tensorflow machine learning cookbook

learning and unsupervised learning. There are only few tags (rewards) and there is a delay. Model learning environment behavior. Games, playing games, and games have multiple steps to make continuous decisions. Q-learning, Sarsa, Policy Gradient, Actor Critic. Including algorithm update and decision-making. Deep Q Net

training and distributed training can be very simple to switch, and in the use of different devices: CPU, GPU, TPU, no need to modify too much code. The estimator framework is clear and facilitates communication between developers. Beginners can also directly use some of the estimator models that have been built: DNN models, xgboost models, linear models, and so on. Three, TensorFlow serving and performance optimization 3.1

BatchNp. random. shuffle (test_indices)Test_indices = test_indices [0: test_size]Print (I, np. mean (np. argmax (teY [test_indices], axis = 1) =Sess. run (predict_op, feed_dict = {X: teX [test_indices],P_keep_conv: 1.0,P_keep_hidden: 1.0 }))) MNIST Recurrent Neural Network. Https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/recurrent_network.py. RNN has been successfully applied in the natural language processi

progress of the algorithm, but also because the deep learning technology has achieved very good application effect in all walks of life. deep Learning, as a combination of theory and practice, has emerged in the new algorithm theory, and various deep learning frameworks have been appearing in people's Field of vision. Like Torch,mxnet,theano,caffe and so on. Goo

= 'number of steps to run trainer .')Parser. add_argument ('-- learning_rate', type = float, default = 0.001,Help = 'initial learning rate ')Parser. add_argument ('-- dropout', type = float, default = 0.9,Help = 'keep probability for training dropout .')Parser. add_argument ('-- Data_dir ',Type = str,Default = OS. path. join (OS. getenv ('test _ tmpdir', '/tmp '),'Tensorflow/mnist/input_data '),Help = 'Dir

TensorFlow TensorFlow (Tengsanfo) is Google based on the development of the second generation of artificial intelligence learning system, its name comes from its own operating principles. Tensor (tensor) means n-dimensional arrays, flow (stream) means the computation based on data flow diagram, TensorFlow flows from on

timeline for the last loop and export to json to view with # chrome: // tracing /. # create a timeline file in the last loop and use chrome: // tracing/to open the analysis if I = train_loops-1: sess. run (train_step, feed_dict = {x: batch_xs, y _: batch_ys}, options = tf. runOptions (trace_level = tf. runOptions. FULL_TRACE), run_metadata = run_metadata) trace = timeline. timeline (step_stats = run_metadata.step_stats) with open ('timeline. ctf. json ', 'w') as trace_file: trace_file.write (tr

Install the deep learning framework TensorFlow in Ubuntu I recently learned about TensorFlow, a new open-source deep learning framework for Google. It was found that python 2.7.x is needed when installing it; I have been using CentOS for Linux before. While CentOS is not updated, the built-in Python is usually less tha

Learning notes TF024: TensorFlow achieves Softmax Regression (Regression) Recognition of handwritten numbersTensorFlow implements Softmax Regression (Regression) to recognize handwritten numbers. MNIST (Mixed National Institute of Standards and Technology database), simple machine vision dataset, 28x28 pixels handwritten number, only grayscale value information,

Learning notes TF024: TensorFlow achieves Softmax Regression (Regression) Recognition of handwritten numbers, tf024softmax TensorFlow implements Softmax Regression (Regression) to recognize handwritten numbers. MNIST (Mixed National Institute of Standards and Technology database), simple machine vision dataset, 28x28 p

Reproduced in the Daily Digest of deep learning, convlstm principle and its tensorflow realizationThis document references convolutional LSTM network:a machine learning approach forPrecipitation nowcasting Today introduced a very famous network structure--convlstm, it not only has the LSTM time series modelling ability

tags (space delimited): Wang Cao TensorFlow notes Note-taker: Wang GrassNote Finishing Time February 24, 2017TensorFlow official English document address: Https://www.tensorflow.org/get_started/mnist/beginnersOfficial documents When this article was compiled last updated: February 15, 2017 1. Case Background This article is followed by the second tutorial of the official TensorFlow document – Identifying ha

= Mnist.train.next_batch (batch_size)Sess.run (Optimizer, feed_dict={x:batch_x, y:batch_y, keep_prob:dropout})If step% Display_step = = 0:# Calculate loss value and accuracy, outputLoss, acc = Sess.run ([cost, accuracy], feed_dict={x:batch_x, Y:batch_y, Keep_prob:1.})Print "Iter" + str (step*batch_size) + ", Minibatch loss=" + "{:. 6f}". Format (Loss) + ", Training accuracy=" + "{:. 5f}". f Ormat (ACC)Step + = 1Print "Optimization finished!"# Calculate Test AccuracyPrint "Testing accuracy:", se

1. General machine learning predictive function coefficient (y=0.1x+0.3) #-*-CODING:GBK-*- import tensorflow as tf import numpy as NP #生成数据, y=0.1x+0.3 X_data=np.random.rand ( Astype (np.float32) y_data=x_data*0.1+0.3 # # #开始创建tensorflow结构 ### WEIGHT=TF. Variable (Tf.random_uniform ([1],-1.0,1.0)) BIASES=TF. Variabl

rate, the higher the accuracy. Mini-batch size. The size of each batch determines the weight update rules. The average value is obtained and the weight is updated only after the entire batch of sample gradients are calculated. The higher the batch, the faster the training speed. The matrix and linear algebra libraries are used for acceleration, and the weight update frequency is low. The smaller the batch, the slower the training speed. Set the machine