目錄：

1. 分散式Estimator
   • 自定義模型
   • 建立自己的機器學習Estimator
   • 調節RunConfig執行時的引數
   • Experiment和LearnRunner
2. 深度學習Estimator
   • 深度神經網路
   • 廣度深度模型
3. 機器學習Estimator
   • 線性/邏輯迴歸
   • 隨機森林
   • K均值聚類
   • 支援向量機
4. DataFrame
5. 監督器Monitors
6. 程式碼例子

一、分散式Estimator

Estimator包含各種機器學習和深度學習的類，使用者能直接使用這些高階類，同時可根據實際的應用需求快速建立自己的子類。

六、程式碼例子---TFlearn實現AlexNet

資料為鮮花資料集 ：

17_Category_Flower 是一個不同種類鮮花的影象資料，包含 17 不同種類的鮮花，每類 80 張該類鮮花的圖片，鮮花種類是英國地區常見鮮花。

程式碼：

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)) ##此句呼叫了tflearn資料夾下dataset中oxflower17.py函式，下載資料


#構建AlexNet網路

# 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')