Machine_Learning

import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, LeakyReLU, Conv2D
from keras.optimizers import RMSprop
from keras import backend as K
K.tensorflow_backend._get_available_gpus()
from matplotlib.pyplot import imshow
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data

# 讀入 MNIST
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# the data, shuffled and split between train and test sets
x_train = x_train.reshape(60000 , 784)
x_test = x_test.reshape(10000, 784)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255

# convert class vectors to binary class matrices
num_classes = 10
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

print(x_train.shape, 'train samples')
print(x_test.shape, 'test samples')
print(y_train.shape, 'train labels')
print(y_test.shape, 'test labels')

# modeling
model = Sequential()
model.add(Dense(680, activation=LeakyReLU(), input_shape=(784,)))
model.add(Dense(1230, activation=LeakyReLU()))
model.add(Dense(10, activation='softmax'))
#model.summary()

print('modling complete')

# training
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

print('training complete')

# testing

model.fit(x_train, y_train, batch_size=150, epochs=20, verbose=1)

scores = model.evaluate(x_test, y_test)

print('Acc on testing data : ', scores[1])