#Handwritten Digit Recognition Using Convolutional Neural Networks in Python with Keras
#https://machinelearningmastery.com/handwritten-digit-recognition-using-convolutional-neural-networks-python-keras/ 


# Larger CNN for the MNIST Dataset
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.utils import to_categorical
# load data
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# reshape to be [samples][width][height][channels]
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')
# normalize inputs from 0-255 to 0-1
X_train = X_train / 255
X_test = X_test / 255
# one hot encode outputs
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
num_classes = y_test.shape[1]
# define the larger model
def larger_model():
	# create model
	model = Sequential()
	model.add(Conv2D(30, (5, 5), input_shape=(28, 28, 1), activation='relu'))
	model.add(MaxPooling2D())
	model.add(Conv2D(15, (3, 3), activation='relu'))
	model.add(MaxPooling2D())
	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
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=200)
# Final evaluation of the model
scores = model.evaluate(X_test, y_test, verbose=0)
print("Large CNN Error: %.2f%%" % (100-scores[1]*100))


