tensorflow-gpu

问题 I successfully created my tensorflow environment with Anaconda3 on my machine with the steps introduced on this link. But when I try to try to do : >>> import tensorflow as tf I get the following error messages : OSError and ImportError. Traceback (most recent call last): File "C:\Users\Froilan\Anaconda3\envs\tensorflow\lib\site- packages\tensorflow\python\platform\self_check.py", line 75, in preload_check ctypes.WinDLL(build_info.cudart_dll_name) File "C:\Users\Froilan\Anaconda3\envs

问题 I was using Tensorflow(CPU version) for my Deep Learning Model. Specifically using DNNRegressor Estimator for training, with given set of parameters (network structure, hidden layers, alpha etc.) Though I was able to reduce the loss, but model took very large time for learning (approx 3 days.) and time it was taking was 9 sec per 100th step. I came accross this article :- https://medium.com/towards-data-science/how-to-traine-tensorflow-models-79426dabd304 and found that GPU's can be more

问题 I am running a prediction on a tensorflow-serving model, and I get back this PredictResponse object as output: Result: outputs { key: "outputs" value { dtype: DT_FLOAT tensor_shape { dim { size: 1 } dim { size: 20 } } float_val: 0.000343723397236 float_val: 0.999655127525 float_val: 3.96821117632e-11 float_val: 1.20521548297e-09 float_val: 2.09611101809e-08 float_val: 1.46216549979e-09 float_val: 3.87274603497e-08 float_val: 1.83520256769e-08 float_val: 1.47733780764e-08 float_val: 8

问题 Command which i used to install virtualenv:- sudo pip install virtualenv Result:- The directory '/Users/apple/Library/Caches/pip/http' or its parent directory is not owned by the current user and the cache has been disabled. Please check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag. The directory '/Users/apple/Library/Caches/pip' or its parent directory is not owned by the current user and caching wheels has been disabled. check the

问题 I'm looking at porting from a different production machine learning framework to TensorFlow. In our current system for both training and inference we load copies of our model onto as many GPUs as are on the machine. I would like to keep this way of load-balancing for now. Where can I find a simple example of loading one copy of a TF model onto each GPU that's available on a machine? 回答1: Here's an example from https://github.com/rafaljozefowicz/lm/blob/master/language_model.py#L21 You wrap

问题 I have the same problem as in the similar question and tried the proposed solution, but it did not work. Below you can find the stacktrace. I am on Windows 10 x64 with Python 3.5.2 and GPU NVidia Geforce 1050. I also checked the TensorFlow site for common errors. C:\Users\Steph>ipython Python 3.5.2 (v3.5.2:4def2a2901a5, Jun 25 2016, 22:18:55) [MSC v.1900 64 bit (AMD64)] Type 'copyright', 'credits' or 'license' for more information IPython 6.1.0 -- An enhanced Interactive Python. Type '?' for

问题 I've created a network in Keras that uses both convolutional and LSTM layers. And I've read that tensorflow can't handle well the LSTM layers in GPU: http://minimaxir.com/2017/07/cpu-or-gpu/ In fact, my network got slow in the GPU (hoping its not a further problem). I want tensorflow to throw all convolutional operations to the GPU, but keep all the LSTM operations in the CPU. Is that possible? So, considering I can find the tensorflow graph and identify each of its tensors, can I define

问题 I have used an AWS p3 instance to train the following model using GPU acceleration: x = CuDNNLSTM(128, return_sequences=True)(inputs) x = Dropout(0.2)(x) x = CuDNNLSTM(128, return_sequences=False)(x) x = Dropout(0.2)(x) predictions = Dense(1, activation='tanh')(x) model = Model(inputs=inputs, outputs=predictions) After training I saved the model with Keras' save_model function and moved it to a separate production server that doesn't have a GPU. When I attempt to predict using the model on

问题 I am trying to convert trained_checkpoint to final frozen model from the export_inference_graph.py script provided in tensorflow/models,but the following error results. And yes,I have already setup $PYTHONPATH to "models/slim" but still I get this error,can someone help me out? $ echo $PYTHONPATH :/home/ishara/tensorflow_models/models:/home/ishara/tensorflow_models/models/slim *****************************problem****************************************************************************

问题 I've made this neural net to figure out whether a house is a good buy or a bad buy. For some reasons the code is not updating weights and biases. My loss stays same. This is my code: I've made this neural net to figure out whether a house is a good buy or a bad buy. For some reasons the code is not updating weights and biases. My loss stays same. This is my code: import pandas as pd import tensorflow as tf data = pd.read_csv("E:/workspace_py/datasets/good_bad_buy.csv") features = data.drop([