I’d recommend to always use tf.get_variable(…) — it will make it way easier to refactor your code if you need to share variables at any time, e.g. in a multi-gpu setting (see the multi-gpu CIFAR example). There is no downside to it. Pure tf.Variable is lower-level; at some point tf.get_variable() did not exist so some … Read more
Actually – model.save saves all information need for restarting training in your case. The only thing which could be spoiled by reloading model is your optimizer state. To check that – try to save and reload model and train it on training data.
There are lots of things I have seen make a model diverge. Too high of a learning rate. You can often tell if this is the case if the loss begins to increase and then diverges to infinity. I am not to familiar with the DNNClassifier but I am guessing it uses the categorical cross … Read more
You can do [n.name for n in tf.get_default_graph().as_graph_def().node] Also, if you are prototyping in an IPython notebook, you can show the graph directly in notebook, see show_graph function in Alexander’s Deep Dream notebook
answer moved from edit to self-answer as requested I just played around with this a little bit, and it seems that if one combines tf.control_dependencies with tf.record_summaries_every_n_global_steps it behaves as expected and the summary only gets recorded every nth step. But if they are run together within a session, such as session.run([train, summs]), the summaries … Read more
Ok I think this is about the simplest way to explain it all. Your example is 1 image, size 2×2, with 1 channel. You have 1 filter, with size 1×1, and 1 channel (size is height x width x channels x number of filters). For this simple case the resulting 2×2, 1 channel image (size … Read more
Tensorflow 2.1+ What’s going on? With the new Tensorflow 2.1 release, the default tensorflow pip package contains both CPU and GPU versions of TF. In previous TF versions, not finding the CUDA libraries would emit an error and raise an exception, while now the library dynamically searches for the correct CUDA version and, if it … Read more
Yes you can run keras models on GPU. Few things you will have to check first. your system has GPU (Nvidia. As AMD doesn’t work yet) You have installed the GPU version of tensorflow You have installed CUDA installation instructions Verify that tensorflow is running with GPU check if GPU is working sess = tf.Session(config=tf.ConfigProto(log_device_placement=True)) … Read more
It appears that the network hasn’t learned anything in the layers one to three. The last layer does change, so that means that there either may be something wrong with the gradients (if you’re tampering with them manually), you’re constraining learning to the last layer by optimizing only its weights or the last layer really … Read more
if __name__ == “__main__”: means current file is executed under a shell instead of imported as a module. tf.app.run() As you can see through the file app.py def run(main=None, argv=None): “””Runs the program with an optional ‘main’ function and ‘argv’ list.””” f = flags.FLAGS # Extract the args from the optional `argv` list. args = … Read more