# -*- coding: utf-8 -*-
"""
Created on Mon Oct 17 15:55:13 2016

@author: balaks
"""
#fully functional CNN for MNIST handwritten digit recognition on Kaggle

#importing libraries
import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Flatten
from keras.layers.convolutional import Convolution2D
from keras.layers.convolutional import MaxPooling2D
from keras.utils import np_utils
from keras import backend as K
K.set_image_dim_ordering('th')

#for repeatability
seed = 7
np.random.seed(seed)

# create data sets, skipping the header row with [1:]
dataset = pd.read_csv("../input/train.csv")
y_train = dataset[[0]].values.ravel()
X_train = dataset.iloc[:,1:].values

# reshape to be [samples][pixels][width][height]
X_train = X_train.reshape(X_train.shape[0], 1, 28, 28).astype('float32')

# normalize inputs from 0-255 to 0-1
X_train = X_train / 255

# one hot encode outputs
y_train = np_utils.to_categorical(y_train)

#subsetting loaded data for use as train and test
randIndexTrain = np.random.randint(X_train.shape[0],size=150)
randIndexTest = np.random.randint(X_train.shape[0],size=50)

X_train1 = X_train[randIndexTrain,:]
y_train1 = y_train[randIndexTrain,:]

X_test1 = X_train[randIndexTest,:]
y_test1 = y_train[randIndexTest,:]

#used later to specify the number of neurons in the output layer
num_classes = y_test1.shape[1]


def baseline_model():
	# create model
	model = Sequential()
	model.add(Convolution2D(32, 5, 5, border_mode='valid', input_shape=(1, 28, 28), activation='relu'))
	model.add(MaxPooling2D(pool_size=(2, 2)))
	model.add(Dropout(0.2))
	model.add(Flatten())
	model.add(Dense(128, activation='relu'))
	model.add(Dense(num_classes, activation='softmax'))
	# Compile model
	model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
	return model
 

# build the model
model = baseline_model()
# Fit the model
model.fit(X_train1, y_train1, validation_data=(X_test1, y_test1), nb_epoch=1, batch_size=10, verbose=0)
# Final evaluation of the model
scores = model.evaluate(X_test1, y_test1, verbose=0)
print("Baseline Error: %.2f%%" % (100-scores[1]*100)) 

type(scores)