tensorflow2.0

问题 TF Version: 2.2.0-rc3 (in Colab) I am using the following code (from tf.keras get computed gradient during training) in a callback to compute gradients for all parameters in a model. def on_train_begin(self, logs=None): # Functions return weights of each layer self.layerweights = [] for lndx, l in enumerate(self.model.layers): if hasattr(l, 'kernel'): self.layerweights.append(l.kernel) input_tensors = [self.model.inputs[0], self.model.sample_weights[0], self.model.targets[0], K.learning_phase

问题 I have conda 4.8.3 and Python 3.7.4 on Windows 8.1. I have tf 2.0.0 installed in a conda environment. How do I upgrade to 2.2.x? Or, how do I just install 2.2.x in a conda environment? Edit 1: pip install --upgrade tensorflow says: Requirement already up-to-date: tensorflow in d:\anaconda3\envs\tf2\lib\site-packages (2.1.0) but tf version is still 2.0. Edit 2: conda install tensorflow==2.2.0 says: PackagesNotFoundError: The following packages are not available from current channels:

问题 I would like to use GradientTape to observe gradients during eager execution mode. Is it possible to create a GradientTape once, which then records everything, as if it had global context? Here is an example of what I would like to do: import numpy as np import tensorflow as tf x = tf.Variable(np.ones((2,))) y=2*x z=2*y tf.gradients(z, x) # RuntimeError, not supported in eager execution Now, this can be fixed easily: with tf.GradientTape() as g: y = 2*x z = 2*y g.gradient(y, x) # this works

问题 I'm training a model with tensorflow 2.0 . The images in my training set are of different resolutions. The Model I've built can handle variable resolutions (conv layers followed by global averaging). My training set is very small and I want to use full training set in a single batch. Since my images are of different resolutions, I can't use model.fit() . So, I'm planning to pass each sample through the network individually, accumulate the errors/gradients and then apply one optimizer step. I

问题 I'm training a model with tensorflow 2.0 . The images in my training set are of different resolutions. The Model I've built can handle variable resolutions (conv layers followed by global averaging). My training set is very small and I want to use full training set in a single batch. Since my images are of different resolutions, I can't use model.fit() . So, I'm planning to pass each sample through the network individually, accumulate the errors/gradients and then apply one optimizer step. I

问题 I have had problems here, here and there installing tensorflow 2 over the last year or so. So I am trying miniconda. I have an amd radeon hd 6670 and an amd radeon hd 6450. I just downloaded miniconda and made an environment and did a pip install --upgrade tensorflow in an miniconda prompt on windows 8.1 and got tensorflow 2.2. When I try to import tensorflow I get the stack trace below. I did download visual studio to get the latest redistributebles (I think). seems like this occurs near

问题 I have had problems here, here and there installing tensorflow 2 over the last year or so. So I am trying miniconda. I have an amd radeon hd 6670 and an amd radeon hd 6450. I just downloaded miniconda and made an environment and did a pip install --upgrade tensorflow in an miniconda prompt on windows 8.1 and got tensorflow 2.2. When I try to import tensorflow I get the stack trace below. I did download visual studio to get the latest redistributebles (I think). seems like this occurs near

问题 I have had problems here, here and there installing tensorflow 2 over the last year or so. So I am trying miniconda. I have an amd radeon hd 6670 and an amd radeon hd 6450. I just downloaded miniconda and made an environment and did a pip install --upgrade tensorflow in an miniconda prompt on windows 8.1 and got tensorflow 2.2. When I try to import tensorflow I get the stack trace below. I did download visual studio to get the latest redistributebles (I think). seems like this occurs near

问题 I am following the object_detection_tutorial.ipynb tutorial. Here is the code ( I only put parts which are needed, the rest of the code is the same as the notebook): my_results = [] # I added this, a list to hold the detected classes PATH_TO_LABELS = 'D:\\TensorFlow\\models\\research\\object_detection\\data\\oid_v4_label_map.pbtxt' category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True) PATH_TO_TEST_IMAGES_DIR = pathlib.Path('C:\\Users\

问题 When I'm trying to run Yolo detection examples, I got that error: 2020-02-02 21:39:00.821721: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll WARNING:tensorflow:From C:\Users\Dominux\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\ops\resource_variable_ops.py:1635: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be