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:3'):
        model = Sequential()
        model.add(TimeDistributed(BatchNormalization(), input_shape=(3, 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(Conv3D(4, kernel_size=3, strides=1, activation='relu'))
        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()

    train = np.load('npz/window_train.npz')
    x_train, y_train = train['xs'], train['ys']
    val = np.load('npz/window_val.npz')
    x_val, y_val = val['xs'], val['ys']

    print(np.count_nonzero(y_train))
    print(np.count_nonzero(y_val))

    fit_arg = {
        'x': x_train, 
        'y': y_train,
        'batch_size': 80,
        'epochs': 100,
        'shuffle': True,
        'validation_data': (x_val, y_val),
        'callbacks': get_callbacks('conv3d'),
    }
    # model.fit(**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:3'):
        model = Sequential()
        model.add(TimeDistributed(BatchNormalization(), input_shape=(3, 224, 224, 3)))
        model.add(TimeDistributed(Conv2D(5, kernel_size=5, strides=2, activation='relu')))
        model.add(TimeDistributed(Conv2D(10, kernel_size=4, strides=1, activation='relu')))
        model.add(TimeDistributed(Conv2D(15, kernel_size=3, strides=1, activation='relu')))
        model.add(TimeDistributed(BatchNormalization()))
        model.add(TimeDistributed(MaxPooling2D(pool_size=3)))
        model.add(Conv3D(8, kernel_size=3, strides=1, activation='relu'))
        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()

    train = np.load('npz/window_train.npz')
    x_train, y_train = train['xs'], train['ys']
    val = np.load('npz/window_val.npz')
    x_val, y_val = val['xs'], val['ys']

    print(np.count_nonzero(y_train))
    print(np.count_nonzero(y_val))

    fit_arg = {
        'x': x_train, 
        'y': y_train,
        'batch_size': 80,
        'epochs': 100,
        'shuffle': True,
        'validation_data': (x_val, y_val),
        'callbacks': get_callbacks('conv3d'),
    }
    # model.fit(**fit_arg)


if __name__ == '__main__':
    main()
Update at 2017-07-23 19-16-37