巧妙利用TFlearn训练自己的数据集（一）

本人研究方向为目标分类识别。

研究生阶段的研究创新点已经初具规模，老师要求多有对比实验。

利用其他经典网络来训练自己收集的图片训练集，是我想到比较直观的办法。TFlearn是Tensorflwo的一个高级API，里面有各种已经写好的网络代码示例，简单明了。（具体关于TFLearn的资料，大家自己去百度巧妙利用TFlearn训练自己的数据集（一））

在TFlearn文件夹下examples/images/里面有许多图像分类的网络可用，但缺点是里面的网络用的数据Oxford17 ,Cifar10 ,Mnist三大经典数据集，而且每个网络的数据导入函数又关联到其他函数，好不麻烦！！！

经过投机取巧的思考，如训练自己的数据集，解决方法如下：

以使用Oxford17数据集为代表的Alexnet为例：

Alexnet代码如下

from __future__ import division, print_function, absolute_import

import tflearn
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.normalization import local_response_normalization
from tflearn.layers.estimator import regression

import tflearn.datasets.oxflower17 as oxflower17
X, Y = oxflower17.load_data(one_hot=True, resize_pics=(227, 227)) ##此句调用了tflearn文件夹下dataset中oxflower17.py函数，
##此函数主要作用是下载Oxfords数据

# Building 'AlexNet'
network = input_data(shape=[None, 227, 227, 3])
network = conv_2d(network, 96, 11, strides=4, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 256, 5, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 256, 3, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 17, activation='softmax')
network = regression(network, optimizer='momentum',
                     loss='categorical_crossentropy',
                     learning_rate=0.001)

# Training
model = tflearn.DNN(network, checkpoint_path='model_alexnet',
                    max_checkpoints=1, tensorboard_verbose=2)
model.fit(X, Y, n_epoch=1000, validation_set=0.1, shuffle=True,
          show_metric=True, batch_size=64, snapshot_step=200,
          snapshot_epoch=False, run_id='alexnet_oxflowers17')