CNN卷積神經網絡簡單實作模型

這是基于Mnist手寫識别的資料訓練的一個簡單的CNN卷積神經網絡，可以直接在網上下載下傳訓練資料集，但是經常會出現連接配接不到伺服器的提示，是以我下到本地進行資料的加載，下面代碼的資料加載有問題，是以自己找了一些代碼整出來了這個資料加載的辦法，連結為：https://blog.csdn.net/lxiao428/article/details/83020066

# -*- coding: utf-8 -*-
"""
Created on Fri Oct 26 22:09:21 2018

@author: Lxiao217
"""

from keras import models
from keras import layers


#卷積神經網絡接收形狀為(image_height, image_width, image_channels)的輸入張量（不包括批量次元）
model = models.Sequential()
model.add(layers.Conv2D(32, (3,3), padding = 'same', activation = 'relu', input_shape = (28,28,1)))
model.add(layers.MaxPooling2D())
model.add(layers.Conv2D(64, (3,3), padding = 'same', activation = 'relu'))
model.add(layers.MaxPooling2D())
model.add(layers.Conv2D(64, (3,3), padding = 'same', activation = 'relu'))
#print(model.summary())
#将輸出張量展平為1D張量
model.add(layers.Flatten())
model.add(layers.Dense(64, activation = 'relu'))
model.add(layers.Dense(10, activation = 'softmax'))
print(model.summary())

from keras.datasets import mnist
from keras.utils import to_categorical

train_images = mnist.load_data('F:\\python\\DeepLearning\\train-images-idx3-ubyte')
train_labels = mnist.load_data('F:\\python\\DeepLearning\\train-labels-idx1-ubyte')
test_images = mnist.load_data('F:\\python\\DeepLearning\\t10k-images-idx3-ubyte')
test_labels = mnist.load_data('F:\\python\\DeepLearning\\t10k-labels-idx1-ubyte')

(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
train_images = train_images.reshape((60000, 28, 28, 1))
train_images = train_images.astype('float32') / 255
test_images = test_images.reshape((10000, 28, 28, 1))
test_images = test_images.astype('float32') / 255
train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=5, batch_size=64)