基于pycaffe从零开始写mnist（第三篇）——生成deploy.prototxt，做最后的验证

deploy.prototxt和之前的train.prototxt文件内容差不多，因此可以不需要用代码进行生成，直接将不一样的地方进行修改即可

下面是用代码生成的deploy.prototxt

# -*- coding: utf-8 -*-__author__ = 'xuy'import caffedef creat_deploy():    net = caffe.NetSpec()    net.conv1 = caffe.layers.Convolution(bottom = 'data', kernel_size = 5, num_output = 20,                                         weight_filler = dict(type = 'xavier'))    net.pool1 = caffe.layers.Pooling(net.conv1, kernel_size = 2, stride = 2,                                     pool = caffe.params.Pooling.MAX)    net.conv2 = caffe.layers.Convolution(net.pool1, kernel_size = 5, num_output = 50,                                         weight_filler = dict(type = 'xavier'))    net.pool2 = caffe.layers.Pooling(net.conv2, kernel_size = 2, stride = 2,                                     pool = caffe.params.Pooling.MAX)    net.fc1 =   caffe.layers.InnerProduct(net.pool2, num_output = 500,                                          weight_filler = dict(type = 'xavier'))    net.relu1 = caffe.layers.ReLU(net.fc1, in_place = True)    net.score = caffe.layers.InnerProduct(net.relu1, num_output = 10,                                          weight_filler = dict(type = 'xavier'))    net.prob = caffe.layers.Softmax(net.score)    return net.to_proto()def write_net(deploy_proto):    #写入deploy.prototxt文件    with open(deploy_proto, 'w') as f:        #写入第一层数据描述        f.write('input:"data"\n')        f.write('input_dim:1\n')        f.write('input_dim:3\n')        f.write('input_dim:28\n')        f.write('input_dim:28\n')        f.write(str(creat_deploy()))if __name__ == '__main__':     my_project_root = "/home/xuy/桌面/code/python/caffe/python_mnist/"    #my-caffe-project目录     deploy_proto = my_project_root + "mnist/deploy.prototxt"     write_net(deploy_proto)

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最后对于训练的模型caffemodel进行预测，随机找出测试集当中的一张图片进行测试

# -*- coding: utf-8 -*-__author__ = 'xuy''''labels_filename的作用是：将数字标签转换回类别名称'''import caffeimport numpy as npdef test(my_project_root, deploy_proto):#对于已经训练好的caffemodel进行测试    caffe_model = my_project_root + 'mnist_iter_9380.caffemodel'        #caffe_model文件的位置    img = my_project_root + 'mnist/test/8/00542.png'                    #随机找的一张待测图片    labels_filename = my_project_root + 'mnist/test/labels.txt'            #类别名称文件,将数字标签转换回类别名称    net = caffe.Net(deploy_proto, caffe_model, caffe.TEST)                #加载model和deploy    #图片预处理设置    transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})  #设定图片的shape格式(1,3,28,28)    transformer.set_transpose('data', (2,0,1))                            #改变维度的顺序，由原始图片(28,28,3)变为(3,28,28)    transformer.set_raw_scale('data', 255)                                # 缩放到【0，255】之间    transformer.set_channel_swap('data', (2,1,0))                       #交换通道，将图片由RGB变为BGR    im = caffe.io.load_image(img)                                       #加载图片    net.blobs['data'].data[...] = transformer.preprocess('data',im)     #执行上面设置的图片预处理操作，并将图片载入到blob中    out = net.forward()                                                    #执行测试,对于deploy.prototxt进行前向传播测试    labels = np.loadtxt(labels_filename, str, delimiter='\t')           #读取类别名称文件当中的所有labels内容    prob = net.blobs['prob'].data[0].flatten()                             #取出最后一层（Softmax）属于某个类别的概率值    order = prob.argsort()[-1]                                          #将概率值排序，取出最大值所在的序号    print '图片数字为:',labels[order]                                   #将该序号转换成对应的类别名称，并打印if __name__ == '__main__':    my_project_root = "/home/xuy/桌面/code/python/caffe/python_mnist/"    #my-caffe-project目录    deploy_proto = my_project_root + "mnist/deploy.prototxt"            #保存deploy.prototxt文件的位置    test(my_project_root, deploy_proto)