import numpy as np # Array manipulation
import pandas as pd # Dataframe manipulation

# Multilayer perceptron Neural Network
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.utils import np_utils

# Load data
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')

# fix random seed for reproducibility
seed = 7
np.random.seed(seed)

# Extract images pixels
images = train.iloc[:,1:].values
images = images.astype(np.float)

# Extract numbers Labels
labels = train.iloc[:,0].values

# Normalize input from 0-255 to 0-1
images = images / 255.0
num_pixels =  images.shape[1]

# one hot encode outputs
labels = np_utils.to_categorical(labels)
num_classes = labels.shape[1]

# define baseline model
def mlp_model():
	# create model
	model = Sequential()
	model.add(Dense(num_pixels, input_dim=num_pixels, init='normal', activation='relu'))
	model.add(Dense(num_classes, init='normal', activation='softmax'))
	# Compile model
	model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
	return model
	
# build the model
model = mlp_model()
# Fit the model
model.fit(images, labels, nb_epoch=10, batch_size=200, verbose=2)

# use the NN model to classify test data
pred = model.predict(test.values)
pred = pred.argmax(1) # transform the binary matrix to an array of 0 to 9 labels
# save results
np.savetxt('submission_MLP.csv', np.c_[range(1,len(test)+1),pred], delimiter=',', header = 'ImageId,Label', comments = '', fmt='%d')