tensorflow

问题 I am following this tutorial: https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/?utm_campaign=chrome_series_machinelearning_063016&utm_source=gdev&utm_medium=yt-desc#4 I am running this part of the code: python retrain.py \ --bottleneck_dir=bottlenecks \ --how_many_training_steps=500 \ --model_dir=inception \ --summaries_dir=training_summaries/basic \ --output_graph=retrained_graph.pb \ --output_labels=retrained_labels.txt \ --image_dir=flower_photos Here is the error that

问题 I'm trying to run a tensorflow code in v2.0 and I'mg getting the following error AttributeError: module 'tensorflow' has no attribute 'logging' I don't want to simply remove it from the code. why this code has been removed? why should I do instead? 回答1: tf.logging was for Logging and Summary Operations and in TF 2.0 it has been removed in favor of the open-source absl-py, and to make the main tf.* namespace has functions that will be used more often. In TF.2 lesser used functions are gone or

问题 I'm trying to run a tensorflow code in v2.0 and I'mg getting the following error AttributeError: module 'tensorflow' has no attribute 'logging' I don't want to simply remove it from the code. why this code has been removed? why should I do instead? 回答1: tf.logging was for Logging and Summary Operations and in TF 2.0 it has been removed in favor of the open-source absl-py, and to make the main tf.* namespace has functions that will be used more often. In TF.2 lesser used functions are gone or

问题 I used to use Tensorflow a lot before, but moved over to Pytorch because it was just a lot easier to debug. The nice thing I found with PyTorch is that I have to write my own training loop, so I can step through the code and find errors. I can fire up pdb and check the tensor shapes and transformations, etc., without difficulty. In Tensorflow I was using the model.fit() function all the time, and so any error message I got was like 6 pages of C code where the error message did not give me any

问题 I have the necessity to calculate more then one accuracy in the same time, concurrently. correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})) The piece of code is the same of the mnist example in the tutorial of TensorFlow but instead of having: W = tf.Variable(tf.zeros([784, 10])) b = tf.Variable(tf.zeros([10])) I have two placeolder

问题 I used a temporary tensor to store data in my customized gpu-based op. For debug purpose, I want to print the data of this tensor by traditional printf inside C++. How can I pull this gpu-based tensor to cpu and then print its contents. Thank you very much. 回答1: If by temporary you mean allocate_temp instead of allocate_output , there is no way of fetching the data on the python side. I usually return the tensor itself during debugging so that a simple sess.run fetches the result. Otherwise,

问题 I used a temporary tensor to store data in my customized gpu-based op. For debug purpose, I want to print the data of this tensor by traditional printf inside C++. How can I pull this gpu-based tensor to cpu and then print its contents. Thank you very much. 回答1: If by temporary you mean allocate_temp instead of allocate_output , there is no way of fetching the data on the python side. I usually return the tensor itself during debugging so that a simple sess.run fetches the result. Otherwise,

问题 Anaconda has different packages for Tensorflow with and without GPU support. In particular, to install Tensorflow with GPU, you should run: conda install tensorflow-gpu While for the non-GPU version, you should install: conda install tensorflow By checking the version of the installed package, conda installs Tensorflow version 2.1. But as of today the latest version of Tensorflow is 2.3. Furthermore, as can be seen in the Tensorflow officla documentation, the latest version can be installed

问题 I have successfully followed this transfer learning tutorial to make my own classifier with two classes, "impressionism" and "modernism". Now trying to get a label for my test image, applying advice from this thread: y_prob = model.predict(new_image) y_prob (gives this output) array([[3.1922062e-04, 9.9968076e-01]], dtype=float32) y_classes = y_prob.argmax(axis=-1) y_classes (gives this output) array([1]) # create a list containing the class labels labels = ['modernism', 'impressionism']