卷积神经网络-Keras-val_acc Keyerror 'acc'

Question

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

在最后一部分我得到这个错误： [](https://i.stack.imgur.com/sQ0Cb.png])

 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'])

原文由 Tala Emami 发布，翻译遵循 CC BY-SA 4.0 许可协议

Answer 1

当您编译模型时，您的 log 变量将与 metrics 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")])

将给出 KeyError: 'acc' 因为你没有在 --- metrics=["accuracy"] model.compile 。

当指标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 KeyError: 'acc' 因为你设置了 binary_accuracy 指标但要求 accuracy 稍后。

如果将上面的代码改成

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

它会起作用。

原文由 Qin Heyang 发布，翻译遵循 CC BY-SA 4.0 许可协议

Answer 2

在一个不太常见的情况下（正如我在一些 tensorflow 更新后预期的那样），尽管在模型定义中选择了 metrics=[“accuracy”] ，但我仍然遇到同样的错误。

解决方案是：将 metrics=[“acc”] 替换为 metrics=[“accuracy”] everywhere 。就我而言，我无法绘制我训练历史的参数。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']

原文由 Pe Dro 发布，翻译遵循 CC BY-SA 4.0 许可协议