continueTrain.py

import os
import numpy as np
import pandas as pd
from keras import models, optimizers, backend
from keras.layers import core, convolutional, pooling
from sklearn import model_selection,utils
from dataPreprocessing import generate_samples, preprocess

if __name__ == '__main__':

    # Read splitted  data

    df_train = pd.read_csv('train.csv')
    df_valid = pd.read_csv('test.csv')

    # CNN Model Architecture
    model = models.Sequential()
    model.add(convolutional.Convolution2D(16, 3, 3, input_shape=(32, 128, 3), activation='relu'))
    model.add(pooling.MaxPooling2D(pool_size=(2, 2)))
    model.add(convolutional.Convolution2D(32, 3, 3, activation='relu'))
    model.add(pooling.MaxPooling2D(pool_size=(2, 2)))
    model.add(convolutional.Convolution2D(64, 3, 3, activation='relu'))
    model.add(pooling.MaxPooling2D(pool_size=(2, 2)))
    model.add(core.Flatten())
    model.add(core.Dense(500, activation='relu'))
    model.add(core.Dropout(.5))
    model.add(core.Dense(100, activation='relu'))
    model.add(core.Dropout(.25))
    model.add(core.Dense(20, activation='relu'))
    model.add(core.Dense(1))
    model.compile(optimizer=optimizers.Adam(lr=1e-04), loss='mean_squared_error')

    # load the exist model
    model.load_weights("model.h5")

    history = model.fit_generator(# continue  training model for 17 epochs
        generate_samples(df_train, ''),
        samples_per_epoch=df_train.shape[0],
        nb_epoch=17,#0.016
        validation_data=generate_samples(df_valid, '', augment=False),
        nb_val_samples=df_valid.shape[0],
    )

    with open(os.path.join('', 'model.json'), 'w') as file: # save trained model 
        file.write(model.to_json())

    backend.clear_session()