tensorflow neural network

Learning notes TF057: TensorFlow MNIST, convolutional neural network, recurrent neural network, unsupervised learning, tf057tensorflow MNIST convolutional neural network. Https://github

TensorFlow Neural Network Optimization Strategy Learning, tensorflow Network Optimization During the optimization of the neural network model, we will encounter many problems, such as h

Code (with detailed comments for source code) and dataset can be downloaded in github: Https://github.com/crazyyanchao/TensorFlow-HelloWorld #-*-Coding:utf-8-*-' convolution neural network test mnist data ' ######## #导入MNIST数据 ######## from Tensorflow.examples.tutorials.mnist Import input_data import TensorFlow as t

01 Simple Convolution network # "TensorFlow Combat" TensorFlow realize convolution neural network # WIN10 Tensorflow1.0.1 python3.5.3 # CUDA v8.0 cudnn-8.0-windows10-x64-v5.1 # Filen ame:sz05.01.py # Simple convolution network

(): Image_size= 224Images=TF. Variable (Tf.random_normal ([Batch_size, Image_size, Image_size,3], Dtype=Tf.float32, StdDev=1e-1)) Keep_prob=Tf.placeholder (tf.float32) predictions, Softmax, FC8, p=inference_op (images, keep_prob) init=tf.global_variables_initializer () config=TF. Configproto () Config.gpu_options.allocator_type='BFC'Sess= TF. Session (config=config) sess.run (init) time_tensorflow_run (sess, predictions, {keep_prob:1.0},"Forward") O

model, which uses the class (cifar10.py and cifar10_input.py) to read CTFAR-10 data (CTFAR-10 a classic dataset) when building models git clone git@github.com:tensorflow/models.git Convolution neural network structure:CONV1 convolution layer and activation functionMaximum pool of pool1Norm1 LRNConv2 convolution layer and activation functionNorm2 LRNMaximum pool layer of pool2LOCAL3 full join layer and ac

6th Chapter Image Recognition and convolution neural network 6.1 image recognition problems and the classic data set 6.2 convolution neural network introduction 6.3 convolutional neural network common structure 6.3.1 convolution l

Learning notes TF053: Recurrent Neural Network, TensorFlow Model Zoo, reinforcement learning, deep forest, deep learning art, tf053tensorflow Recurrent Neural Networks. Bytes. Natural language processing (NLP) applies the network model. Unlike feed-forward

TensorFlow implements RNN Recurrent Neural Network, tensorflowrnn RNN (recurrent neural Network) recurrent neural Network It is mainly used for natural language processing (NLP) RNN is

original link: http://www.cnblogs.com/learn-to-rock/p/5677458.htmlaccidentally on the internet to see a I am very interested in the project Magenta, with TensorFlow let neural network automatically create music. The vernacular is: You can use some of the style of music to make models, and then use the training model of the new music processing to create new music

= 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

LeNet5 convolution neural network forward propagation # TensorFlow actual combat Google Depth Learning Framework 06 image recognition and convolution neural network # WIN10 Tensorflow1.0.1 python3.5.3 # CUDA v8.0 cudnn-8.0-windows10-x64-v5.1 # filename:LeNet5_infernece.py

Floor, fully connected layer The number of input nodes in this layer is 120, the number of output nodes is 84, the total parameter is 120*84+84=10164. seventh floor, fully connected layer The number of input nodes in this layer is 84, the number of output nodes is 10, and the total parameters are 84*10+10=850 tensorflow implementation LeNet-5 The following is a TensorFlow program to implement a convolution

TensorFlow is used to train a simple binary classification neural network model. Use TensorFlow to implement the 4.7 pattern classification exercise in neural networks and machine learning The specific problem is to classify the dual-Crescent dataset as shown in. Tools used

TensorFlow let neural networks automatically create musicA few days ago to see an interesting share, the main idea is how to use TensorFlow teach neural network automatically create music. It sounds so fun, there's wood! As a Coldplay, the first idea was to automatically gen

TensorFlow deep learning convolutional neural network CNN, tensorflowcnn I. Convolutional Neural Network Overview ConvolutionalNeural Network (CNN) was originally designed to solve image recognition and other problems. CNN's curre

disconnects the connection arcs between certain nodes, so that they do not participate in the training for the time being.2. Data preprocessingThe data used for training is read first. from Import = input_data.read_data_sets ('./data/mnist', one_hot=true)In the preceding input layer, each sample entered is one-dimensional data, and the sample data of the convolutional neural network will be multidimension

, n_y): "" " creates the Placeholders for the TensorFlow session. Arguments: n_h0-scalar, height of an input image n_w0-scalar, width of an input image n_c0-scalar, nu Mber of channels of the input n_y-scalar, number of classes Returns: X--placeholder for the data input, O f shape [None, N_h0, N_w0, n_c0] and Dtype "float" Y--placeholder for the input labels, of shape [None, n_y] and DT Ype "float" "" " # # #

Learning notes TF055: TensorFlow neural network provides a simple one-dimensional quadratic function. tf055tensorflow TensorFlow running mode. Load data, define hyperparameters, build networks, train models, evaluate models, and predict. Construct raw data that satisfies the quadratic function y = ax ^ 2 + B, and const

The mnist examples of convolutional neural networks and the neural network examples in the previous blog post are mostly the same. But CNN has more layers, and the network model needs to be built on its own.The procedure is more complicated, I will be divided into several parts to describe.First, download and load the