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#!/usr/bin/python3
# _*_ coding=utf-8 _*_
import argparse
import code
import readline
import signal
import sys
from keras.datasets import mnist
from keras import models
from keras import layers
from keras.utils import to_categorical
import matplotlib.pyplot as plt
def SigHandler_SIGINT(signum, frame):
print()
sys.exit(0)
class Argparser(object):
def __init__(self):
parser = argparse.ArgumentParser()
parser.add_argument("--string", type=str, help="string")
parser.add_argument("--bool", action="store_true", help="bool", default=False)
parser.add_argument("--dbg", action="store_true", help="debug", default=False)
self.args = parser.parse_args()
# write code here
def premain(argparser):
signal.signal(signal.SIGINT, SigHandler_SIGINT)
#here
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
'''
print(train_images.shape)
print(len(train_labels))
print(train_labels)
print(test_images.shape)
print(len(test_labels))
print(test_labels)
digit = train_images[4]
plt.imshow(digit, cmap=plt.cm.binary)
plt.show()
'''
network = models.Sequential()
network.add(layers.Dense(512, activation="relu", input_shape=(28*28,)))
network.add(layers.Dense(10, activation="softmax"))
#network.compile(optimizer="rmsprop", loss="categorical_crossentropy", metrics=["accuracy"])
network.compile(optimizer="rmsprop", loss="mse", metrics=["accuracy"])
train_images = train_images.reshape((60000, 28 * 28))
train_images = train_images.astype("float32") / 255
test_images = test_images.reshape((10000, 28 * 28))
test_images = test_images.astype("float32") / 255
train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)
network.fit(train_images, train_labels, epochs=5, batch_size=128)
test_loss, test_acc = network.evaluate(test_images, test_labels)
print("test_acc:", test_acc)
def main():
argparser = Argparser()
if argparser.args.dbg:
try:
premain(argparser)
except Exception as e:
print(e.__doc__)
if e.message: print(e.message)
variables = globals().copy()
variables.update(locals())
shell = code.InteractiveConsole(variables)
shell.interact(banner="DEBUG REPL")
else:
premain(argparser)
if __name__ == "__main__":
main()
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