import os os.environ['TF_CPP_MIN_LOG_LEVEL']='2' import tensorflow as tfmatrix1 = tf.constant([[3,3]])matrix2 = tf.constant([[2],[2]])protuct = tf.matmul(matrix1,matrix2) # sess = tf.Session()# result = sess.run(protuct) # print(result)# sess.close()…

import tensorflow as tfimport numpy as np ##save to file#rember to define the same dtype and shape when restore# W = tf.Variable([[1,2,3],[3,4,5]],dtype=tf.float32,name='Weights')# b = tf.Variable([[1,2,3]],dtype=tf.float32,name='biases') # init = tf…

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

import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_data#number 1 to 10 datamnist = input_data.read_data_sets('MNIST_data',one_hot=True) def add_layer(inputs,in_size,out_size,activation_function=None): Weights = tf.Variable(t…

import tensorflow as tfimport 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'): with tf.name_sco…

import tensorflow as tfimport numpy as npimport 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]) + 0.1) W…

import tensorflow as tf input1 = tf.placeholder(tf.float32)input2 = tf.placeholder(tf.float32) output = tf.multiply(input1,input2) with tf.Session() as sess: print(sess.run(output,feed_dict={input1:[7.],input2:[2.0]}))…

import tensorflow as tf state = tf.Variable(0,name='counter') one = tf.constant(1) new_value = tf.add(state,one)update = tf.assign(state,new_value) init = tf.initialize_all_variables()#must have if define variable with tf.Session() as sess: sess.run(…

import tensorflow as tfimport numpy as np #create datax_data = np.random.rand(100).astype(np.float32)y_data = x_data*0.1+0.3 ####create tensorflow structure start###Weights = tf.Variable(tf.random_uniform([1],-1.0,1.0))biases = tf.Variable(tf.zeros([…

bilibili莫烦tensorflow视频教程学习笔记 1.初次使用Tensorflow实现一元线性回归 # 屏蔽警告 import os os.environ[' import numpy as np import tensorflow as tf # create dataset x_data = np.random.rand(100).astype(np.float32) y_data = x_data * 2 + 5 ### create tensorflow structure St…

1,感谢莫烦 2,第一个实例:用tf拟合线性函数 import tensorflow as tf import numpy as np # create data x_data = np.random.rand(100).astype(np.float32) y_data = x_data*0.1 + 0.3 #先创建我们的线性函数目标 #搭建模型 Weights = tf.Variable(tf.random_uniform([1], -1.0, 1.0)) biases = tf.Varia…

  一.Matplotlib[结果可视化] #import os #os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' import tensorflow as tf import numpy as np import matplotlib.pyplot as plt #添加一个神经层,定义添加神经层的函数 def add_layer(inputs, in_size, out_size, activation_function = None): Weights =…

稍稍乱入的CNN,本文依然是学习周莫烦视频的笔记. 还有 google 在 udacity 上的 CNN 教程. CNN(Convolutional Neural Networks) 卷积神经网络简单讲就是把一个图片的数据传递给CNN,原涂层是由RGB组成,然后CNN把它的厚度加厚,长宽变小,每做一层都这样被拉长,最后形成一个分类器: 如果想要分成十类的话,那么就会有0到9这十个位置,这个数据属于哪一类就在哪个位置上是1,而在其它位置上为零. 在 RGB 这个层,每一次把一块核心抽出来,然后厚度…

滴:转载引用请注明哦[握爪] https://www.cnblogs.com/zyrb/p/9700343.html 莫烦教程是一个免费的机器学习(不限于)的学习教程,幽默风俗的语言让我们这些刚刚起步的小白们感到Friendly~o(*￣︶￣*)o.为了巩固自己的记忆,也小小の贡献于他人,将莫烦教程进行整理.难免于有错误恳请批评指正,也希望自己始终能愉悦的学习!PS:大多数为整理文本,少部分添加自己的理解. Artificial Neural Nets VS Neural Nets ? 二三十年…

bilibili莫烦scikit-learn视频学习笔记 1.使用KNN对iris数据分类 from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier # 从datasets中导入iris数据,包含150条样本,每条样本4个feature iris_data = datasets.load_i…

莫烦视频网址 这个代码实现了预测和可视化 import os # third-party library import torch import torch.nn as nn import torch.utils.data as Data import torchvision import matplotlib.pyplot as plt # torch.manual_seed() # reproducible # Hyper Parameters EPOCH = # train the tra…

各种优化器的比较 莫烦的对各种优化通俗理解的视频 import torch import torch.utils.data as Data import torch.nn.functional as F from torch.autograd import Variable import matplotlib.pyplot as plt # 超参数 LR = 0.01 BATCH_SIZE = EPOCH = # 生成假数据 # torch.unsqueeze() 的作用是将一维变二维,torc…

import torch from torch.autograd import Variable import matplotlib.pyplot as plt torch.manual_seed() # fake data x = torch.unsqueeze(torch.linspace(-,,),dim=) y = x.pow() + 0.2 * torch.rand(x.size()) x, y = Variable(x,requires_grad=False), Variable(y…

import tensorflow as tf matrix1 = tf.constant([[3,3]]) # 1X2 matrix2 = tf.constant([[2], [2]]) product = tf.matmul(matrix1, matrix2) #method 1 #sess = tf.Session() #result = sess.run(product) #print(result) #sess.close() #method 2 with tf.Session() a…

Tensorflow 简介 1.1 科普: 人工神经网络 VS 生物神经网络 1.2 什么是神经网络 (Neural Network) 1.3 神经网络 梯度下降 1.4 科普: 神经网络的黑盒不黑 1.5 为什么选 Tensorflow? 1.6 Tensorflow 安装 1.7 神经网络在干嘛 Tensorflow 基础构架 2.1 处理结构 2.2 例子2 2.3 Session 会话控制 2.4 Variable 变量 2.5 Placeholder 传入值 2.6 什么是激励函数 (…

一.TensorFlow中的优化器 tf.train.GradientDescentOptimizer:梯度下降算法 tf.train.AdadeltaOptimizer tf.train.AdagradOptimizer tf.train.MomentumOptimizer:动量梯度下降算法 tf.train.AdamOptimizer:自适应矩估计优化算法 tf.train.RMSPropOptimizer tf.train.AdagradDAOptimizer tf.train.FtrlO…

转载自: http://blog.csdn.net/Hanging_Gardens/article/details/72784392 https://www.cnblogs.com/hypnus-ly/p/8040951.html 会话Session持有并管理tensorflow程序运行时的所有资源.tensorflow构建的计算图Graph必须通过Session会话才能执行,如果只是在计算图中定义了图的节点但没有使用Session会话的话,就不能运行该节点. 调用会话的三种方式: 方式一:明确…

Exception ignored in: <bound method BaseSession.__del__ of <tensorflow.python.client.session.Session object at 0x7fd3edd13e10>> Traceback (most recent call last): File "venv/lib/python3.5/site-packages/tensorflow/python/client/session.py&…

import tensorflow as tf import numpy as np # create data x_data = np.random.rand(100).astype(np.float32) y_data = x_data*0.1 + 0.3 ### create tensorflow structure start ### Weights = tf.Variable(tf.random_uniform([1], -1.0, 1.0)) biases = tf.Variable…

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, fee…

import tensorflow as tf from sklearn.datasets import load_digits from sklearn.cross_validation import train_test_split from sklearn.preprocessing import LabelBinarizer #load data digits = load_digits() X = digits.data y = digits.target y = LabelBinar…

import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets('MNIST_data', one_hot = True) # # add layer # def add_layer(inputs, in_size, out_size, activation_function = None): Weights = tf.Vari…

import tensorflow as tf import numpy as np import matplotlib.pyplot as plt # # add layer # def add_layer(inputs, in_size, out_size,n_layer, activation_function = None): layer_name = 'layer%s' % n_layer with tf.name_scope(layer_name): with tf.name_sco…

import tensorflow as tf import numpy as np import matplotlib.pyplot as plt def add_layer(inputs, in_size, out_size, activation_function = None): with tf.name_scope('layer'): with tf.name_scope('Weights'): Weights = tf.Variable(tf.random_normal([in_si…

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])) # hang lie biases = tf.Variable(tf.zeros([1,…