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 Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras import backend as K
from keras.utils import np_utils
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas as pd 

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

print(train.shape)

train.rename(columns={ '0': 'label'}, inplace=True)

print(train.head())

X_train = train.drop('label', axis=1)
y_train = train['label']

standard_scaler = MinMaxScaler()
standard_scaler.fit(X_train)

X_train = standard_scaler.transform(X_train)
X_test = standard_scaler.transform(X_test)

X_train = X_train.reshape((X_train.shape[0], 28, 28, 1)).astype('float32')
X_test = X_test.reshape((X_test.shape[0], 28, 28, 1)).astype('float32')

print(X_train.shape, X_test.shape)

y_train = np_utils.to_categorical(y_train)

num_classes = y_train.shape[1]

print(num_classes)


model = Sequential()

model.add(Conv2D(32, (5, 5), input_shape=(28, 28, 1), activation='relu', data_format="channels_last", padding="same"))
model.add(Conv2D(32, (5, 5), activation='relu', data_format="channels_last", padding="same"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Conv2D(64, (3, 3), activation='relu', data_format="channels_last", padding="same"))
model.add(Conv2D(64, (3, 3), activation='relu', data_format="channels_last", padding="same"))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2,2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(256, activation = "relu"))
model.add(Dropout(0.5))
model.add(Dense(10, activation = "softmax"))

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=10, batch_size=256, verbose=2)

predictions = model.predict(X_test)
predictions = np.argmax(predictions, axis=1)
predictions = pd.Series(predictions, name="Label")

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

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

print("DONE!")