from sklearn.ensemble import RandomForestClassifier
import numpy as np
import pandas as pd

# The competition datafiles are in the directory ../input
# Read competition data files:
dataset = pd.read_csv("../input/train.csv")
target = dataset[[0]].values.ravel()
train = dataset.iloc[:,1:].values
test = pd.read_csv("../input/test.csv").values

# convert to array, specify data type, and reshape
target = target.astype(np.uint8)
train = np.array(train).reshape((-1, 1, 28, 28)).astype(np.uint8)
test = np.array(test).reshape((-1, 1, 28, 28)).astype(np.uint8)

import lasagne
from lasagne import layers
from lasagne.updates import nesterov_momentum
from nolearn.lasagne import NeuralNet
from nolearn.lasagne import visualize

net1 = NeuralNet(
        layers=[('input', layers.InputLayer),
                ('hidden', layers.DenseLayer),
                ('output', layers.DenseLayer),
                ],
        # layer parameters:
        input_shape=(None,1,28,28),
        hidden_num_units=1000, # 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=0.0001,
        update_momentum=0.9,

        max_epochs=15,
        verbose=1,
        )
        
# Train the network
net1.fit(train, target)

def CNN(n_epochs):
    net1 = NeuralNet(
        layers=[
        ('input', layers.InputLayer),
        ('conv1', layers.Conv2DLayer),      #Convolutional layer.  Params defined below
        ('pool1', layers.MaxPool2DLayer),   # Like downsampling, for execution speed
        ('conv2', layers.Conv2DLayer),
        ('hidden3', layers.DenseLayer),
        ('output', layers.DenseLayer),
        ],

    input_shape=(None, 1, 28, 28),
    conv1_num_filters=7, 
    conv1_filter_size=(3, 3), 
    conv1_nonlinearity=lasagne.nonlinearities.rectify,
        
    pool1_pool_size=(2, 2),
        
    conv2_num_filters=12, 
    conv2_filter_size=(2, 2),    
    conv2_nonlinearity=lasagne.nonlinearities.rectify,
        
    hidden3_num_units=1000,
    output_num_units=10, 
    output_nonlinearity=lasagne.nonlinearities.softmax,

    update_learning_rate=0.0001,
    update_momentum=0.9,

    max_epochs=n_epochs,
    verbose=1,
    )
    return net1

cnn = CNN(15).fit(train,target) # train the CNN model for 15 epochs

# use the NN model to classify test data
pred = cnn.predict(test)

# save results
np.savetxt('submission_cnn.csv', np.c_[range(1,len(test)+1),pred], delimiter=',', header = 'ImageId,Label', comments = '', fmt='%d')