Question

我目前正致力于区域语言的手写数字识别。目前，我专注于Oriya。我通过CNN模型测试MNIST数据集，我试图在我的Oriya数据集上应用该模型。模特表现不佳。它给出了错误的预测。我有4971个样本的数据集如何提高准确度？
这是我的代码：

from keras.models import Sequential
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 numpy as np
import matplotlib.pyplot as plt
import matplotlib
import os
import theano
from PIL import Image
from numpy import *
# SKLEARN
from sklearn.utils import shuffle
from sklearn.cross_validation import train_test_split



# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)



# input image dimensions
img_rows, img_cols = 28, 28

# number of channels
img_channels = 1


path2 = '/home/saumya/Desktop/Oriya/p'    #path of folder of images


imlist = os.listdir(path2)


im1 = array(Image.open('/home/saumya/Desktop/Oriya/p' + '/'+ imlist[0]))    # open one image to get size
m,n = im1.shape[0:2] # get the size of the images
imnbr = len(imlist) # get the number of images


# create matrix to store all flattened images
immatrix = array([array(Image.open('/home/saumya/Desktop/Oriya/p' + '/'+  im2)).flatten()
           for im2 in imlist],'f')

label=np.ones((num_samples,),dtype = int)
label[1:503]=0
label[503:1000]=1
label[1000:1497]=2
label[1497:1995]=3
label[1995:2493]=4
label[2493:2983]=5
label[2983:3483]=6
label[3483:3981]=7
label[3981:4479]=8
label[4479:4972]=9

print(label[1000])
data,Label = shuffle(immatrix,label, random_state=2)
train_data = [data,Label]

img=immatrix[2496].reshape(img_rows,img_cols)
plt.imshow(img)
plt.show()



(X, y) = (train_data[0],train_data[1])



X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=4)


X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)
X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)

X_train = X_train.astype('float32')
X_test = X_test.astype('float32')

X_train /= 255
X_test /= 255

print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')



X_train = X_train.reshape(X_train.shape[0], 1, 28, 28).astype('float32')
X_test = X_test.reshape(X_test.shape[0], 1, 28, 28).astype('float32')



y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
num_classes = y_test.shape[1]


def baseline_model():
# create model
model = Sequential()
model.add(Conv2D(32, (3,3), input_shape=(1, 28, 28), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))

#model.add(Conv2D(64, (5, 5), input_shape=(1, 10, 10), activation='relu'))
#model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(num_classes, activation='softmax', name = 'first_dense_layer'))
# Compile model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model



# build the model
model = baseline_model()
# Fit the model
hist=model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=30, batch_size=100, verbose=2)
# Final evaluation of the model
scores = model.evaluate(X_test, y_test, verbose=0)
print("CNN Error: %.2f%%" % (100-scores[1]*100))



score = model.evaluate(X_test, y_test, verbose=0)
print('Test Loss:', score[0])
print('Test accuracy:', score[1])

test_image = X_test[0:1]
print (test_image.shape)

print(model.predict(test_image))
print(model.predict_classes(test_image))
print(y_test[0:1])




# define the larger model
def larger_model():
# create model
model = Sequential()
model.add(Conv2D(30, (5, 5), input_shape=(1, 28, 28), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(15, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(50, activation='relu', name='first_dense_layer'))
model.add(Dense(num_classes, activation='softmax'))
# Compile model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model


# build the model
model = larger_model()
# Fit the model
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=200)
# Final evaluation of the model
scores = model.evaluate(X_test, y_test, verbose=0)
print("Large CNN Error: %.2f%%" % (100-scores[1]*100))

我正在尝试使用opencv调整模型大小，它会产生以下错误：
/root/mc-x64-2.7/conda-bld/opencv-3_1482254119970/work/opencv-3.1.0/modules/imgproc/src/imgwarp.cpp:3229：错误：（-215）ssize.area（）＆gt; 0在函数调整大小

Answer 1

如何提高准确度？

有点难以从您发布的内容中给出详细答案，并且没有看到一些数据样本，但仍会尝试对此进行抨击。我所看到的可能有助于提高您的准确性：

获取更多数据。在深度学习中，通常使用大量数据，并且在添加更多数据时模型几乎总是会改进。如果您无法获取新数据，可以尝试通过添加噪声或类似修改来使用您获得的样本生成更多样本。
我发现你的模型训练目前有30和10个时代。 我建议你增加纪元数，这样你的模型就有更多时间收敛。这也是大多数时候在一定程度上提高性能。
我还看到您的模型上的批量大小为100和200。 您可以尝试减少培训流程的批量大小，因此您的培训会在每个时期执行更多次的渐变更新（请记住，您甚至可以使用batch_size=1升级的模型每个样本，而不是批次。
或者，您可以尝试迭代地增加架构的复杂性和层次，并比较您的效果。最好从简单模型开始，训练和测试，然后添加图层和节点，直到您对结果满意为止。我也看到你尝试过混合卷积和非卷积方法;在增加架构的复杂性之前，您可以先尝试其中一种方法。

CNN模型给出了错误的预测

1 个答案: