From c659ae936cb4d105ec2e6bb95cfba177d28febc5 Mon Sep 17 00:00:00 2001
From: shixiaowen03 <shixiaowen03@meituan.com>
Date: Fri, 21 Dec 2018 12:12:29 +0800
Subject: [PATCH] distributional rl

---
 .idea/workspace.xml                           |  345 +++---
 RL/Basic-DisRL-Demo/Categorical_DQN.py        |  120 ++
 RL/Basic-DisRL-Demo/Config.py                 |   22 +
 RL/Basic-DisRL-Demo/main.py                   |   65 ++
 RL/Basic-DisRL-Demo/readme                    |    2 +
 RL/Basic-DisRL-Demo/utils.py                  |   35 +
 .../class_and_instance-checkpoint.ipynb       |    6 +
 basic/class_and_instance.ipynb                | 1031 +++++++++++++++++
 8 files changed, 1458 insertions(+), 168 deletions(-)
 create mode 100644 RL/Basic-DisRL-Demo/Categorical_DQN.py
 create mode 100644 RL/Basic-DisRL-Demo/Config.py
 create mode 100644 RL/Basic-DisRL-Demo/main.py
 create mode 100644 RL/Basic-DisRL-Demo/readme
 create mode 100644 RL/Basic-DisRL-Demo/utils.py
 create mode 100644 basic/.ipynb_checkpoints/class_and_instance-checkpoint.ipynb
 create mode 100644 basic/class_and_instance.ipynb

diff --git a/.idea/workspace.xml b/.idea/workspace.xml
index 98a45f4b..3c612acc 100644
--- a/.idea/workspace.xml
+++ b/.idea/workspace.xml
@@ -2,10 +2,7 @@
 <project version="4">
   <component name="ChangeListManager">
     <list default="true" id="69f12537-6c3d-43b1-a62a-ad07d70a14cd" name="Default" comment="">
-      <change type="MODIFICATION" beforePath="$PROJECT_DIR$/.idea/misc.xml" afterPath="$PROJECT_DIR$/.idea/misc.xml" />
-      <change type="MODIFICATION" beforePath="$PROJECT_DIR$/.idea/tensorflow1.2.iml" afterPath="$PROJECT_DIR$/.idea/tensorflow1.2.iml" />
       <change type="MODIFICATION" beforePath="$PROJECT_DIR$/.idea/workspace.xml" afterPath="$PROJECT_DIR$/.idea/workspace.xml" />
-      <change type="MODIFICATION" beforePath="$PROJECT_DIR$/basic/python_lazy_property.ipynb" afterPath="$PROJECT_DIR$/basic/python_lazy_property.ipynb" />
     </list>
     <option name="EXCLUDED_CONVERTED_TO_IGNORED" value="true" />
     <option name="TRACKING_ENABLED" value="true" />
@@ -17,13 +14,14 @@
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     <SUITE FILE_PATH="coverage/tensorflow1_2$preprocess.coverage" NAME="preprocess Coverage Results" MODIFIED="1541940517652" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/recommendation/Basic-RippleNet-Demo" />
+    <SUITE FILE_PATH="coverage/tensorflow1_2$Categorical_DQN.coverage" NAME="Categorical_DQN Coverage Results" MODIFIED="1545365293074" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/RL/Basic-DisRL-Demo" />
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+    <SUITE FILE_PATH="coverage/tensorflow1_2$main.coverage" NAME="main Coverage Results" MODIFIED="1545137858530" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/RL/Basic-NoisyNet-Demo" />
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@@ -31,7 +29,7 @@
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+    <SUITE FILE_PATH="coverage/tensorflow1_2$main__1_.coverage" NAME="main (1) Coverage Results" MODIFIED="1545365395991" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/RL/Basic-DisRL-Demo" />
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@@ -56,23 +54,33 @@
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-        <entry file="file://$PROJECT_DIR$/RL/Basic-NoisyNet-Demo/main.py">
+      <file leaf-file-name="Categorical_DQN.py" pinned="false" current-in-tab="false">
+        <entry file="file://$PROJECT_DIR$/RL/Basic-DisRL-Demo/Categorical_DQN.py">
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+                <element signature="e#0#23#0" expanded="true" />
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+      <file leaf-file-name="readme" pinned="false" current-in-tab="true">
+        <entry file="file://$PROJECT_DIR$/RL/Basic-DisRL-Demo/readme">
+          <provider selected="true" editor-type-id="text-editor">
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+          </provider>
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+      </file>
       <file leaf-file-name="NoisyNetDQN.py" pinned="false" current-in-tab="false">
         <entry file="file://$PROJECT_DIR$/RL/Basic-NoisyNet-Demo/NoisyNetDQN.py">
           <provider selected="true" editor-type-id="text-editor">
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@@ -80,48 +88,40 @@
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-      <file leaf-file-name="readme" pinned="false" current-in-tab="false">
-        <entry file="file://$PROJECT_DIR$/RL/Basic-NoisyNet-Demo/readme">
+      <file leaf-file-name="utils.py" pinned="false" current-in-tab="false">
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-      <file leaf-file-name="RL_brain.py" pinned="false" current-in-tab="false">
-        <entry file="file://$PROJECT_DIR$/RL/DQN-demo/RL_brain.py">
+      <file leaf-file-name="main.py" pinned="false" current-in-tab="false">
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       <file leaf-file-name="Config.py" pinned="false" current-in-tab="false">
-        <entry file="file://$PROJECT_DIR$/RL/Basic-NoisyNet-Demo/Config.py">
+        <entry file="file://$PROJECT_DIR$/RL/Basic-DisRL-Demo/Config.py">
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@@ -155,13 +155,6 @@
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-        <option value="$PROJECT_DIR$/recommendation/Basic-DIEN-Demo/model.py" />
-        <option value="$PROJECT_DIR$/recommendation/Basic-Meta-Graph-Model.py" />
-        <option value="$PROJECT_DIR$/recommendation/Basic-DKN-Demo/news/news_preprocess.py" />
-        <option value="$PROJECT_DIR$/recommendation/Basic-DKN-Demo/kg/prepare_data_for_transx.py" />
-        <option value="$PROJECT_DIR$/recommendation/Basic-DKN-Demo/kg/kg_preprocess.py" />
-        <option value="$PROJECT_DIR$/recommendation/Basic-DKN-Demo/kg/Fast-TransX/transD/transD.cpp" />
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@@ -201,11 +194,18 @@
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         <option value="$PROJECT_DIR$/RL/Basic-NoisyNet-Demo/Config.py" />
-        <option value="$PROJECT_DIR$/RL/Basic-NoisyNet-Demo/utils.py" />
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         <option value="$PROJECT_DIR$/RL/Basic-NoisyNet-Demo/readme" />
         <option value="$PROJECT_DIR$/ssfsdfsdf.py" />
+        <option value="$PROJECT_DIR$/RL/Basic-NoisyNet-Demo/utils.py" />
+        <option value="$PROJECT_DIR$/RL/Basic-DisRL-Demo.py" />
+        <option value="$PROJECT_DIR$/RL/Basic-DisRL-Demo/Config.py" />
+        <option value="$PROJECT_DIR$/RL/Basic-DisRL-Demo/util.py" />
+        <option value="$PROJECT_DIR$/RL/Basic-DisRL-Demo/utils.py" />
+        <option value="$PROJECT_DIR$/RL/Basic-DisRL-Demo/main.py" />
+        <option value="$PROJECT_DIR$/RL/Basic-DisRL-Demo/Categorical_DQN.py" />
+        <option value="$PROJECT_DIR$/RL/Basic-DisRL-Demo/readme" />
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@@ -234,8 +234,8 @@
       <foldersAlwaysOnTop value="true" />
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@@ -243,6 +243,35 @@
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               <item name="tensorflow1.2" type="462c0819:PsiDirectoryNode" />
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+              <item name="tensorflow1.2" type="462c0819:PsiDirectoryNode" />
+              <item name="RL" type="462c0819:PsiDirectoryNode" />
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@@ -283,21 +312,21 @@
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+  <component name="RunManager" selected="Python.main (1)">
+    <configuration name="Categorical_DQN" type="PythonConfigurationType" factoryName="Python" temporary="true">
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       <option name="PARENT_ENVS" value="true" />
       <envs>
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       <option name="SDK_HOME" value="" />
-      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/RL/Basic-HER-Demo" />
+      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/RL/Basic-DisRL-Demo" />
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       <option name="ADD_CONTENT_ROOTS" value="true" />
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-      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/RL/Basic-HER-Demo/HindSightExperienceReplay.py" />
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@@ -320,74 +349,74 @@
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       <option name="EMULATE_TERMINAL" value="false" />
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-    <configuration name="main" type="PythonConfigurationType" factoryName="Python" temporary="true">
+    <configuration name="main (1)" type="PythonConfigurationType" factoryName="Python" temporary="true">
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       <option name="PARENT_ENVS" value="true" />
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       <option name="SDK_HOME" value="" />
-      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/RL/Basic-NoisyNet-Demo" />
+      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/RL/Basic-DisRL-Demo" />
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+    <entry file="file://$PROJECT_DIR$/basic/some_numpy_func.py" />
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@@ -776,15 +720,26 @@
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@@ -792,35 +747,89 @@
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 </project>
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diff --git a/RL/Basic-DisRL-Demo/Categorical_DQN.py b/RL/Basic-DisRL-Demo/Categorical_DQN.py
new file mode 100644
index 00000000..281a6ddf
--- /dev/null
+++ b/RL/Basic-DisRL-Demo/Categorical_DQN.py
@@ -0,0 +1,120 @@
+import tensorflow as tf
+import numpy as np
+import random
+from collections import deque
+from Config import Categorical_DQN_Config
+from utils import conv, dense
+import math
+
+
+class Categorical_DQN():
+    def __init__(self,env,config):
+        self.sess = tf.InteractiveSession()
+        self.config = config
+        self.v_max = self.config.v_max
+        self.v_min = self.config.v_min
+        self.atoms = self.config.atoms
+
+        self.time_step = 0
+        self.epsilon = self.config.INITIAL_EPSILON
+        self.state_shape = env.observation_space.shape
+        self.action_dim = env.action_space.n
+
+        target_state_shape = [1]
+        target_state_shape.extend(self.state_shape)
+
+        self.state_input = tf.placeholder(tf.float32,target_state_shape)
+        self.action_input = tf.placeholder(tf.int32,[1,1])
+
+        self.m_input = tf.placeholder(tf.float32,[self.atoms])
+
+        self.delta_z = (self.v_max - self.v_min) / (self.atoms - 1)
+        self.z = [self.v_min + i * self.delta_z for i in range(self.atoms)]
+
+        self.build_cate_dqn_net()
+
+        self.saver = tf.train.Saver()
+
+        self.sess.run(tf.global_variables_initializer())
+
+        self.save_model()
+        self.restore_model()
+
+
+
+
+    def build_layers(self, state, action, c_names, units_1, units_2, w_i, b_i, reg=None):
+        with tf.variable_scope('conv1'):
+            conv1 = conv(state, [5, 5, 3, 6], [6], [1, 2, 2, 1], w_i, b_i)
+        with tf.variable_scope('conv2'):
+            conv2 = conv(conv1, [3, 3, 6, 12], [12], [1, 2, 2, 1], w_i, b_i)
+        with tf.variable_scope('flatten'):
+            flatten = tf.contrib.layers.flatten(conv2)
+
+        with tf.variable_scope('dense1'):
+            dense1 = dense(flatten, units_1, [units_1], w_i, b_i)
+        with tf.variable_scope('dense2'):
+            dense2 = dense(dense1, units_2, [units_2], w_i, b_i)
+        with tf.variable_scope('concat'):
+            concatenated = tf.concat([dense2, tf.cast(action, tf.float32)], 1)
+        with tf.variable_scope('dense3'):
+            dense3 = dense(concatenated, self.atoms, [self.atoms], w_i, b_i) # 返回
+        return dense3
+
+    def build_cate_dqn_net(self):
+        with tf.variable_scope('target_net'):
+            c_names = ['target_net_arams',tf.GraphKeys.GLOBAL_VARIABLES]
+            w_i = tf.random_uniform_initializer(-0.1,0.1)
+            b_i = tf.constant_initializer(0.1)
+            self.z_target = self.build_layers(self.state_input,self.action_input,c_names,24,24,w_i,b_i)
+
+        with tf.variable_scope('eval_net'):
+            c_names = ['eval_net_params',tf.GraphKeys.GLOBAL_VARIABLES]
+            w_i = tf.random_uniform_initializer(-0.1,0.1)
+            b_i = tf.constant_initializer(0.1)
+            self.z_eval = self.build_layers(self.state_input,self.action_input,c_names,24,24,w_i,b_i)
+
+
+        self.q_eval = tf.reduce_sum(self.z_eval * self.z)
+        self.q_target = tf.reduce_sum(self.z_target * self.z)
+
+        self.cross_entropy_loss = -tf.reduce_sum(self.m_input * tf.log(self.z_eval))
+
+        self.optimizer = tf.train.AdamOptimizer(self.config.LEARNING_RATE).minimize(self.cross_entropy_loss)
+
+
+
+
+    def train(self,s,r,action,s_,gamma):
+        list_q_ = [self.sess.run(self.q_target,feed_dict={self.state_input:[s_],self.action_input:[[a]]}) for a in range(self.action_dim)]
+        a_ = tf.argmax(list_q_).eval()
+        m = np.zeros(self.atoms)
+        p = self.sess.run(self.z_target,feed_dict = {self.state_input:[s_],self.action_input:[[a_]]})[0]
+        for j in range(self.atoms):
+            Tz = min(self.v_max,max(self.v_min,r+gamma * self.z[j]))
+            bj = (Tz - self.v_min) / self.delta_z # 分在第几个块里
+            l,u = math.floor(bj),math.ceil(bj) # 上下界
+
+            pj = p[j]
+
+            m[int(l)] += pj * (u - bj)
+            m[int(u)] += pj * (bj - l)
+
+        self.sess.run(self.optimizer,feed_dict={self.state_input:[s] , self.action_input:[action], self.m_input: m })
+
+
+
+    def save_model(self):
+        print("Model saved in : ", self.saver.save(self.sess, self.config.MODEL_PATH))
+
+    def restore_model(self):
+        self.saver.restore(self.sess, self.config.MODEL_PATH)
+        print("Model restored.")
+
+
+    def greedy_action(self,s):
+        self.epsilon = max(self.config.FINAL_EPSILON, self.epsilon * self.config.EPSILIN_DECAY)
+        if random.random() <= self.epsilon:
+            return random.randint(0, self.action_dim - 1)
+        return np.argmax(
+            [self.sess.run(self.q_target,feed_dict={self.state_input:[s],self.action_input:[[a]]}) for a in range(self.action_dim)])
diff --git a/RL/Basic-DisRL-Demo/Config.py b/RL/Basic-DisRL-Demo/Config.py
new file mode 100644
index 00000000..36495f52
--- /dev/null
+++ b/RL/Basic-DisRL-Demo/Config.py
@@ -0,0 +1,22 @@
+class Categorical_DQN_Config():
+    v_min = 0
+    v_max = 1000
+    atoms = 51
+
+    # ENV_NAME = "CartPole-v1"
+    ENV_NAME = 'Breakout-v0'  # 0: hold   1: throw the ball   2: move right   3: move left
+    # ENV_NAME = "Freeway-v0"
+    GAMMA = 0.99  # discount factor for target Q
+    START_TRAINING = 1000  # experience replay buffer size
+    BATCH_SIZE = 64  # size of minibatch
+    UPDATE_TARGET_NET = 400  # update eval_network params every 200 steps
+    LEARNING_RATE = 0.01
+    MODEL_PATH = './model/C51DQN_model'
+
+    INITIAL_EPSILON = 0.9  # starting value of epsilon
+    FINAL_EPSILON = 0.05  # final value of epsilon
+    EPSILIN_DECAY = 0.9999
+
+    replay_buffer_size = 2000
+    iteration = 5
+    episode = 300  # 300 games per iteration
diff --git a/RL/Basic-DisRL-Demo/main.py b/RL/Basic-DisRL-Demo/main.py
new file mode 100644
index 00000000..dfa36cf7
--- /dev/null
+++ b/RL/Basic-DisRL-Demo/main.py
@@ -0,0 +1,65 @@
+
+import matplotlib.pyplot as plt
+import tensorflow as tf
+import gym
+import numpy as np
+import pickle
+from Config import Categorical_DQN_Config
+from Categorical_DQN import Categorical_DQN
+
+
+def map_scores(dqfd_scores=None, ddqn_scores=None, xlabel=None, ylabel=None):
+    if dqfd_scores is not None:
+        plt.plot(dqfd_scores, 'r')
+    if ddqn_scores is not None:
+        plt.plot(ddqn_scores, 'b')
+    if xlabel is not None:
+        plt.xlabel(xlabel)
+    if ylabel is not None:
+        plt.ylabel(ylabel)
+    plt.show()
+
+def BreakOut_CDQN(index, env):
+    with tf.variable_scope('DQfD_' + str(index)):
+        agent = Categorical_DQN(env, Categorical_DQN_Config())
+    scores = []
+    for e in range(Categorical_DQN_Config.episode):
+        done = False
+        score = 0  # sum of reward in one episode
+        state = env.reset()
+        # while done is False:
+        last_lives = 5
+        throw = True
+        items_buffer = []
+        while not done:
+            env.render()
+            action = 1 if throw else agent.greedy_action(state)
+            next_state, real_reward, done, info = env.step(action)
+            lives = info['ale.lives']
+            train_reward = 1 if throw else -1 if lives < last_lives else real_reward
+            score += real_reward
+            throw = lives < last_lives
+            last_lives = lives
+            # agent.train(state, train_reward, [action], next_state, 0.1)
+            items_buffer.append([state, [action], next_state, 0.1])
+            state = next_state
+            if train_reward != 0:  # train when miss the ball or score or throw the ball in the beginning
+                for item in items_buffer:
+                    agent.train(item[0], -1 if throw else train_reward, item[1], item[2], item[3])
+                items_buffer = []
+        scores.append(score)
+        agent.save_model()
+        # if np.mean(scores[-min(10, len(scores)):]) > 495:
+        #     break
+    return scores
+
+
+if __name__ == '__main__':
+    env = gym.make("Breakout-v0")
+    CDQN_sum_scores = np.zeros(Categorical_DQN_Config.episode)
+    for i in range(Categorical_DQN_Config.iteration):
+        scores = BreakOut_CDQN(i,env)
+        c51_sum_scores = [a + b for a, b in zip(scores, CDQN_sum_scores)]
+    C51DQN_mean_scores = CDQN_sum_scores / Categorical_DQN_Config.iteration
+    with open('/Users/mahailong/C51DQN/C51DQN_mean_scores.p', 'wb') as f:
+        pickle.dump(C51DQN_mean_scores, f, protocol=2)
\ No newline at end of file
diff --git a/RL/Basic-DisRL-Demo/readme b/RL/Basic-DisRL-Demo/readme
new file mode 100644
index 00000000..2562e4fd
--- /dev/null
+++ b/RL/Basic-DisRL-Demo/readme
@@ -0,0 +1,2 @@
+论文：A Distributional Perspective on Reinforcement Learning
+地址：https://arxiv.org/abs/1707.06887
\ No newline at end of file
diff --git a/RL/Basic-DisRL-Demo/utils.py b/RL/Basic-DisRL-Demo/utils.py
new file mode 100644
index 00000000..339ebdfb
--- /dev/null
+++ b/RL/Basic-DisRL-Demo/utils.py
@@ -0,0 +1,35 @@
+import tensorflow as tf
+from tensorflow.python.framework import ops
+
+
+def conv(inputs, kernel_shape, bias_shape, strides, w_i, b_i=None, activation=tf.nn.relu):
+    # 使用tf.layers
+    # relu1 = tf.layers.conv2d(input_imgs, filters=24, kernel_size=[5, 5], strides=[2, 2],
+    #                          padding='SAME', activation=tf.nn.relu,
+    #                          kernel_initializer=w_i, bias_initializer=b_i)
+    weights = tf.get_variable('weights', shape=kernel_shape, initializer=w_i)
+    conv = tf.nn.conv2d(inputs, weights, strides=strides, padding='SAME')
+    if bias_shape is not None:
+        biases = tf.get_variable('biases', shape=bias_shape, initializer=b_i)
+        return activation(conv + biases) if activation is not None else conv + biases
+    return activation(conv) if activation is not None else conv
+
+
+def dense(inputs, units, bias_shape, w_i, b_i=None, activation=tf.nn.relu):
+    # 使用tf.layers，注意：先flatten
+    # dense1 = tf.layers.dense(tf.contrib.layers.flatten(relu5), activation=tf.nn.relu, units=50)
+    if not isinstance(inputs, ops.Tensor):
+        inputs = ops.convert_to_tensor(inputs, dtype='float')
+        # dim_list = inputs.get_shape().as_list()
+        # flatten_shape = dim_list[1] if len(dim_list) <= 2 else reduce(lambda x, y: x * y, dim_list[1:])
+        # reshaped = tf.reshape(inputs, [dim_list[0], flatten_shape])
+    if len(inputs.shape) > 2:
+        inputs = tf.contrib.layers.flatten(inputs)
+    flatten_shape = inputs.shape[1]
+    weights = tf.get_variable('weights', shape=[flatten_shape, units], initializer=w_i)
+    dense = tf.matmul(inputs, weights)
+    if bias_shape is not None:
+        assert bias_shape[0] == units
+        biases = tf.get_variable('biases', shape=bias_shape, initializer=b_i)
+        return activation(dense + biases) if activation is not None else dense + biases
+    return activation(dense) if activation is not None else dense
\ No newline at end of file
diff --git a/basic/.ipynb_checkpoints/class_and_instance-checkpoint.ipynb b/basic/.ipynb_checkpoints/class_and_instance-checkpoint.ipynb
new file mode 100644
index 00000000..2fd64429
--- /dev/null
+++ b/basic/.ipynb_checkpoints/class_and_instance-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/basic/class_and_instance.ipynb b/basic/class_and_instance.ipynb
new file mode 100644
index 00000000..dccf6fd3
--- /dev/null
+++ b/basic/class_and_instance.ipynb
@@ -0,0 +1,1031 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class Animal():\n",
+    "    def __init__(self,name,weight):\n",
+    "        self.name = name\n",
+    "        self.weight = weight\n",
+    "    def print_weight(self):\n",
+    "        print(\"%s : %s\" % (self.name,self.weight))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "dog = Animal('hasky',90)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "90\n",
+      "100\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(dog.weight) #90\n",
+    "dog.weight = 100\n",
+    "print(dog.weight) #100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hasky : 90\n"
+     ]
+    }
+   ],
+   "source": [
+    "class Animal():\n",
+    "    def __init__(self,name,weight):\n",
+    "        self.name = name\n",
+    "        self.weight = weight\n",
+    "    def print_weight(self):\n",
+    "        print(\"%s : %s\" % (self.name,self.weight))\n",
+    "\n",
+    "dog = Animal('hasky',90)\n",
+    "dog.print_weight() # hasky : 90"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hasky\n"
+     ]
+    },
+    {
+     "ename": "AttributeError",
+     "evalue": "'Animal' object has no attribute '__name'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-13-b6aba6167e0d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0mdog\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAnimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'hasky'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m90\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# hasky\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# 'Animal' object has no attribute '__name'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m \u001b[0mdog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_weight\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_weight\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# 100\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'Animal' object has no attribute '__name'"
+     ]
+    }
+   ],
+   "source": [
+    "class Animal():\n",
+    "    def __init__(self,name,weight):\n",
+    "        self.__name = name\n",
+    "        self.__weight = weight\n",
+    "    def print_weight(self):\n",
+    "        print(\"%s : %s\" % (self.name,self.weight))\n",
+    "    def get_name(self):\n",
+    "        return self.__name\n",
+    "    def get_weight(self):\n",
+    "        return self.__weight\n",
+    "    def set_name(self,name):\n",
+    "        self.__name = name\n",
+    "    def set_weight(self,weight):\n",
+    "        self.__weight = weight\n",
+    "\n",
+    "dog = Animal('hasky',90)\n",
+    "print(dog.get_name()) # hasky\n",
+    "print(dog.__name) # AttributeError: 'Animal' object has no attribute '__name'\n",
+    "dog.set_weight(100)\n",
+    "print(dog.get_weight()) # 100\n",
+    "print(dog.__weight) # AttributeError: 'Animal' object has no attribute '__weight'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'_Animal__name': 'hasky', '_Animal__weight': 90}"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dog.__dict__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Animal is running\n",
+      "Dog is eating\n",
+      "Cat is running\n"
+     ]
+    }
+   ],
+   "source": [
+    "class Animal():\n",
+    "    def __init__(self,name,weight):\n",
+    "        self.name = name\n",
+    "        self.weight = weight\n",
+    "    def print_weight(self):\n",
+    "        print(\"%s : %s\" % (self.name,self.weight))\n",
+    "    def run(self):\n",
+    "        print('Animal is running')\n",
+    "\n",
+    "class Dog(Animal):\n",
+    "    def eat(self):\n",
+    "        print('Dog is eating')\n",
+    "class Cat(Animal):\n",
+    "    def run(self):\n",
+    "        print('Cat is running')\n",
+    "\n",
+    "dog = Dog('hasky',10)\n",
+    "dog.run() # Animal is running\n",
+    "dog.eat() # Dog is eating\n",
+    "cat = Cat('tt',20)\n",
+    "cat.run() # Cat is running"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'hasky'"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class Animal():\n",
+    "    def __init__(self,name,weight):\n",
+    "        self.name = name\n",
+    "        self.weight = weight\n",
+    "    def print_weight(self):\n",
+    "        print(\"%s : %s\" % (self.name,self.weight))\n",
+    "    def run(self):\n",
+    "        print('Animal is running')\n",
+    "\n",
+    "class Dog(Animal):\n",
+    "    def __init__(self,name,weight,height):\n",
+    "        super().__init__(name,weight)\n",
+    "        self.height = height\n",
+    "    def eat(self):\n",
+    "        print('Dog is eating')\n",
+    "\n",
+    "dog = Dog('hasky',10,20)\n",
+    "dog.name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n",
+      "True\n",
+      "True\n",
+      "False\n"
+     ]
+    }
+   ],
+   "source": [
+    "class Animal():\n",
+    "    def __init__(self,name,weight):\n",
+    "        self.name = name\n",
+    "        self.weight = weight\n",
+    "    def print_weight(self):\n",
+    "        print(\"%s : %s\" % (self.name,self.weight))\n",
+    "    def run(self):\n",
+    "        print('Animal is running')\n",
+    "\n",
+    "class Dog(Animal):\n",
+    "    def eat(self):\n",
+    "        print('Dog is eating')\n",
+    "\n",
+    "a = Dog(\"hasky\",90)\n",
+    "b = Animal('tt',10)\n",
+    "print(isinstance(a,Dog))# True\n",
+    "print(isinstance(a,Animal))# True\n",
+    "print(isinstance(b,Animal))# True\n",
+    "print(isinstance(b,Dog))# False\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Animal is running\n",
+      "Animal is running\n",
+      "Animal is running\n",
+      "Animal is running\n"
+     ]
+    }
+   ],
+   "source": [
+    "def run_twice(obj):\n",
+    "    obj.run()\n",
+    "    obj.run()\n",
+    "\n",
+    "a = Dog(\"hasky\",90)\n",
+    "b = Animal('tt',10)\n",
+    "run_twice(a)\n",
+    "run_twice(b)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "start....\n",
+      "start....\n"
+     ]
+    }
+   ],
+   "source": [
+    "class Timer(object):\n",
+    "    def run(self):\n",
+    "        print('start....')\n",
+    "\n",
+    "timer = Timer()\n",
+    "run_twice(timer)\n",
+    "# start....\n",
+    "# start...."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['__add__',\n",
+       " '__class__',\n",
+       " '__contains__',\n",
+       " '__delattr__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__getitem__',\n",
+       " '__getnewargs__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__iter__',\n",
+       " '__le__',\n",
+       " '__len__',\n",
+       " '__lt__',\n",
+       " '__mod__',\n",
+       " '__mul__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__rmod__',\n",
+       " '__rmul__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__subclasshook__',\n",
+       " 'capitalize',\n",
+       " 'casefold',\n",
+       " 'center',\n",
+       " 'count',\n",
+       " 'encode',\n",
+       " 'endswith',\n",
+       " 'expandtabs',\n",
+       " 'find',\n",
+       " 'format',\n",
+       " 'format_map',\n",
+       " 'index',\n",
+       " 'isalnum',\n",
+       " 'isalpha',\n",
+       " 'isdecimal',\n",
+       " 'isdigit',\n",
+       " 'isidentifier',\n",
+       " 'islower',\n",
+       " 'isnumeric',\n",
+       " 'isprintable',\n",
+       " 'isspace',\n",
+       " 'istitle',\n",
+       " 'isupper',\n",
+       " 'join',\n",
+       " 'ljust',\n",
+       " 'lower',\n",
+       " 'lstrip',\n",
+       " 'maketrans',\n",
+       " 'partition',\n",
+       " 'replace',\n",
+       " 'rfind',\n",
+       " 'rindex',\n",
+       " 'rjust',\n",
+       " 'rpartition',\n",
+       " 'rsplit',\n",
+       " 'rstrip',\n",
+       " 'split',\n",
+       " 'splitlines',\n",
+       " 'startswith',\n",
+       " 'strip',\n",
+       " 'swapcase',\n",
+       " 'title',\n",
+       " 'translate',\n",
+       " 'upper',\n",
+       " 'zfill']"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir('ABC')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n",
+      "3\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('ABC'.__len__())\n",
+    "print(len('ABC'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n",
+      "20\n"
+     ]
+    }
+   ],
+   "source": [
+    "class Animal():\n",
+    "    def __init__(self,name,weight):\n",
+    "        self.name = name\n",
+    "        self.weight = weight\n",
+    "    def print_weight(self):\n",
+    "        print(\"%s : %s\" % (self.name,self.weight))\n",
+    "    def run(self):\n",
+    "        print('Animal is running')\n",
+    "\n",
+    "dog = Animal('hasky',20)\n",
+    "print(hasattr(dog,'name')) # True\n",
+    "setattr(dog,'height',20)\n",
+    "print(getattr(dog,'height')) # 20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dog\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(getattr(dog,'type','dog')) #dog"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<bound method Animal.run of <__main__.Animal object at 0x104f33cc0>>\n",
+      "Animal is running\n"
+     ]
+    }
+   ],
+   "source": [
+    "f = getattr(dog,'run')\n",
+    "print(f) # <bound method Animal.run of <__main__.Animal object at 0x104f33cc0>>\n",
+    "f() # Animal is running"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20\n"
+     ]
+    }
+   ],
+   "source": [
+    "class Animal():\n",
+    "    def __init__(self,name,weight):\n",
+    "        self.name = name\n",
+    "        self.weight = weight\n",
+    "dog = Animal('hasky',90)\n",
+    "dog.height = 20 \n",
+    "print(dog.height) # 20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ss\n",
+      "ss\n",
+      "aa\n",
+      "ss\n"
+     ]
+    }
+   ],
+   "source": [
+    "class Animal():\n",
+    "    tt = 'ss'\n",
+    "    def __init__(self,name,weight):\n",
+    "        self.name = name\n",
+    "        self.weight = weight\n",
+    "print(Animal.tt) # ss\n",
+    "dog = Animal('hasky',90) \n",
+    "print(dog.tt) # ss\n",
+    "dog.tt = 'aa'\n",
+    "print(dog.tt) # aa\n",
+    "print(Animal.tt)# ss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20\n"
+     ]
+    }
+   ],
+   "source": [
+    "class Animal():\n",
+    "    pass\n",
+    "\n",
+    "ani = Animal()\n",
+    "ani.name = 'hasky'\n",
+    "\n",
+    "from types import MethodType\n",
+    "def set_age(self,age):\n",
+    "    self.age = age\n",
+    "\n",
+    "ani.set_age = MethodType(set_age,ani)\n",
+    "ani.set_age(20)\n",
+    "print(ani.age) #20 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'Animal' object has no attribute 'set_age'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-48-3dee81caeeaa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mani2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAnimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m#print(ani2.name) # AttributeError: 'Animal' object has no attribute 'name'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mani2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_age\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m: 'Animal' object has no attribute 'set_age'"
+     ]
+    }
+   ],
+   "source": [
+    "ani2 = Animal()\n",
+    "print(ani2.name) # AttributeError: 'Animal' object has no attribute 'name'\n",
+    "ani2.set_age(20) # AttributeError: 'Animal' object has no attribute 'set_age'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20\n"
+     ]
+    }
+   ],
+   "source": [
+    "Animal.set_age = set_age\n",
+    "ani2.set_age(20)\n",
+    "print(ani2.age) # 20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'Animal' object has no attribute 'height'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-51-7e68d06a99b8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mani\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'hasky'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mani\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m90\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mani\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m20\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m: 'Animal' object has no attribute 'height'"
+     ]
+    }
+   ],
+   "source": [
+    "class Animal():\n",
+    "    __slots__ = ['name','weight']\n",
+    "    \n",
+    "ani = Animal()\n",
+    "ani.name = 'hasky'\n",
+    "ani.weight = 90\n",
+    "ani.height = 20 # AttributeError: 'Animal' object has no attribute 'height'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20\n"
+     ]
+    }
+   ],
+   "source": [
+    "class Dog(Animal):\n",
+    "    pass\n",
+    "dog = Dog()\n",
+    "dog.height = 20\n",
+    "print(dog.height) # 20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "60\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "weight must between 1 ~ 200!",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-54-25af3c0b5625>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mani\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_weight\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m60\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mani\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_weight\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mani\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_weight\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m999\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-54-25af3c0b5625>\u001b[0m in \u001b[0;36mset_weight\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m      8\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'weight must be an integer!'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m200\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'weight must between 1 ~ 200!'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0mani\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAnimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: weight must between 1 ~ 200!"
+     ]
+    }
+   ],
+   "source": [
+    "class Animal(object):\n",
+    "\n",
+    "    def get_weight(self):\n",
+    "         return self.weight\n",
+    "\n",
+    "    def set_weight(self, value):\n",
+    "        if not isinstance(value, int):\n",
+    "            raise ValueError('weight must be an integer!')\n",
+    "        if value < 1 or value > 200:\n",
+    "            raise ValueError('weight must between 1 ~ 200!')\n",
+    "        self.weight = value\n",
+    "ani = Animal()\n",
+    "ani.set_weight(60)\n",
+    "print(ani.get_weight()) # 60\n",
+    "ani.set_weight(999) # ValueError: weight must between 1 ~ 200!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "60\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "weight must between 1 ~ 200!",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-58-d7e6d566089d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0mani\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m60\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mani\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0mani\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m999\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-58-d7e6d566089d>\u001b[0m in \u001b[0;36mweight\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m      9\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'weight must be an integer!'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m200\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'weight must between 1 ~ 200!'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_weight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: weight must between 1 ~ 200!"
+     ]
+    }
+   ],
+   "source": [
+    "class Animal(object):\n",
+    "    @property\n",
+    "    def weight(self):\n",
+    "         return self._weight\n",
+    "        \n",
+    "    @weight.setter\n",
+    "    def weight(self, value):\n",
+    "        if not isinstance(value, int):\n",
+    "            raise ValueError('weight must be an integer!')\n",
+    "        if value < 1 or value > 200:\n",
+    "            raise ValueError('weight must between 1 ~ 200!')\n",
+    "        self._weight = value\n",
+    "        \n",
+    "ani = Animal()\n",
+    "ani.weight = 60\n",
+    "print(ani.weight) # 60\n",
+    "ani.weight = 999 #  ValueError: weight must between 1 ~ 200!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "60\n"
+     ]
+    }
+   ],
+   "source": [
+    "class Animal(object):\n",
+    "    @property\n",
+    "    def birth(self):\n",
+    "         return self._birth\n",
+    "        \n",
+    "    @birth.setter\n",
+    "    def birth(self, value):\n",
+    "        if not isinstance(value, int):\n",
+    "            raise ValueError('birth must be an integer!')\n",
+    "        if value < 1 or value > 2018:\n",
+    "            raise ValueError('birth must between 1 ~ 2018!')\n",
+    "        self._birth = value\n",
+    "    \n",
+    "    @property\n",
+    "    def age(self):\n",
+    "        return 2018 - self._birth\n",
+    "        \n",
+    "ani = Animal()\n",
+    "ani.birth = 1958\n",
+    "print(ani.age) # 60"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<__main__.Animal object at 0x104fccd68>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(ani) # <__main__.Animal object at 0x104fccd68>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Animal object with name : tt\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<__main__.Animal at 0x104e8ecf8>"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class Animal(object):\n",
+    "    def __init__(self,name):\n",
+    "        self.name = name\n",
+    "    def __str__(self):\n",
+    "        return 'Animal object with name : %s' % self.name\n",
+    "print(Animal('tt')) # Animal object with name : tt\n",
+    "a = Animal('tt')\n",
+    "a # <__main__.Animal at 0x104e8ecf8>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "5\n",
+      "8\n"
+     ]
+    }
+   ],
+   "source": [
+    "class Fib(object):\n",
+    "    def __init__(self):\n",
+    "        self.a, self.b = 0, 1 # 初始化两个计数器a，b\n",
+    "\n",
+    "    def __iter__(self):\n",
+    "        return self # 实例本身就是迭代对象，故返回自己\n",
+    "\n",
+    "    def __next__(self):\n",
+    "        self.a, self.b = self.b, self.a + self.b # 计算下一个值\n",
+    "        if self.a > 10: # 退出循环的条件\n",
+    "            raise StopIteration()\n",
+    "        return self.a # 返回下一个值\n",
+    "for n in Fib():\n",
+    "    print(n)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2, 3, 5]\n",
+      "89\n"
+     ]
+    }
+   ],
+   "source": [
+    "class Fib(object):\n",
+    "    def __getitem__(self, n):\n",
+    "        if isinstance(n, int): # n是索引\n",
+    "            a, b = 1, 1\n",
+    "            for x in range(n):\n",
+    "                a, b = b, a + b\n",
+    "            return a\n",
+    "        if isinstance(n, slice): # n是切片\n",
+    "            start = n.start\n",
+    "            stop = n.stop\n",
+    "            if start is None:\n",
+    "                start = 0\n",
+    "            a, b = 1, 1\n",
+    "            L = []\n",
+    "            for x in range(stop):\n",
+    "                if x >= start:\n",
+    "                    L.append(a)\n",
+    "                a, b = b, a + b\n",
+    "            return L\n",
+    "f = Fib()\n",
+    "print(f[2:5]) # [1, 1, 2, 3, 5]\n",
+    "print(f[10]) # 89"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20\n"
+     ]
+    }
+   ],
+   "source": [
+    "class Animal():\n",
+    "    def __init__(self,name,weight):\n",
+    "        self.name = name\n",
+    "        self.weight = weight\n",
+    "    def __getattr__(self,attr):\n",
+    "        if attr == 'height':\n",
+    "            return 20\n",
+    "a = Animal(\"hasky\",90)\n",
+    "print(a.height) # 20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20\n",
+      "25\n"
+     ]
+    }
+   ],
+   "source": [
+    "class Animal():\n",
+    "    def __init__(self,name,weight):\n",
+    "        self.name = name\n",
+    "        self.weight = weight\n",
+    "    def __getattr__(self,attr):\n",
+    "        if attr == 'height':\n",
+    "            return 20\n",
+    "        elif attr == 'age':\n",
+    "            return lambda: 25\n",
+    "a = Animal(\"hasky\",90)\n",
+    "print(a.height) # 20\n",
+    "print(a.age()) # 25"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "Animal object has no attribute abc",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-78-a143cd822444>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Animal object has no attribute %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mattr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAnimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"hasky\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m90\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabc\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# None\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-78-a143cd822444>\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, attr)\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mattr\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'age'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;32mlambda\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m25\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Animal object has no attribute %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mattr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAnimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"hasky\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m90\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabc\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# None\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: Animal object has no attribute abc"
+     ]
+    }
+   ],
+   "source": [
+    "class Animal():\n",
+    "    def __init__(self,name,weight):\n",
+    "        self.name = name\n",
+    "        self.weight = weight\n",
+    "    def __getattr__(self,attr):\n",
+    "        if attr == 'height':\n",
+    "            return 20\n",
+    "        elif attr == 'age':\n",
+    "            return lambda: 25\n",
+    "        raise AttributeError('Animal object has no attribute %s' % attr)\n",
+    "a = Animal(\"hasky\",90)\n",
+    "print(a.abc) # AttributeError: Animal object has no attribute abc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}