数据挖掘 - 找到合适的 CNN 模型架构和参数 - 吾爱随笔录

我目前正在创建一个 CNN 模型，用于对字体是否为Arial、Verdana和Times New Roman进行分类Georgia。总而言之，有类，16因为我还考虑过检测字体是regular,bold还是. 所以。italicsbold italics4 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)

以下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)

谁能建议我如何知道正确的网络架构和参数以获得最佳精度？我应该如何开始调整我的网络？