tensorflow demo

/core:tensorflow ", ], Under the current Tensorflow/build directory to perform Bazel build:libtensorflow.so, the operation effect is as follows: Compile time about 2000 seconds, look at the computer performance, this compilation uses dual core four threads, 4GB memory.When the execution is complete, the following directories are generated in the directory where the workspace file resides: Bazel-bin, Bazel-genfiles,bazel-out, Bazel-

When reproduced, please specify the source: Xiu Yu Xuan Chen System Environment Description: ------------------------------------ Operating system: Ubunt 14.03 _ x86_64 operating system Memory: 8GB HDD 500G ------------------------------------First, compile the TensorFlow on Android Demo 1.1 build environmentL Download TensorFlow First, select a directory to down

This is my first blog, in reference to other people's blog to install the process, for my platform system, encountered a lot of problems, here to write my practice and the problems encountered.For the reference to the blogger's article, here to express thanks.For this blog, if there is bad writing or wrong place, because my level is limited, as well as the limitations of the problems encountered, can not be taken into account, please give understanding, and hope to get good suggestions, for good

range (1, Len (SYS.ARGV)):img = Cv2.imread (sys.argv[i])img = Cv2.resize (IMG, (28, 28))Test_image.append (Img.flatten (). Astype (Np.float32)/255.0)Filenames.append (Sys.argv[i])Test_image = Np.asarray (test_image)Images_placeholder = Tf.placeholder ("float", shape= (None, Image_pixels))Labels_placeholder = Tf.placeholder ("float", shape= (None, num_classes))Keep_prob = Tf.placeholder ("float")Logits = Inference (Images_placeholder, Keep_prob)Sess = tf. InteractiveSession ()Saver = Tf.train.Sa

believe not to say, we all know, the first in the go on the defeat of humans, and then upgraded version of the master 60 consecutive disk, the principle does not say, the network of many analytical articles, paste two pictures to pay tribute:WaveNet: Speech Audio synthesis Here DeepMind's little brother demonstrated wavenet some of the demo, specific can see here to understand.Paste some effect comparison:The combination of XLA, Keras and

TensorFlow Learning Notes 4: Distributed TensorFlow Brief Introduction The TensorFlow API provides cluster, server, and supervisor to support distributed training of models. The distributed training introduction about TensorFlow can refer to distributed TensorFlow. A simpl

for biases b = tf.Variable(0.0, name="biases") y_model = model(X, w, b) cost = tf.square(Y - y_model) # use square error for cost function # construct an optimizer to minimize cost and fit line to mydata train_op = tf.train.GradientDescentOptimizer(0.01).minimize(cost) # launch the graph in a session with tf.Session() as sess: # you need to initialize variables (in this case just variable w) init = tf.initialize_all_variables() sess.run(init) # train for i in range(100): for (

one of the hotspots of the current depth learning, and can pay attention to the different parts of input, give a series of understanding) Implementation of the Neural style-neural style (neural style is an algorithm to redraw a picture by imitating the painting style of an existing painting) Pretty Tensor-pretty Tensor provides an advanced builder API The realization of neural style-neural Style TensorFlow White Paper notes-A summary of annotat

Development environment: Mac OS 10.12.5Python 2.7.10GCC 4.2.1Mac default is no pip, install PIP.sudo easy_install pip1. Installing virtualenvsudo pip install virtualenv--upgradeCreate a working directory:sudo virtualenv--system-site-packages ~/tensorflowMake the directory, activate the sandboxCD ~/tensorflowSOURCE Bin/activateInstall TensorFlow in 2.virtualenvAfter entering the sandbox, execute the following command to install

1. Download and install Anaconda1.1 downloadDownload the Linux version from Anaconda official website (https://www.continuum.io/downloads)https://repo.continuum.io/archive/(Recommended python3.5)1.2 InstallationCD ~/downloadssudo bash anaconda2-5.0.1-linux-x86_64.sh (download the corresponding version of Python2.7 here)Ask if you want to add the Anaconda bin to the user's environment variable and select yes!Installation is complete.2. Install tensorflow2.1 set up

"Google" + "deep learning", two tags let the December 2015 Google open-source deep learning tool TensorFlow after its release quickly became the world's hottest open source project, April 2016, open source TensorFlow support distributed features, The application to the production environment is further.The TensorFlow API supports Python 2.7 and Python 3.3+, with

Through a few routines, we gradually established a perceptual knowledge of tensorflow. This article will further from the internal principle of deep understanding, and then for reading source to lay a good foundation.1. Graph (graph)The TensorFlow calculation is abstracted as a forward graph that includes several nodes. As shown in the example:The corresponding TensorFl