tensorflow model serving

Model optimization is important for both traditional machine learning and deep learning, especially in deep learning, and it is likely that more difficult challenges will need to be addressed during training. At present, the popular and widely used optimization algorithm has a random gradient descent, with the momentum of the random gradient descent, Rmsprop algorithm, with momentum of Rmsprop,adadelta and Adam, and so on, the following will be select

language processing model. Last week, Google open-source its TensorFlow natural language analytic database syntaxnet based on AI system. Over the past two years, Google researchers have used this analysis to publish a series of neural network analysis models. Since the release of Syntaxnet, the author has been concerned about it, of course, also always expect this software to open source. However, this art

TensorFlow Simple loading training model, if a layer of shape is not the same automatically omitted, can also use reshape. Def optimistic_restore (Session, Save_file): reader = Tf.train.NewCheckpointReader (save_file) saved_shapes = Reader.get_variable_to_shape_map () var_names = sorted ([(Var.name, Var.name.split (': ') [0]) for VAR in tf.global_ Variables () If Var.name.split (':

When using random gradient descent algorithm to train the neural network, the meaning of the tf.train.ExponentialMovingAverage sliding average operation is to improve the robustness of the model on the test data (robustness). The tf.train.ExponentialMovingAverage under the TensorFlow need to provide an attenuation rate (decay). This attenuation rate is used to control the speed of

/ Imagenet-vgg-verydeep-19.mat '/data/cat.jpg' == (1, input_image.shape[0], input_image.shape[1], input_image.shape[2])Fourth step: Training model, output feature imageWith TF. Session as Sess:image= Tf.placeholder ('float', shape=shape)#Training ModelNets, mean_pixel, all_layers =net (vgg_path, image)#removal of mean valuesInput_image_pre =Np.array ([Preprocess (Input_image, Mean_pixel)]) layers=all_layers forI, layerinchEnumerate (layers):#individ