQ：

为什么培训的损失比测试损失要高得多？

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

此外，培训损失是每批培训数据的平均损失。因为你的模型随着时间的推移而变化，所以一个时代的第一批的损失通常比最后一批的损失要高。另一方面，使用该模型计算时期的测试损失，因为它在时期结束时计算，导致较低的损失。

所以你看到的行为并不像看到ML理论后看起来那么不寻常。这也解释了当你在同一个模型上同时评估训练和测试集时，你突然会得到预期的行为（train acc> val acc）。我猜想在你的情况下，dropout的存在尤其会妨碍训练期间的准确性达到1.0，而在评估（测试）期间达到这个水平。

您可以通过添加回调来进一步调查，该回调可以在每个时期保存模型。然后你可以使用这两套评估每个保存的模型来重新创建你的图。