train.py

"""
File: train_emotion_classifier.py
Author: Octavio Arriaga
Email: arriaga.camargo@gmail.com
Github: https://github.com/oarriaga
Description: Train emotion classification model
"""

from keras.callbacks import CSVLogger, ModelCheckpoint, EarlyStopping
from keras.callbacks import ReduceLROnPlateau
from keras.preprocessing.image import ImageDataGenerator

from cnn import mini_XCEPTION
from utils import preprocess_input
import numpy as np
import h5py
import sklearn
from sklearn.cross_validation import train_test_split
# parameters
batch_size = 32
num_epochs = 10000
input_shape = (48, 48, 1)
validation_split = 0.1
num_classes = 7
patience = 50
base_path = 'trained_models/float_models/'

# data generator
data_generator = ImageDataGenerator(
                        featurewise_center=False,
                        featurewise_std_normalization=False,
                        rotation_range=10,
                        width_shift_range=0.1,
                        height_shift_range=0.1,
                        zoom_range=.1,
                        horizontal_flip=True)

# model parameters/compilation
model = mini_XCEPTION(input_shape, num_classes)
model.compile(optimizer='adam', loss='categorical_crossentropy',
              metrics=['accuracy'])
model.summary()


datasets = ['fer2013']

for dataset_name in datasets:
    print('Training dataset:', dataset_name)
    # callbacks
    log_file_path = base_path + dataset_name + '_emotion_training.log'
    csv_logger = CSVLogger(log_file_path, append=False)
    early_stop = EarlyStopping('val_loss', patience=patience)
    reduce_lr = ReduceLROnPlateau('val_loss', factor=0.1,
                                  patience=int(patience/4), verbose=1)
    trained_models_path = base_path + dataset_name + '_mini_XCEPTION'
    model_names = trained_models_path + '.{epoch:02d}-{val_acc:.2f}.hdf5'
    model_checkpoint = ModelCheckpoint(model_names, 'val_loss', verbose=1,
                                                    save_best_only=True)
    callbacks = [model_checkpoint, csv_logger, early_stop, reduce_lr]

    # loading dataset
    f = h5py.File('Data.hdf5','r')
    X = f['X'].value
    X = preprocess_input(X)
    Y = f['Y'].value
    f.close()
    #X = np.load('X.npy')
    #Y = np.load('Y.npy')
    train_X,test_X,train_Y,test_Y = train_test_split(X,Y,test_size=validation_split,random_state=0)

    model.fit_generator(data_generator.flow(train_X, train_Y,
                                            batch_size),
                        steps_per_epoch=len(train_X) / batch_size,
                        epochs=num_epochs, verbose=1, callbacks=callbacks,
                        validation_data=(test_X,test_Y))