CNN 模型给出了错误的预测

Question

我目前正在研究区域语言的手写数字识别。目前，我专注于奥里亚语。我通过 CNN 模型测试 MNIST 数据集，并尝试将模型应用于我的 Oriya 数据集。模型表现不佳。它给出了错误的预测。我有一个包含 4971 个样本的数据集。
如何提高准确率？
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 调整模型的大小，它会生成以下错误：
/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() > 0 in function resize

Answer 1

如何提高准确率？

有点难以从您发布的内容中给出详细的答案，并且没有看到一些数据样本，但仍然会尝试对此进行尝试。我可以看到可能有助于提高准确性的是：

1. 获取更多数据。在深度学习中，通常使用大量数据，并且模型几乎总是在添加更多数据时得到改进。如果您无法获得新数据，您可以尝试使用您获得的数据生成更多样本，方法是添加噪声或类似修改。

2. 我看到您目前有 30 个和 10 个 epoch 来训练您的模型。 我建议你增加 epoch 的数量，这样你的模型就有更多的时间收敛。这在大多数情况下也可以在一定程度上提高性能。

3. 我还看到您的模型上的批量大小为 100 和 200。 您可以尝试减少训练过程的批量大小，以便您的训练在每个 epoch 上执行更多次梯度更新（请记住，您甚至可以使用 batch_size=1 为 each 升级模型/em> 样本，而不是批次）。

4. 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性能。最好从一个简单的模型开始，训练和测试，然后添加层和节点，直到您对结果满意为止。我还看到您尝试了混合卷积和非卷积方法；在增加架构的复杂性之前，您可以尝试从其中一种方法开始。