基于MINIST数据集的卷积神经网络
该代码，使用cnn对MINIST数据集（包含7千张28*28的单通道（灰度图、黑白图）图片）分类（0-9，10个类别）
名词解释
mnist，手写数据集。
MNIST（Mixed National Institute of Standards and Technology database）是一个计算机视觉数据集，它包含70000张手写数字的灰度图片，其中每一张图片包含 28 X 28 个像素点。
数据集来自美国国家标准与技术研究所, National Institute of Standards and Technology (NIST). 训练集 (training set) 由来自 250 个不同人手写的数字构成, 其中 50% 是高中学生, 50% 来自人口普查局 (the Census Bureau) 的工作人员. 测试集(test set) 也是同样比例的手写数字数据.
官网详细解介绍 （详情，数据集结构、下载）
cnn（Convolutional Neural Networks），卷积神经网络
代码注释
mnist_cnn.py （点击查看原文）
'''Trains a simple convnet on the MNIST dataset.
训练一个基于MINIST数据集的简单卷积神经网络
Gets to 99.25% test accuracy after 12 epochs
12个周期后达到99.25%的精确度
(there is still a lot of margin for parameter tuning).
（通过参数调整还可提升精确度）
16 seconds per epoch on a GRID K520 GPU.
使用一个GRID K520 GPU （图形处理器）每个周期16秒
'''
from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
batch_size = 128
num_classes = 10
epochs = 12
# input image dimensions
# 输入图像维度
img_rows, img_cols = 28, 28
# the data, shuffled and split between train and test sets
# 用于训练和测试的数据集，经过了筛选（清洗、数据样本顺序打乱）和分割（分割为训练和测试集）
(x_train, y_train), (x_test, y_test) = mnist.load_data()
if K.image_data_format() == 'channels_first': # Theano框架，图像通道在前
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else: # TensorFlow框架，图像通道在后
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
# 类别向量转为2分类矩阵
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
activation='relu',
input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
代码运行
C:\ProgramData\Anaconda3\python.exe E:/keras-master/examples/mnist_cnn.py x_train shape: (60000, 28, 28, 1) 60000 train samples 10000 test samples Train on 60000 samples, validate on 10000 samples Epoch 1/12 128/60000 [..............................] - ETA: 17:33 - loss: 2.3013 - acc: 0.1016 384/60000 [..............................] - ETA: 5:58 - loss: 2.2054 - acc: 0.2057 640/60000 [..............................] - ETA: 3:39 - loss: 2.1000 - acc: 0.2656 896/60000 [..............................] - ETA: 2:39 - loss: 2.0585 - acc: 0.3114 1152/60000 [..............................] - ETA: 2:06 - loss: 1.9493 - acc: 0.3533 1408/60000 [..............................] - ETA: 1:45 - loss: 1.8618 - acc: 0.3899 1664/60000 [..............................] - ETA: 1:30 - loss: 1.7447 - acc: 0.4255 1920/60000 [..............................] - ETA: 1:19 - loss: 1.6956 - acc: 0.4458 2176/60000 [>.............................] - ETA: 1:11 - loss: 1.6158 - acc: 0.4720 2432/60000 [>.............................] - 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ETA: 1s - loss: 0.2767 - acc: 0.9142 54912/60000 [==========================>...] - ETA: 1s - loss: 0.2758 - acc: 0.9144 55168/60000 [==========================>...] - ETA: 1s - loss: 0.2754 - acc: 0.9146 55424/60000 [==========================>...] - ETA: 1s - loss: 0.2750 - acc: 0.9148 55680/60000 [==========================>...] - ETA: 1s - loss: 0.2745 - acc: 0.9150 55936/60000 [==========================>...] - ETA: 1s - loss: 0.2738 - acc: 0.9153 56192/60000 [===========================>..] - ETA: 0s - loss: 0.2731 - acc: 0.9155 56448/60000 [===========================>..] - ETA: 0s - loss: 0.2723 - acc: 0.9158 56704/60000 [===========================>..] - ETA: 0s - loss: 0.2719 - acc: 0.9159 56960/60000 [===========================>..] - ETA: 0s - loss: 0.2711 - acc: 0.9161 57216/60000 [===========================>..] - ETA: 0s - loss: 0.2704 - acc: 0.9164 57472/60000 [===========================>..] - ETA: 0s - loss: 0.2696 - acc: 0.9166 57728/60000 [===========================>..] - ETA: 0s - loss: 0.2687 - acc: 0.9169 57984/60000 [===========================>..] - ETA: 0s - loss: 0.2682 - acc: 0.9170 58240/60000 [============================>.] - ETA: 0s - loss: 0.2678 - acc: 0.9172 58496/60000 [============================>.] - ETA: 0s - loss: 0.2671 - acc: 0.9174 58752/60000 [============================>.] - ETA: 0s - loss: 0.2664 - acc: 0.9176 59008/60000 [============================>.] - ETA: 0s - loss: 0.2656 - acc: 0.9179 59264/60000 [============================>.] - ETA: 0s - loss: 0.2654 - acc: 0.9180 59520/60000 [============================>.] - ETA: 0s - loss: 0.2647 - acc: 0.9182 59776/60000 [============================>.] - ETA: 0s - loss: 0.2639 - acc: 0.9184 60000/60000 [==============================] - 16s 265us/step - loss: 0.2633 - acc: 0.9186 - val_loss: 0.0583 - val_acc: 0.9809 Epoch 2/12 128/60000 [..............................] - ETA: 12s - loss: 0.0178 - acc: 0.9922 384/60000 [..............................] - ETA: 12s - loss: 0.0326 - acc: 0.9870 59904/60000 [============================>.] - ETA: 0s - loss: 0.0300 - acc: 0.9907 60000/60000 [==============================] - 14s 235us/step - loss: 0.0299 - acc: 0.9907 - val_loss: 0.0265 - val_acc: 0.9915 Epoch 11/12 128/60000 [..............................] - ETA: 12s - loss: 0.0337 - acc: 0.9922 384/60000 [..............................] - ETA: 12s - loss: 0.0295 - acc: 0.9948 59904/60000 [============================>.] - ETA: 0s - loss: 0.0257 - acc: 0.9918 60000/60000 [==============================] - 13s 213us/step - loss: 0.0257 - acc: 0.9918 - val_loss: 0.0291 - val_acc: 0.9919 Test loss: 0.029070270710035036 Test accuracy: 0.9919 Process finished with exit code 0