char74k_cnn.py

#!/usr/bin/env python
'''Train a simple deep CNN on the CIFAR10 small images dataset.

GPU run command:
    THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python cifar10_cnn.py

It gets down to 0.65 test logloss in 25 epochs, and down to 0.55 after
50 epochs.  (it's still underfitting at that point, though).

Note: the data was pickled with Python 2, and some encoding issues
might prevent you from loading it in Python 3. You might have to load
it in Python 2, save it in a different format, load it in Python 3 and
repickle it.

'''

from __future__ import print_function
#from keras.datasets import cifar10
import char74
from keras.preprocessing.image import ImageDataGenerator
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, Adadelta, Adagrad
from keras.utils import np_utils, generic_utils
from six.moves import range

batch_size = 32
nb_classes = 26
nb_epoch = 7
data_augmentation = False

# input image dimensions
img_rows, img_cols = 32, 32
# the CIFAR10 images are RGB
img_channels = 3


def load_dataset():
    # the data, shuffled and split between train and test sets
    # (X_train, y_train), (X_test, y_test) = cifar10.load_data()
    (X_train, y_train), (X_test, y_test) = char74.load_data("char74kdata")
    print('X_train shape:', X_train.shape)
    print(X_train.shape[0], 'train samples')
    print(X_test.shape[0], 'test samples')

    # convert class vectors to binary class matrices
    Y_train = np_utils.to_categorical(y_train, nb_classes)
    Y_test = np_utils.to_categorical(y_test, nb_classes)

    X_train = X_train.astype('float32')
    X_test = X_test.astype('float32')
    # X_train /= 255
    # X_test /= 255

    return X_train, Y_train, X_test, Y_test


def make_network():
    model = Sequential()

    model.add(Convolution2D(32, 3, 3, border_mode='same',
                            input_shape=(img_channels, img_rows, img_cols)))
    model.add(Activation('relu'))
    model.add(Convolution2D(32, 3, 3))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))

    model.add(Convolution2D(64, 3, 3, border_mode='same'))
    model.add(Activation('relu'))
    model.add(Convolution2D(64, 3, 3))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))

    model.add(Flatten())
    model.add(Dense(512))
    model.add(Activation('relu'))
    model.add(Dropout(0.5))
    model.add(Dense(nb_classes))
    model.add(Activation('softmax'))

    return model

def train_model(model, X_train, Y_train, X_test, Y_test):
    sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
    model.compile(loss='categorical_crossentropy',
              optimizer=sgd,
              metrics=['accuracy'])

    if not data_augmentation:
        print('Not using data augmentation.')
        model.fit(X_train, Y_train, batch_size=batch_size,
              nb_epoch=nb_epoch,
              validation_data=(X_test, Y_test), shuffle=True)
    else:
        print('Using real-time data augmentation.')

    # this will do preprocessing and realtime data augmentation
        datagen = ImageDataGenerator(
            featurewise_center=False,  # set input mean to 0 over the dataset
            samplewise_center=False,  # set each sample mean to 0
            featurewise_std_normalization=False,  # divide inputs by std of the dataset
            samplewise_std_normalization=False,  # divide each input by its std
            zca_whitening=False,  # apply ZCA whitening
            rotation_range=10,  # randomly rotate images in the range (degrees, 0 to 180)
            width_shift_range=0,  # randomly shift images horizontally (fraction of total width)
            height_shift_range=0,  # randomly shift images vertically (fraction of total height)
            horizontal_flip=False,  # randomly flip images
            vertical_flip=False)  # randomly flip images

    # compute quantities required for featurewise normalization
    # (std, mean, and principal components if ZCA whitening is applied)
        datagen.fit(X_train)

    # fit the model on the batches generated by datagen.flow()
        model.fit_generator(datagen.flow(X_train, Y_train, batch_size=batch_size),
                        samples_per_epoch=X_train.shape[0],
                        nb_epoch=nb_epoch,
                        validation_data=(X_test, Y_test),
                        nb_worker=1)

def save_model(model):
    model_json = model.to_json()
    open('char74k_architecture1.json', 'w').write(model_json)
    model.save_weights('char74k_weights1.h5', overwrite=True)


if __name__ == '__main__':
    X_train, Y_train, X_test, Y_test = load_dataset()
    model = make_network()
    train_model(model, X_train, Y_train, X_test, Y_test)
    save_model(model)