In [6]:
import tensorflow as tf
import numpy as np
In [2]:
import time
time.strftime('%m%d',time.localtime(time.time()))
Out[2]:
In [18]:
import pandas as pd
data = pd.read_excel('/home/jeffmxh/IMG_Inception/mid_end_incp.xlsx')
dict(data.groupby('prediction').size())
import os
os.system('ls -hl')
Out[18]:
In [23]:
import re
data = pd.read_excel('/home/jeffmxh/IMG_Inception/mid_end_incp.xlsx')
def re_blank(x):
x = re.sub(', ', ',', x)
x = re.sub(' ', '_', x)
return x
data['prediction'] = data['prediction'].apply(re_blank)
result = list(data.prediction)
In [31]:
from pickle import load
import re
In [32]:
data = load(open("/home/da/nlp/ltp_test/seq2seq_inputs.pkl", 'rb'))
In [37]:
data.shape[0]/1024*50
Out[37]:
In [18]:
content = data.data
label = data.label
In [33]:
content_str = [' '.join(a) + '\n' for a in content]
label_str = [' '.join(a) + '\n' for a in content]
content_train = content_str[0:250000]
content_dev = content_str[250000:]
label_train = label_str[0:250000]
label_dev = label_str[250000:]
In [34]:
content_train[0]
Out[34]:
In [35]:
with open('Jeffmxh/content_train.txt', 'w') as f:
f.writelines(content_train)
with open('Jeffmxh/content_dev.txt', 'w') as f:
f.writelines(content_dev)
with open('Jeffmxh/label_train.txt', 'w') as f:
f.writelines(label_train)
with open('Jeffmxh/label_dev.txt', 'w') as f:
f.writelines(label_dev)
In [4]:
s = 'asdfa\000sfwafwf'
import re
ILLEGAL_CHARACTERS_RE = re.compile(r'[\000-\010]|[\013-\014]|[\016-\037]')
m = ILLEGAL_CHARACTERS_RE.sub('', s)
m
Out[4]:
In [2]:
import os
import re
a = os.listdir('/home/jeffmxh/shiny_shape/',)
pattern = re.compile('.+?.jpg$')
pattern.match(a[0])
list(filter(pattern.match,a))
Out[2]:
In [4]:
class info():
def __init__(self, name):
self.name = name
@property
def show_name(self):
return 'Name : ' + str(self.name)
@staticmethod
def show_params():
print('This is staticmethod')
In [5]:
a = info('Jeff')
print(a.show_name)
info.show_params()
print(isinstance(a, info))
In [ ]:
def log(func):
def _log(a, b):
print("before myfunc() called.")
ret = func(a, b)
print(" after myfunc() called. result: %s" % ret)
return ret
return _log
@log
de
In [33]:
elems = np.array([1, 2, 3, 4, 5, 6])
initializer = np.array(0)
sum_one = tf.scan(
lambda a, x: x[0] + x[1] + a, (elems + 1, elems), initializer)
with tf.Session() as sess:
result = sess.run(sum_one)
print(result)
# f = lambda a, x: x[0] - x[1] + a
(elems + 1, elems)
Out[33]:
In [48]:
import time
print(time.asctime( time.localtime(time.time()) ))
In [46]:
elems = np.array([0, 0, 0, 0, 0, 0])
initializer = (np.array(1), np.array(2))
fibonaccis = tf.scan(lambda a, _: (a[1], a[0] + a[1]), elems, initializer)
with tf.Session() as sess:
result = sess.run(fibonaccis)
print(result)
initializer
Out[46]:
In [2]:
matrix1 = tf.constant([[3.,3.]])
matrix2 = tf.constant([[2.], [2.]])
product = tf.matmul(matrix1, matrix2)
In [6]:
with tf.Session() as sess:
result = sess.run(product)
print(result)
In [10]:
ckpt = tf.train.get_checkpoint_state('/home/jeffmxh/deepnlp/deepnlp/textsum/ckpt/')
if not ckpt:
print("aaa")
In [11]:
x_data = np.random.rand(100).astype('float32')
y_data = x_data*0.1+0.3
In [12]:
W = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
b = tf.Variable(tf.zeros([1]))
y = W * x_data + b
init = tf.global_variables_initializer()
In [13]:
# 最小化均方误差
loss = tf.reduce_mean(tf.square(y - y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)
In [14]:
with tf.Session() as sess:
sess.run(init)
for step in range(200):
sess.run(train)
if step % 20 == 0:
print(step, sess.run(W), sess.run(b))
In [15]:
# 使用 NumPy 生成假数据(phony data), 总共 100 个点.
x_data = np.float32(np.random.rand(2, 100)) # 随机输入
y_data = np.dot([0.100, 0.200], x_data) + 0.300
In [16]:
y_data
Out[16]:
In [17]:
# 构造一个线性模型
#
b = tf.Variable(tf.zeros([1]))
W = tf.Variable(tf.random_uniform([1, 2], -1.0, 1.0))
y = tf.matmul(W, x_data) + b
In [18]:
# 最小化方差
loss = tf.reduce_mean(tf.square(y - y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)
In [19]:
# 初始化变量
init = tf.global_variables_initializer()
# 启动图 (graph)
with tf.Session() as sess:
sess.run(init)
for step in range(200):
sess.run(train)
if step % 20==0:
print(step, sess.run(W), sess.run(b))