找到适当的CNN模型架构和参数

Question

我当前正在创建一个CNN模型，该模型对字体是Arial，Verdana，Times New Roman和Georgia进行分类。总共有16类，因为我考虑过还要检测字体是regular，bold，italics还是bold italics。所以4 fonts * 4 styles = 16 classes。

我在训练中使用的数据如下：

 Training data set : 800 image patches of 256 * 256 dimension (50 for each class)
 Validation data set : 320 image patches of 256 * 256 dimension (20 for each class)
 Testing data set : 160 image patches of 256 * 256 dimension (10 for each class)

下面是我的数据的示例屏幕截图：

下面是我的初始代码：

import numpy as np
import keras
from keras import backend as K
from keras.models import Sequential
from keras.layers import Activation
from keras.layers.core import Dense, Flatten
from keras.optimizers import Adam
from keras.metrics import categorical_crossentropy
from keras.preprocessing.image import ImageDataGenerator
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import *
from matplotlib import pyplot as plt
import itertools
import matplotlib.pyplot as plt
import pickle


image_width = 256
image_height = 256

train_path = 'font_model_data/train'
valid_path =  'font_model_data/valid'
test_path = 'font_model_data/test'


train_batches = ImageDataGenerator().flow_from_directory(train_path, target_size=(image_width, image_height), classes=['1','2','3','4', '5', '6', '7', '8', '9', '10', '11', '12','13', '14', '15', '16'], batch_size = 16)
valid_batches = ImageDataGenerator().flow_from_directory(valid_path, target_size=(image_width, image_height), classes=['1','2','3','4', '5', '6', '7', '8', '9', '10', '11', '12','13', '14', '15', '16'], batch_size = 16)
test_batches = ImageDataGenerator().flow_from_directory(test_path, target_size=(image_width, image_height), classes=['1','2','3','4', '5', '6', '7', '8', '9', '10', '11', '12','13', '14', '15', '16'], batch_size = 160)


 imgs, labels = next(train_batches)

 #CNN model
 model = Sequential([
     Conv2D(32, (3,3), activation='relu', input_shape=(image_width, image_height, 3)),
     Flatten(),
     Dense(16, activation='softmax'),
 ])

 print(model.summary())

 model.compile(Adam(lr=.0001),loss='categorical_crossentropy', metrics=['accuracy'])
 model.fit_generator(train_batches, steps_per_epoch = 50, validation_data= valid_batches, validation_steps = 20, epochs = 1, verbose = 2)

 model_pickle = open('cnn_font_model.pickle', 'wb')
 pickle.dump(model, model_pickle)
 model_pickle.close()
 print('Training Done.')

 test_imgs, test_labels = next(test_batches)

 predictions = model.predict_generator(test_batches, steps = 160, verbose = 2)
 print(predictions)

有人可以建议我如何知道正确的网络体系结构和参数以获得最佳精度吗？我应该如何开始调整网络？

Answer 1

在选择“网络”之前，您需要将图像图块细分为带有字符的字幕，并馈入以下架构...

# Initialising the CNN
classifier = Sequential()

# Step 1 - Convolution
classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))

# Step 2 - Pooling
classifier.add(MaxPooling2D(pool_size = (2, 2)))

# Adding a second convolutional layer
classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))

# Step 3 - Flattening
classifier.add(Flatten())

# Step 4 - Full connection
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 1, activation = 'sigmoid'))

# Compiling the CNN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
classifier.fit_generator(training_set,
                     steps_per_epoch = XXX,
                     epochs = XX,
                     validation_data = test_set,
                     validation_steps = XXX)
from keras.models import load_model
classifier.save('your_classifier.h5')