keras学习实例（三）：CNN

CNN即Convolutional Neural Networks即卷积神经网络，是一种多层神经网络，擅长处理图像特别是大图像的相关机器学习问题。

本次使用keras实现CNN,包含两层卷积(Conv)，两层最大池化(MaxPooling)，两层全连接（Dense）.

本次实例运行程序：

#-*- coding: UTF-8 -*-

"""
To know more or get code samples, please visit my website:
https://morvanzhou.github.io/tutorials/
Or search: 莫烦Python
Thank you for supporting!
"""

# please note, all tutorial code are running under python3.5.
# If you use the version like python2.7, please modify the code accordingly

# 6 - CNN example

# to try tensorflow, un-comment following two lines
# import os
# os.environ['KERAS_BACKEND']='tensorflow'

import numpy as np
np.random.seed(1337)  # for reproducibility
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Activation, Convolution2D, MaxPooling2D, Flatten
from keras.optimizers import Adam

# download the mnist to the path '~/.keras/datasets/' if it is the first time to be called
# training X shape (60000, 28x28), Y shape (60000, ). test X shape (10000, 28x28), Y shape (10000, )
#(X_train, y_train), (X_test, y_test) = mnist.load_data()


###调用本地mnist数据集
import numpy as np
path='/home/ren_dong/文档/mnist.npz'
f = np.load(path)
X_train, y_train = f['x_train'], f['y_train']
X_test, y_test = f['x_test'], f['y_test']
f.close()

# data pre-processing
X_train = X_train.reshape(-1, 1,28, 28)/255.      #黑白照片
X_test = X_test.reshape(-1, 1,28, 28)/255.
y_train = np_utils.to_categorical(y_train, num_classes=10)
y_test = np_utils.to_categorical(y_test, num_classes=10)

# Another way to build your CNN
model = Sequential()

# Conv layer 1 output shape (32, 28, 28)

model.add(Convolution2D(
    batch_input_shape=(None, 1, 28, 28),
    filters=32,
    kernel_size=5,
    strides=1,
    padding='same',     # Padding method
    data_format='channels_first',
))
model.add(Activation('relu'))

# Pooling layer 1 (max pooling) output shape (32, 14, 14)
model.add(MaxPooling2D(
    pool_size=2,
    strides=2,
    padding='same',    # Padding method
    data_format='channels_first',
))

# Conv layer 2 output shape (64, 14, 14)
model.add(Convolution2D(64, 5, strides=1, padding='same', data_format='channels_first'))
model.add(Activation('relu'))

# Pooling layer 2 (max pooling) output shape (64, 7, 7)
model.add(MaxPooling2D(2, 2, 'same', data_format='channels_first'))

# Fully connected layer 1 input shape (64 * 7 * 7) = (3136), output shape (1024)
model.add(Flatten())       ##flatten将三维变为一维
model.add(Dense(1024))
model.add(Activation('relu'))

# Fully connected layer 2 to shape (10) for 10 classes
model.add(Dense(10))
model.add(Activation('softmax'))

# Another way to define your optimizer
adam = Adam(lr=1e-4)

# We add metrics to get more results you want to see
model.compile(optimizer=adam,
              loss='categorical_crossentropy',
              metrics=['accuracy'])

print('Training ------------')
# Another way to train the model
model.fit(X_train, y_train, epochs=1, batch_size=64,)  ###fit功能进行训练

print('\nTesting ------------')
# Evaluate the model with the metrics we defined earlier
loss, accuracy = model.evaluate(X_test, y_test)   #evalu

print('\ntest loss: ', loss)
print('\ntest accuracy: ', accuracy)

运行结果如下：

因为小编使用CPU运行，所以时间稍长一些，最后的loss和accurary还是蛮不错的，有兴趣的同学可以试一下。

 

 

 

 

 

参考教程：

https://study.163.com/course/courseLearn.htm?courseId=1003340023#/learn/video?lessonId=1003809045&courseId=1003340023

https://github.com/MorvanZhou/tutorials/blob/master/kerasTUT/6-CNN_example.py