# -*- coding: utf-8 -*-

"""

Created on Tue Oct 2 20:57:40 2018

@author: anjan

"""

#Data preprocessing

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

from keras.utils.np_utils import to_categorical

digits = pd.read_csv('input/train.csv')

test = pd.read_csv('input/test.csv')

X = digits.iloc[:,1:].values

y = digits.iloc[:,0].values

#normalize

X = X/255.0

test = test/255.0

#reshape

X = X.reshape(-1,28,28,1)

test = test.values.reshape(-1,28,28,1)

#label encoding

y = to_categorical(y, num_classes=10)

#train test split

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.1,random_state=2)

#build the model

from keras.models import Sequential

from keras.layers import Convolution2D, MaxPooling2D, Dense, Flatten

classifier = Sequential()

classifier.add(Convolution2D(32, (3,3), input_shape=(28,28,1), activation='relu'))

classifier.add(MaxPooling2D(pool_size=(2,2)))

classifier.add(Convolution2D(32, (3,3), activation='relu'))

classifier.add(MaxPooling2D(pool_size=(2,2)))

classifier.add(Flatten())

classifier.add(Dense(128, activation='relu'))

classifier.add(Dense(10, activation='softmax'))

#compiling CNN

classifier.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])

classifier.fit(X_train, y_train, batch_size=32, epochs=30, validation_data=(X_test, y_test))

#predict values from validation set

Y_pred = classifier.predict(X_test)

# Convert predictions classes to one hot vectors

Y_pred_classes = np.argmax(Y_pred,axis = 1)

# Convert validation observations to one hot vectors

Y_true = np.argmax(y_test,axis = 1)

#plot confusion matrix

from sklearn.metrics import confusion_matrix

conf_matrix = confusion_matrix(Y_true, Y_pred_classes)

#predicting test set

results = classifier.predict(test)

results = np.argmax(results, axis=1)

results = pd.Series(results, name="label")

g = plt.imshow(test[2][:,:,0])

submission = pd.concat([pd.Series(range(1,28001),name = "ImageId"),results],axis = 1)

submission.to_csv("mnist_submission.csv",index=False)