InnerPeace-Wu

import tensorflow as tf

# 1
# add it in one line, NOTE: we need lanch a session before.
writer = tf.summary.FileWriter('path/to/file/name', sess.graph)

# 2
writer = tf.summary.FileWriter('path/to/file/name')
#somewhere after lanching the session to add graph. NOTE: add it later.
writer.add_graph(sess.graph)

InnerPeace-Wu

import tensorflow as tf

def tf_attention():
    a = tf.constant([[2,3],[4,5]])
    b = tf.constant([[[1,1,1],[2,2,2]],[[3,3,3],[4,4,4]]])
    ##
    #what we want is: [[[2,2,2],[6,6,6]],[[12,12,12],[20,20,20]]]
    #then sum it over dimension-1 to get: [[ 8  8  8], [32 32 32]]
    ##
    d = tf.expand_dims(a, 2)

InnerPeace-Wu

import tensorflow as tf

#with default settings
def scope_defsetting():
    with tf.variable_scope('qa', initializer=tf.zeros_initializer()):
        #tf.get_variable_scope().reuse_variables()
        # if without setting initializer in get_varaible, use the default one in the scope.
        a = tf.get_variable('a', [2,4], tf.float32)
        #tf.get_variable_scope().reuse == True
        with tf.Session() as sess:

InnerPeace-Wu

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import math

from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import linalg_ops

InnerPeace-Wu

import tensorflow as tf

def get_v_names():
    tf.reset_default_graph()
    with tf.variable_scope('test'):
        a = tf.Variable(tf.ones([2]),tf.float32, name='a')
        b = tf.Variable(tf.zeros([3]), dtype=tf.float32, name='b',trainable=False)
    # GLOBAL_VARIABLES: get all variable, e.g. a & b
    # GLOBAL_TRAINABLE: get trainable variable, e.g. a
    for i in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES):#TRAINABLE

InnerPeace-Wu

import tensorflow as tf

#aplly exponential decay on learning rate
global_step = tf.Variable(0, trainable=False)
stater_learning_rate = lr #for start
learning_rate = tf.train.exponential_decay(starter_learning_rate, global_step, 
                                           decay_steps, decay_rate, staircase=True)

optimizer = tf.train.AdamOptimizer(learning_rate)

InnerPeace-Wu

import matplotlib.pyplot as plt

fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(16, 9))
plt.subplot(1, 2, 1)
plt.hist(data, normed=True, bins=30)
plt.subplot(1, 2, 2)
#plot things...
plt.xlabel('x')
plt.ylabel('y')
plt.title('tile')

InnerPeace-Wu

import tensorflow as tf

class testCell(tf.nn.rnn_cell.RNNCell):

    def __init__(self, input_size=1, state_size=1):
        self.input_size = input_size
        self._state_size = state_size

    @property
    def state_size(self):

InnerPeace-Wu

# ----------------------------------------------
# DenseCap
# Written by InnerPeace
# ----------------------------------------------

"""read large region description json file"""

import ijson
import json
import sys

InnerPeace-Wu

let g:python_host_prog='/usr/local/bin/python'

set nocompatible
filetype off
let mapleader=";"

set relativenumber number " 相对行号
au FocusLost * :set norelativenumber number
au FocusGained * :set relativenumber
" 插入模式下用绝对行号, 普通模式下用相对