train_model.py

# import required modules
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers
import matplotlib.pyplot as plt 

# import created model
from net import Net

# Dimensions of our images
img_width, img_height = 32, 32

# 3 channel image
no_of_channels = 3

# train data Directory
train_data_dir = 'train/' 
# test data Directory
validation_data_dir = 'test/' 

epochs = 80
batch_size = 32

#initialize model
model = Net.build(width = img_width, height = img_height, depth = no_of_channels)
print('building done')
# Compile model
rms = optimizers.RMSprop(lr=0.001, rho=0.9, epsilon=None, decay=0.0)
print('optimizing done')

model.compile(loss='categorical_crossentropy',
              optimizer=rms,
              metrics=['accuracy'])

print('compiling')

# this is the augmentation configuration used for training
# horizontal_flip = False, as we need to retain Characters
train_datagen = ImageDataGenerator(
    featurewise_center=True,
    featurewise_std_normalization=True,
    rescale=1. / 255,
    shear_range=0.1,
    zoom_range=0.1,
    rotation_range=5,
    width_shift_range=0.05,
    height_shift_range=0.05,
    horizontal_flip=False)

# this is the augmentation configuration used for testing, only rescaling
test_datagen = ImageDataGenerator(featurewise_center=True, featurewise_std_normalization=True, rescale=1. / 255)

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='categorical')

validation_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='categorical')

# fit the model
history = model.fit_generator(
    train_generator,
    steps_per_epoch=train_generator.samples / batch_size, 
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=validation_generator.samples / batch_size)  

# evaluate on validation dataset
model.evaluate_generator(validation_generator)
# save weights in a file
model.save_weights('trained_weights.h5') 

print(history.history)

# Loss Curves
plt.figure(figsize=[8,6])
plt.plot(history.history['loss'],'r',linewidth=3.0)
plt.plot(history.history['val_loss'],'b',linewidth=3.0)
plt.legend(['Training loss', 'Validation Loss'],fontsize=18)
plt.xlabel('Epochs ',fontsize=16)
plt.ylabel('Loss',fontsize=16)
plt.title('Loss Curves',fontsize=16)
 
# Accuracy Curves
plt.figure(figsize=[8,6])
plt.plot(history.history['acc'],'r',linewidth=3.0)
plt.plot(history.history['val_acc'],'b',linewidth=3.0)

plt.legend(['Training Accuracy', 'Validation Accuracy'],fontsize=18)
plt.xlabel('Epochs ',fontsize=16)
plt.ylabel('Accuracy',fontsize=16)
plt.title('Accuracy Curves',fontsize=16)
plt.show()