import json
from pathlib import Path

import numpy as np
import pandas as pd

import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
set_session(tf.Session(config=config))

from keras.models import Sequential, Model
from keras.preprocessing import image
from keras.layers import *
from keras.optimizers import *

from data import *
from utils import get_callbacks


def main():
    with tf.device('/gpu:2'):
        model = Sequential()
        model.add(TimeDistributed(BatchNormalization(), input_shape=(TIMESTEPS, 224, 224, 3)))
        model.add(TimeDistributed(Conv2D(4, kernel_size=5, strides=3, activation='relu')))
        model.add(TimeDistributed(Conv2D(8, kernel_size=5, strides=2, activation='relu')))
        model.add(TimeDistributed(Conv2D(12, kernel_size=3, strides=1, activation='relu')))
        model.add(TimeDistributed(BatchNormalization()))
        model.add(TimeDistributed(MaxPooling2D(pool_size=3)))
        model.add(TimeDistributed(Flatten()))
        model.add(LSTM(10))
        model.add(BatchNormalization())
        model.add(Flatten())
        model.add(Dense(16))
        model.add(Dropout(0.3))
        model.add(Dense(1, activation='sigmoid'))

    model_arg = {
        'loss': 'binary_crossentropy',
        'optimizer': 'sgd',
        'metrics': ['binary_accuracy']
    }
    model.compile(**model_arg)
    model.summary()

    fit_arg = {
        'generator': window_train_gen_online,
        'steps_per_epoch': N_WINDOW_TRAIN // WINDOW_BATCH_SIZE,
        'epochs': 30,
        'validation_data': window_val_gen_online,
        'validation_steps': N_WINDOW_VAL // WINDOW_BATCH_SIZE,
        'callbacks': get_callbacks('conv3d')
    }

    # model.fit_generator(**fit_arg)


if __name__ == '__main__':
    main()
import json
from pathlib import Path

import numpy as np
import pandas as pd

import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
set_session(tf.Session(config=config))

from keras.models import Sequential, Model
from keras.preprocessing import image
from keras.layers import *
from keras.optimizers import *

from data import *
from utils import get_callbacks


def main():
    with tf.device('/gpu:2'):
        model = Sequential()
        model.add(TimeDistributed(BatchNormalization(), input_shape=(TIMESTEPS, 224, 224, 3)))
        model.add(TimeDistributed(Conv2D(4, kernel_size=5, strides=3, activation='relu')))
        model.add(TimeDistributed(Conv2D(8, kernel_size=5, strides=2, activation='relu')))
        model.add(TimeDistributed(Conv2D(12, kernel_size=3, strides=1, activation='relu')))
        model.add(TimeDistributed(BatchNormalization()))
        model.add(TimeDistributed(MaxPooling2D(pool_size=3)))
        model.add(TimeDistributed(Flatten()))
        model.add(LSTM(10))
        model.add(BatchNormalization())
        model.add(Dense(16))
        model.add(Dropout(0.3))
        model.add(Dense(1, activation='sigmoid'))

    model_arg = {
        'loss': 'binary_crossentropy',
        'optimizer': 'sgd',
        'metrics': ['binary_accuracy']
    }
    model.compile(**model_arg)
    model.summary()

    fit_arg = {
        'generator': window_train_gen_online,
        'steps_per_epoch': N_WINDOW_TRAIN // WINDOW_BATCH_SIZE,
        'epochs': 30,
        'validation_data': window_val_gen_online,
        'validation_steps': N_WINDOW_VAL // WINDOW_BATCH_SIZE,
        'callbacks': get_callbacks('conv3d')
    }

    # model.fit_generator(**fit_arg)


if __name__ == '__main__':
    main()
Update at 2017-07-20 12-49-56