卷积神经网络-Keras-val_acc键误差'acc‘

Question

我正在努力执行西亚诺的CNN节目。我用的是凯拉斯图书馆。我的数据集是55个字母表图像，28x28。

在最后一部分中，我得到了以下错误：

​

train_acc=hist.history['acc']
KeyError: 'acc'

任何帮助都将不胜感激。谢谢。

这是我代码的一部分：

​

from keras.models import Sequential
from keras.models import Model
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.optimizers import SGD, RMSprop, adam
from keras.utils import np_utils

import matplotlib
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from urllib.request import urlretrieve
import pickle
import os
import gzip
import numpy as np
import theano
import lasagne
from lasagne import layers
from lasagne.updates import nesterov_momentum
from nolearn.lasagne import NeuralNet
from nolearn.lasagne import visualize
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from PIL import Image
import PIL.Image
#from Image import *
import webbrowser
from numpy import *
from sklearn.utils import shuffle
from sklearn.cross_validation import train_test_split
from tkinter import *
from tkinter.ttk import *
import tkinter

from keras import backend as K
K.set_image_dim_ordering('th')
%%%%%%%%%%

batch_size = 10

# number of output classes
nb_classes = 6

# number of epochs to train
nb_epoch = 5

# input iag dimensions
img_rows, img_clos = 28,28

# number of channels
img_channels = 3

# number of convolutional filters to use
nb_filters = 32

# number of convolutional filters to use
nb_pool = 2

# convolution kernel size
nb_conv = 3

%%%%%%%%

model = Sequential()

model.add(Convolution2D(nb_filters, nb_conv, nb_conv,
                        border_mode='valid',
                        input_shape=(1, img_rows, img_clos)))
convout1 = Activation('relu')
model.add(convout1)
model.add(Convolution2D(nb_filters, nb_conv, nb_conv))
convout2 = Activation('relu')
model.add(convout2)
model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
model.add(Dropout(0.5))

model.add(Flatten())
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adadelta')

%%%%%%%%%%%%

hist = model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,
              show_accuracy=True, verbose=1, validation_data=(X_test, Y_test))
            
            
hist = model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,
              show_accuracy=True, verbose=1, validation_split=0.2)
%%%%%%%%%%%%%%

train_loss=hist.history['loss']
val_loss=hist.history['val_loss']
train_acc=hist.history['acc']
val_acc=hist.history['val_acc']
xc=range(nb_epoch)
#xc=range(on_epoch_end)

plt.figure(1,figsize=(7,5))
plt.plot(xc,train_loss)
plt.plot(xc,val_loss)
plt.xlabel('num of Epochs')
plt.ylabel('loss')
plt.title('train_loss vs val_loss')
plt.grid(True)
plt.legend(['train','val'])
print (plt.style.available) # use bmh, classic,ggplot for big pictures
plt.style.use(['classic'])

plt.figure(2,figsize=(7,5))
plt.plot(xc,train_acc)
plt.plot(xc,val_acc)
plt.xlabel('num of Epochs')
plt.ylabel('accuracy')
plt.title('train_acc vs val_acc')
plt.grid(True)
plt.legend(['train','val'],loc=4)
#print plt.style.available # use bmh, classic,ggplot for big pictures
plt.style.use(['classic'])

​

Answer 1

在编译模型时，log变量将与metrics保持一致。

例如，下面的代码

model.compile(loss="mean_squared_error", optimizer=optimizer) 
model.fit_generator(gen,epochs=50,callbacks=ModelCheckpoint("model_{acc}.hdf5")])

will给出了一个KeyError: 'acc'，因为您没有在model.compile中设置metrics=["accuracy"]。

当度量标准不匹配时，也会发生此错误。例如

model.compile(loss="mean_squared_error",optimizer=optimizer, metrics="binary_accuracy"]) 
model.fit_generator(gen,epochs=50,callbacks=ModelCheckpoint("model_{acc}.hdf5")])

仍然给出一个KeyError: 'acc'，因为您设置了一个binary_accuracy度量，但是稍后请求accuracy。

如果将上述代码更改为

model.compile(loss="mean_squared_error",optimizer=optimizer, metrics="binary_accuracy"]) 
model.fit_generator(gen,epochs=50,callbacks=ModelCheckpoint("model_{binary_accuracy}.hdf5")])

看起来不错。

Answer 2

在一个不太常见的情况下(正如我在一些tensorflow更新之后所预期的那样)，尽管在模型定义中选择了metrics="accuracy"，但我仍然得到了相同的错误。

解决方案是:将metrics="acc"替换为metrics=“准确性”everywhere。在我的例子中，我无法画出我的训练历史的参数。我不得不替换

acc = history.history['acc']
val_acc = history.history['val_acc']

loss = history.history['loss']
val_loss = history.history['val_loss']

至

acc = history.history['accuracy']
val_acc = history.history['val_accuracy']

loss = history.history['loss']
val_loss = history.history['val_loss']

Answer 3

您可以使用print(history.history.keys())来找出您拥有的度量标准以及它们的名称。在我的例子中，它被称为"accuracy"，而不是"acc"。