import pandas as pd
import lasagne
from lasagne import layers
from lasagne.updates import nesterov_momentum
from nolearn.lasagne import NeuralNet

import sys
import os
import gzip
import pickle
import numpy

# The competition datafiles are in the directory ../input
# Read competition data files:
train = pd.read_csv("../input/train.csv")
test  = pd.read_csv("../input/test.csv")

X = train.drop(['label'], axis=1).values.astype('float32') 
y = train.label.values.astype('int32')
X_test = test.values.astype('float32') 

# Write to the log:
print("Training set has {0[0]} rows and {0[1]} columns".format(train.shape))
print("Test set has {0[0]} rows and {0[1]} columns".format(test.shape))
# Any files you write to the current directory get shown as outputs

net1 = NeuralNet(
        layers=[('input', layers.InputLayer),
                ('hidden', layers.DenseLayer),
                ('output', layers.DenseLayer),
                ],
        # layer parameters:
        input_shape=(None, 28*28),
        hidden_num_units=100,  # number of units in 'hidden' layer
        output_nonlinearity=lasagne.nonlinearities.softmax,
        output_num_units=10,  # 10 target values for the digits 0, 1, 2, ..., 9

        # optimization method:
        update=nesterov_momentum,
        update_learning_rate=0.01,
        update_momentum=0.9,

        max_epochs=10,
        verbose=1,
        )
        
net1.fit(X,y)

pred = net1.predict(X_test)

pred = pd.DataFrame(pred)

pred.to_csv("pred_mlp.csv")