目录：

1. 分布式Estimator
   • 自定义模型
   • 建立自己的机器学习Estimator
   • 调节RunConfig运行时的参数
   • Experiment和LearnRunner
2. 深度学习Estimator
   • 深度神经网络
   • 广度深度模型
3. 机器学习Estimator
   • 线性/逻辑回归
   • 随机森林
   • K均值聚类
   • 支持向量机
4. DataFrame
5. 监督器Monitors
6. 代码例子

一、分布式Estimator

Estimator包含各种机器学习和深度学习的类，用户能直接使用这些高阶类，同时可根据实际的应用需求快速创建自己的子类。

 

六、代码例子---TFlearn实现AlexNet

数据为鲜花数据集 ：

17_Category_Flower 是一个不同种类鲜花的图像数据，包含 17 不同种类的鲜花，每类 80 张该类鲜花的图片，鲜花种类是英国地区常见鲜花。

代码：

import tflearnfrom tflearn.layers.core import input_data, dropout, fully_connectedfrom tflearn.layers.conv import conv_2d, max_pool_2dfrom tflearn.layers.normalization import local_response_normalizationfrom tflearn.layers.estimator import regression import tflearn.datasets.oxflower17 as oxflower17X, 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，下载数据#构建AlexNet网络# 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) # Trainingmodel = 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')