import tensorflow as tf import numpy as np W = tf.Variable(np.arange(6).reshape((2, 3)), dtype=tf.float32, name="weights") b = tf.Variable(np.arange(3).reshape((1, 3)), dtype=tf.float32, name="biases") saver = tf.train.Saver() with tf.…

import tensorflow as tf import numpy as np W = tf.Variable([[2,1,8],[1,2,5]], dtype=tf.float32, name='weights') b = tf.Variable([[1,2,5]], dtype=tf.float32, name='biases') init= tf.global_variables_initializer() saver = tf.train.Saver() with tf.Sessi…

import os os.environ[' import tensorflow as tf import numpy as np x_data = np.random.rand(100).astype(np.float32) y_data = x_data*0.1 + 0.3 Weights = tf.Variable(tf.random_uniform([1], -1.0, 1.0)) biases = tf.Variable(tf.zeros([1])) y = Weights*x_dat…

import tensorflow as tf import numpy as np import matplotlib.pyplot as plt BATCH_START = 0 TIME_STEPS = 20 BATCH_SIZE = 50 INPUT_SIZE = 1 OUTPUT_SIZE = 1 CELL_SIZE = 10 LR = 0.006 BATCH_START_TEST = 0 def get_batch(): global BATCH_START, TIME_STEPS #…

import tensorflow as tf import numpy as np import matplotlib.pyplot as plt BATCH_START = 0 TIME_STEPS = 20 BATCH_SIZE = 50 INPUT_SIZE = 1 OUTPUT_SIZE = 1 CELL_SIZE = 10 LR = 0.006 BATCH_START_TEST = 0 def get_batch(): global BATCH_START, TIME_STEPS x…

load spectra; temp = randperm(size(NIR, 1)); P_train = NIR(temp(1:50),:); T_train = octane(temp(1:50),:); P_test = NIR(temp(51:end),:); T_test = octane(temp(51:end),:); k = 2; [Xloadings,Yloadings,Xscores,Yscores,betaPLS,PLSPctVar,MSE,stats] = plsreg…

load spectra; temp = randperm(size(NIR, 1)); P_train = NIR(temp(1:50),:); T_train = octane(temp(1:50),:); P_test = NIR(temp(51:end),:); T_test = octane(temp(51:end),:); [PCALoadings,PCAScores,PCAVar] = princomp(NIR); figure percent_explained = 100 *…

#Py之Crawler:爬虫利用随机选取代理访问服务器的方法实现下载某网址上所有的图片到指定文件夹 import urllib.request import os import random def open_url(url): req=urllib.request.Request(url) req.add_header("User-Agent", "Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML,…

1. tf.train.Saver() tf.train.Saver()是一个类,提供了变量.模型(也称图Graph)的保存和恢复模型方法. TensorFlow是通过构造Graph的方式进行深度学习,任何操作(如卷积.池化等)都需要operator,保存和恢复操作也不例外. 在tf.train.Saver()类初始化时,用于保存和恢复的save和restore operator会被加入Graph.所以,下列类初始化操作应在搭建Graph时完成. saver = tf.train.Saver()…

import tensorflow as tf import tensorflow.contrib.slim as slim import rawpy import numpy as np import tensorflow as tf import struct import glob import os from PIL import Image import time __sony__ = 0 __huawei__ = 1 __blackberry__ = 2 __stage_raw2ra…

1.保存 将训练好的模型参数保存起来,以便以后进行验证或测试.tf里面提供模型保存的是tf.train.Saver()模块. 模型保存,先要创建一个Saver对象:如 saver=tf.train.Saver() 在创建这个Saver对象的时候,有一个参数经常会用到,max_to_keep 参数,这个是用来设置保存模型的个数,默认为5,即 max_to_keep=5,保存最近的5个模型.如果想每训练一代(epoch)就想保存一次模型,则可以将 max_to_keep设置为None或者0,但是这样…

save =  tf.train.Saver() 通过save. save() 实现数据的加载 通过save.restore() 实现数据的导出 第一步: 数据的载入 import tensorflow as tf #创建变量 v1 = tf.Variable(tf.random_normal([1, 2], name='v1')) v2 = tf.Variable(tf.random_normal([2, 3], name='v2')) #初始化变量 init_op = tf.global_v…

将训练好的模型参数保存起来,以便以后进行验证或测试.tf里面提供模型保存的是tf.train.Saver()模块. 模型保存,先要创建一个Saver对象:如 saver=tf.train.Saver() 在创建这个Saver对象的时候,有一个参数我们经常会用到,就是 max_to_keep 参数,这个是用来设置保存模型的个数,默认为5,即 max_to_keep=5,保存最近的5个模型.如果你想每训练一代(epoch)就想保存一次模型,则可以将 max_to_keep设置为None或者0,如:…

  TensorFlow Saver 保存最佳模型 tf.train.Saver Save Best Model Checkmate is designed to be a simple drop-in solution for a very common Tensorflow use-case: keeping track of the best model checkpoints during training. The BestCheckpointSaver is a wrapper ar…

1. 实例化对象 saver = tf.train.Saver(max_to_keep=1) max_to_keep: 表明保存的最大checkpoint文件数.当一个新文件创建的时候,旧文件就会被删掉.如果值为None或0, 表示保存所有的checkpoint文件.默认值5(也就是说,保存最近的5个checkpoint文件). keep_checkpoint_every_n_hour: 除了保存最近的max_to_keep_checkpoint文件,你还可能想每训练N小时保存一个checkpo…

import tensorflow as tf # 22 scope (name_scope/variable_scope) from __future__ import print_function class TrainConfig: batch_size = 20 time_steps = 20 input_size = 10 output_size = 2 cell_size = 11 learning_rate = 0.01 class TestConfig(TrainConfig):…

import tensorflow as tf from sklearn.datasets import load_digits #from sklearn.cross_validation import train_test_split from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelBinarizer # load data digits = load_di…

1. tf.layers.conv2d(input, filter, kernel_size, stride, padding) # 进行卷积操作 参数说明:input输入数据, filter特征图的个数,kernel_size卷积核的大小,stride步长,padding是否补零 2. tf.layers.conv2d_transpose(input, filter, kernel_size, stride, padding) # 进行反卷积操作 参数说明:input输入数据, filter特…

TensorFlow tf.app&tf.app.flags用法介绍 TensorFlow tf.app argparse  tf.app.flags 下面介绍 tf.app.flags.FLAGS的使用,主要是在用命令行执行程序时,需要传些参数,其实也就可以理解成对argparse库进行的封装,示例代码如下 #coding:utf-8  # 学习使用 tf.app.flags 使用,全局变量  # 可以再命令行中运行也是比较方便,如果只写 python app_flags.py 则代码运行时默…

import tensorflow as tf import numpy as np import matplotlib.pyplot as plt #Import MNIST data from tensorflow.examples.tutorials.mnist import input_data mnist=input_data.read_data_sets("/niu/mnist_data/",one_hot=False) # Parameter learning_rate…

import tensorflow as tf import numpy as np import matplotlib.pyplot as plt #Import MNIST data from tensorflow.examples.tutorials.mnist import input_data mnist=input_data.read_data_sets("/niu/mnist_data/",one_hot=False) # Parameter learning_rate…

import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets('MNIST_data', one_hot=True) lr=0.001 training_iters=100000 batch_size=128 n_inputs=28 n_steps=28 n_hidden_units=128 n_classes=10 x=tf…

import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data # number 1 to 10 data mnist = input_data.read_data_sets('MNIST_data', one_hot=True) def compute_accuracy(v_xs, v_ys): global prediction y_pre = sess.run(prediction, fe…

import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data # number 1 to 10 data mnist = input_data.read_data_sets('MNIST_data', one_hot=True) def add_layer(inputs, in_size, out_size, activation_function=None,): # add one more…

import tensorflow as tf import numpy as np def add_layer(inputs, in_size, out_size, n_layer, activation_function=None): # add one more layer and return the output of this layer layer_name = 'layer%s' % n_layer with tf.name_scope(layer_name): with tf.…

import tensorflow as tf import numpy as np import matplotlib.pyplot as plt def add_layer(inputs, in_size, out_size, activation_function=None): Weights = tf.Variable(tf.random_normal([in_size, out_size])) biases = tf.Variable(tf.zeros([1, out_size]) +…

作 者:marsggbo 出 处:https://www.cnblogs.com/marsggbo版权声明:署名 - 非商业性使用 - 禁止演绎,协议普通文本 | 协议法律文本. --------------------------------------------------------------------------------------------------------------- Tensorflow       tf.app  &  tf.app.flags    用法介绍…

referrence: 莫烦视频 先介绍几个函数 1.tf.cast() 英文解释: 也就是说cast的直译,类似于映射,映射到一个你制定的类型. 2.tf.argmax 原型: 含义:返回最大值所在的坐标.(谁给翻译下最后一句???) ps:谁给解释下axis最后一句话? 例子: 3.tf.reduce_mean() 原型: 含义:一句话来说就是对制定的reduction_index进行均值计算. 注意,reduction_indices为0时,是算的不同的[]的同一个位置上的均值 为1是是算…

以下这些函数可以用于解决梯度消失或梯度爆炸问题上. 1. tf.clip_by_value tf.clip_by_value( t, clip_value_min, clip_value_max, name=None ) Returns:A clipped Tensor. 输入一个张量t,把t中的每一个元素的值都压缩在clip_value_min和clip_value_max之间.小于min的让它等于min,大于max的元素的值等于max. 例子: import tensorflow as tf…

#TF:TF定义两个变量相乘之placeholder先hold类似变量+feed_dict最后外界传入值 import tensorflow as tf input1 = tf.placeholder(tf.float32) #TF一般只能处理float32的数据类型 input2 = tf.placeholder(tf.float32) #ouput = tf.mul(input1, input2) ouput = tf.multiply(input1, input2) #定义两个变量相乘 w…