#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
test = pd.read_csv("../input/test.csv").values

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

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

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

#subsetting loaded data for use as train and test during initial training and tuning
randIndexTrain = np.random.randint(X_train.shape[0],size=15000)
#randIndexTest = np.random.randint(X_train.shape[0],size=500)

#for use during initial model training/ tuning
X_train1 = X_train[randIndexTrain,:]
y_train1 = y_train[randIndexTrain,:]

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

#full sized training data load
#X_train1 = X_train
#y_train1 = y_train


#used later to specify the number of neurons in the output layer
num_classes = y_train1.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

def larger_model():
	# create model
	model = Sequential()
	model.add(Convolution2D(30, 5, 5, border_mode='valid', input_shape=(1, 28, 28), activation='relu'))
	model.add(MaxPooling2D(pool_size=(2, 2)))
	model.add(Convolution2D(15, 3, 3, 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(50, 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 = larger_model()

# Fit the model - initial model training and tuning
#model.fit(X_train1, y_train1, validation_data=(X_test1, y_test1), nb_epoch=10, batch_size=200, verbose=0)
# Final evaluation of the model
#scores = model.evaluate(X_test1, y_test1, verbose=0)
#print(head(scores))

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

#print("Baseline Error: %.2f%%" % (100-scores[1]*100)) 

#for Kaggle submission

# Fit the model
model.fit(X_train1, y_train1, nb_epoch=10, batch_size=200, verbose=0)

# Prediction
pred = model.predict(test)
pred1 = pred.argmax(axis=1)
pred2 = np.c_[range(1,len(test)+1),pred1]

# save results
np.savetxt('submission_cnn_keras.csv', pred2, delimiter=',', header = 'ImageId,Label', comments = '', fmt='%d')