# pylint: skip-file
import sys
sys.path.insert(0, '../../python')
import mxnet as mx
import numpy as np
import os, pickle, gzip
import logging
from common import get_data

# symbol net
batch_size = 100

data = mx.symbol.Variable('data')
conv1= mx.symbol.Convolution(data = data, name='conv1', num_filter=32, kernel=(3,3), stride=(2,2))
bn1 = mx.symbol.BatchNorm(data = conv1, name="bn1")
act1 = mx.symbol.Activation(data = bn1, name='relu1', act_type="relu")
mp1 = mx.symbol.Pooling(data = act1, name = 'mp1', kernel=(2,2), stride=(2,2), pool_type='max')

conv2= mx.symbol.Convolution(data = mp1, name='conv2', num_filter=32, kernel=(3,3), stride=(2,2))
bn2 = mx.symbol.BatchNorm(data = conv2, name="bn2")
act2 = mx.symbol.Activation(data = bn2, name='relu2', act_type="relu")
mp2 = mx.symbol.Pooling(data = act2, name = 'mp2', kernel=(2,2), stride=(2,2), pool_type='max')


fl = mx.symbol.Flatten(data = mp2, name="flatten")
fc2 = mx.symbol.FullyConnected(data = fl, name='fc2', num_hidden=10)
softmax = mx.symbol.SoftmaxOutput(data = fc2, name = 'sm')

num_epoch = 1
model = mx.model.FeedForward(softmax, mx.cpu(),
                             num_epoch=num_epoch,
                             learning_rate=0.1, wd=0.0001,
                             momentum=0.9)
# check data
get_data.GetMNIST_ubyte()

train_dataiter = mx.io.MNISTIter(
        image="data/train-images-idx3-ubyte",
        label="data/train-labels-idx1-ubyte",
        data_shape=(1, 28, 28),
        label_name='sm_label',
        batch_size=batch_size, shuffle=True, flat=False, silent=False, seed=10)
val_dataiter = mx.io.MNISTIter(
        image="data/t10k-images-idx3-ubyte",
        label="data/t10k-labels-idx1-ubyte",
        data_shape=(1, 28, 28),
        label_name='sm_label',
        batch_size=batch_size, shuffle=True, flat=False, silent=False)

def test_mnist():
    # print logging by default
    logging.basicConfig(level=logging.DEBUG)
    console = logging.StreamHandler()
    console.setLevel(logging.DEBUG)
    logging.getLogger('').addHandler(console)

    model.fit(X=train_dataiter,
              eval_data=val_dataiter)
    logging.info('Finish fit...')
    prob = model.predict(val_dataiter)
    logging.info('Finish predict...')
    val_dataiter.reset()
    y = np.concatenate([batch.label[0].asnumpy() for batch in val_dataiter]).astype('int')
    py = np.argmax(prob, axis=1)
    acc1 = float(np.sum(py == y)) / len(y)
    logging.info('final accuracy = %f', acc1)
    assert(acc1 > 0.94)


if __name__ == "__main__":
    test_mnist()