tensorflow-tutorial | Some tensorflow demo | Learning library

Are you trying to optimize:

1 initial data randomization? 2 data randomization across training batches and/or epochs? 3 training/validation throughput (ie, gpu utilization)?

Initial data randomization should be handled when data are initially saved into sharded files. This can be challenging, assuming you can't read the data into memory. One approach is to read all the unique data ids into memory, shuffle those, do your train/validate/test split, and then write your actual data to file shards in that randomized order. Now your data are initially shuffled/split/sharded.

Initial data randomization will make it easier to maintain randomization during training. However, I'd still say it is 'best practice' to re-shuffle file names and re-shuffle a data memory buffer as part of the train/validate data streams. Typically, you'll set up an input stream using multiple threads/processes. The first step is to randomize the file input streams by re-shuffling the filenames. This can be done like:

I updated my skimage package.

Keras does not include by itself any means to export a TensorFlow graph as a protocol buffers file, but you can do it using regular TensorFlow utilities. Here is a blog post explaining how to do it using the utility script freeze_graph.py included in TensorFlow, which is the "typical" way it is done.

However, I personally find a nuisance having to make a checkpoint and then run an external script to obtain a model, and instead prefer to do it from my own Python code, so I use a function like this:

You can disable certificate verification;

Tensorflow uses Feature Extraction which is using the representations of learned by a previous network to extract meaningful features from new samples.

Faster_RCNN_ResNet_101 feature extractor is defined in this class : https://github.com/tensorflow/models/blob/master/research/object_detection/models/faster_rcnn_resnet_v1_feature_extractor.py

I guess set_input() method is removed in latest versions of Keras. If you see this documentation of Keras, there is a function called set_input() function of keras.layers.containers.Sequential class. But its source code is no longer available on Github.

If you look at the source code of Dense layer class in Keras, you will see that there is no such method called set_input() as well. If you also see the source of abstract class Layer which is the base class for Dense layer, you will see there is no such function called set_input().

So, we can conclude, set_input() method is probably no longer available in Keras.

I would guess that you're trying to save the file in a folder (directory) that doesn't exist...

Try using an absolute path for the file instead of just the bare filename.

You might want to check what your current working directory is... that could clear up things.

Does that help?

-josh

All right. I went through official guide for eager execution, and finally done it.

Here's the code:

Finally found the answer. There are two problems in the example from the question.

The first and most obvious was that I called the tf.global_variables_intializer() function which will re-initialize all variables in the session. Instead I should have called the tf.variables_initializer(var_list) where var_list is a list of variables to initialize.

The second problem was that Keras did not use the same session as the native Tensorflow objects. This meant that to be able to run the tensorflow object model2(v) with my session sess it needed to be reinitialized. Again Keras as a simplified interface to tensorflow: Tutorial was able to help

We should start by creating a TensorFlow session and registering it with Keras. This means that Keras will use the session we registered to initialize all variables that it creates internally.