Finding the appropriate CNN Model Architecture and Parameters - machine-learning

Question

I am currently creating a CNN model that classifies whether the font is Arial, Verdana, Times New Roman and Georgia. All in all there are 16 classes since I considered also detecting whether the font is regular, bold, italics or bold italics. So 4 fonts * 4 styles = 16 classes.
The data that I have used in my training are the following:
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)
Below is the sample screenshot of my data:
Below is my initial code:
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)
Can anyone suggest how will I know the right network architecture and parameters to get the optimal accuracy? How should I start tweaking my network?

Answer 1

Before going to choose Network you need to segmentize the image tile into subtitles with characters and feed to the following architecture...
# 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')