python - 卷积神经网络-Keras-val_acc Keyerror 'acc'

Question

我正在尝试通过 Theano 实现 CNN。我使用了 Keras 库。我的数据集是 55 个字母图像，28x28。

在最后一部分中，我收到此错误:

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

任何帮助将非常感激。谢谢。

这是我的代码的一部分:

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

在不太常见的情况下(正如我在一些 tensorflow 更新后所预期的那样)，尽管选择了 指标=[“准确度”] 在模型定义中，我仍然遇到相同的错误。

解决方法是:更换 指标=[“acc”] 与 指标 = [“准确度”] 无处不在 .就我而言，我无法绘制训练历史的参数。我不得不更换

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

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

到

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

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