print("Hello Digit Regocnizer people")
import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense, Flatten, Dropout
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')
seed=7
np.random.seed(seed)
train = pd.read_csv('../input/digit-recognizer/train.csv')
test = pd.read_csv('../input/digit-recognizer/test.csv')
X_train = train.drop(['label'], axis=1).values.astype('float32') 
y_train = train['label'].values.astype('int32')
X_test =  test.values.astype('float32')
X_train = X_train.reshape(X_train.shape[0], 1, 28, 28).astype('float32')
X_test = X_test.reshape(X_test.shape[0], 1, 28, 28).astype('float32')
X_train = X_train/255
X_test = X_test/255
y_train = np_utils.to_categorical(y_train)
num_classes = y_train.shape[1]
def larger_model():
    model = Sequential()
    model.add(Convolution2D(30, 5, 5, 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'))
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model
model = larger_model()
model.fit(X_train, y_train, nb_epoch=10, batch_size=200, verbose=2)
results = model.predict(X_test)
results = np.argmax(results,axis = 1)
results = pd.Series(results,name="Label")
submission = pd.concat([pd.Series(range(1,28001),name = "ImageId"),results],axis = 1)
submission.to_csv("MNIST_results.csv",index=False)