I am working on an image classification problem with tensorflow. I have 2 different CNNs trained separately (in fact 3 in total but I will deal with the third later), for different tasks and on a AWS (Amazon) machine. One tells if there is text in the image and the other one tells if the image is safe for work or not. Now I want to use them in a single script on my computer, so that I can put an image as input and get the results of both networks as output.
I load the two graphs in a single tensorflow Session, using the import_meta_graph API and the import_scope argument and putting each subgraph in a separate scope. Then I just use the restore method of the created saver, giving it the common Session as argument.
Then, in order to run inference, I retrieve the placeholders and final output with graph=tf.get_default_graph() and my_var=graph.get_operation_by_name('name').outputs[0] before using it in sess.run (I think I could just have put 'name' in sess.run instead of fetching the output tensor and putting it in a variable, but this is not my problem).
My problem is the text CNN works perfectly fine, but the nsfw detector always gives me the same output, no matter the input (even with np.zeros()). I have tried both separately and same story: text works but not nsfw. So I don't think the problem comes from using two networks simultaneaously.
I also tried on the original AWS machine I trained it on, and this time the nsfw CNN worked perfectly.
Both networks are very similar. I checked on Tensorboard if everything was fine and I think it is ok. The differences are in the number of hidden units and the fact that I use batch normalization in the nsfw model and not in the text one. Now why this title ? I observed that I had a warning when running the nsfw model that I didn't have when using only the text model:
W tensorflow/core/framework/op_def_util.cc:332] Op Inv is deprecated. It will cease to work in GraphDef version 17. Use Reciprocal.
So I thougt maybe this was the reason, everything else being equal. I checked my GraphDef version, which seems to be 11, so Inv should still work in theory. By the way the AWS machine use tensroflow version 0.10 and I use version 0.12.
I noticed that the text network only had one Inv operation (via a filtering on the names of the operations given by graph.get_operations()), and that the nsfw model had the same operation plus multiple Inv operations due to the batch normalization layers. As precised in the release notes, tf.inv has simply been renamed to tf.reciprocal, so I tried to change the names of the operations to Reciprocal with tf.group(), as proposed here, but it didn't work. I have seen that using tf.identity() and changing the name could also work, but from what I understand, tensorflow graphs are an append-only structure, so we can't really modify its operations (which seems to be immutable anyway).
The thing is:
as I said, the Inv operation should still work in my GraphDef version;
this is only a warning;
the Inv operations only appear under name scopes that begin with 'gradients' so, from my understanding, this shouldn't be used for inference;
the text model also have an Inv operation.
For these reasons, I have a big doubt on my diagnosis. So my final questions are:
do you have another diagnosis?
if mine is correct, is it possible to replace Inv operations with Reciprocal operations, or do you have any other solution?
After a thorough examination of the output of relevant nodes, with the help of Tensorboard, I am now pretty certain that the renaming of Inv to Reciprocal has nothing to do with my problem.
It appears that the last batch normalization layer eliminates almost any variance of its output when the inputs varies. I will ask why elsewhere.
Related
I am running the Word2Vec implementation from gensim twice, and I have a problem with the save function:
model_ = gensim.models.Word2Vec(all_doc, size=int(config['MODEL']['embed_size']),
window=int(config['MODEL']['window']),
workers=multiprocessing.cpu_count(),
sg=1, iter=int(config['MODEL']['iteration']),
negative=int(config['MODEL']['negative']),
min_count=int(config['MODEL']['min_count']), seed=int(config['MODEL']['seed']))
model_.save(config['BASIC']['embedding_dir'])
I obtain different outputs for each time I run it. The first time it gives an "output_embedding", an "output_embedding.trainables.syn1neg.npy" and an "output_embedding.wv.vectors.npy". But the second time it does not give the two npy files, it just generates "output_embedding".
The only thing I change from the first to the second time is the sentences I use as input (all_doc).
Why it does not generate the 3 files ?
Gensim only creates the separate files when the size of the internal numpy arrays is over a certain threshold – so I suspect your all_doc corpus has a very small vocabulary in one case, and a more typically large vocabulary in the other.
When it does generate multiple files, be sure to keep them all together for later loads to work.
(If for some urgent reason you needed to change that behavior, the inherited .save() method takes an optional sep_limit argument to change the threshold - but I'd recommend against mucking with this.)
Separately: that your file names have .trainables. in them suggests you're using a pre-4.0.0 version of Gensim. There've been some improvements to Word2Vec & related algorithms in the latest Gensim, and some older code will need small changes to keep working, so you may want to upgrade to the latest version before building any more functionality on an older base.
I have 4 (or more) models (same structure but different training data). Now I want to ensemble them to make a prediction. I want to pre-load the models and then predict one input message (one message at a time) in parallel via multiprocess. However, the program always stops at "session.run" step. I could not figure it out why.
I tried passing all arguments to the function in each process, as shown in the code below. I also tried using a Queue object and put all the data (except the model object) in the queue. I also tried to set the number of process to 1. It made no difference.
with Manager() as manager:
first_level_test_features=manager.list()
procs =[]
for id in range(4):
p = Process(target=predict, args=(id, (message, models, configs, vocabs, emoji_dict,first_level_test_features)))
procs.append(p)
p.start()
for p in procs:
p.join()
I did not get any error message since it is just stuck there. I would expect the program can start multiple processes and each process uses the model pass to it to make the prediction.
I am unsure how session sharing along different Processes would work, and this is probably where your issue comes from. Given the way TensorFlow works, I would advise implementing the ensemble call as a graph operation, so that it can be run through a single session.run call, with TF handling the parallelization of computations wherever possible.
In practice, if you have symbolic tensors representing the models' predictions, you could use a TF operation to aggregate them (tf.concat, tf.reduce_mean, tf.add_n... whichever suits your design) and end up with a single symbolic tensor representing the ensemble prediction.
I hope this helps; if not, please provide some more details as to what your setting is, notably which form your models have.
I am using this code to train a word2vec model. I am trying to train it incrementally, with using saver.restore(). I am using new data after restoring the model. Since vocabulary size for the old data and new data are not the same, I got an exception like this:
InvalidArgumentError (see above for traceback): Assign requires shapes of both tensors to match. lhs shape= [28908,200] rhs shape= [71291,200]
Here 71291 is vocabulary size for the old data and 28908 is for new data.
It gets the vocabulary words from the train_data file here, and constructs the network model using size of the vocabulary. I thought that if I could set vocabulary size the same for my old data and new data, I can solve this problem.
So, my question is: Can I do that in this code? As far as I understand, I cannot reach skipgram_word2vec() function.
Or, is there any other way of solving this issue in this code beside what I thought? If it is not possible using this code, I will try other ways for my purpose.
Any help is appreciated.
Having taken a look at the source of word2vec_optimized.py I'd say you will need to change the code there. It operates by opening a text file right up front as "training data". For your purposes, you have to change the build_graph method and allow it to get an option to set all that data ( words, counts, words_per_epoch, current_epoch, total_words_processed, examples, labels, opts.vocab_words, opts.vocab_counts, opts.words_per_epoch ) when initializing, and not from a text file.
Then you need to merge the two text files, and load them once, to produce the vocabulary. Then save all the data above, and use that to restore the network at each subsequent run.
If you use more than 2 texts, you need to include all the text you plan to use in the first data to produce the vocabulary, however.
I've built (in jupyter notebook with Python 3.6) a long ML proof of concept, which, in essence, has 3 parts: load & prepare data; train network; use network.
I would like to be able to re-run it from "train network" without the "cost" of preparing the data again & again (even loading the prepared data from a save file takes a noticeable amount of time).
When I run all cells from the start of the network training (the first cell of which includes a K.clear_session to wipe out any previous network - needed if the architecture changes) it fails as, part way through, there are still variables stored (with the same names) which are part of the old graph.
I can see two simple solutions (but you may be able to advise a better method to tidy up):
loop through all the defined variables (Tensors) in global() and del any which are Tensors (implicitly all part of the old session and graph),
or (better)
loop through all the tensors defined in the (old) graph del'ing them before del'ing the (old) graph.
I can see K.get_uid but can't see how I can use this info to accomplish what I need.
In the meantime I have to reset and rerun the whole workbook everytime I make adjustments to the network.
Is there a better way?
I'm using an open source version of a3c implementation in Tensorflow which works reasonably well for atari 2600 experiments. However, when I modify the network for Mujoco, as outlined in the paper, the network refuses to learn anything meaningful. Has anyone managed to make any open source implementations of a3c work with continuous domain problems, for example mujoco?
I have done a continuous action of Pendulum and it works well.
Firstly, you will build your neural network and output mean (mu) and standard deviation (sigma) for selecting an action.
The essential part of the continuous action is to include a normal distribution. I'm using tensorflow, so the code is looks like:
normal_dist = tf.contrib.distributions.Normal(mu, sigma)
log_prob = normal_dist.log_prob(action)
exp_v = log_prob * td_error
entropy = normal_dist.entropy() # encourage exploration
exp_v = tf.reduce_sum(0.01 * entropy + exp_v)
actor_loss = -exp_v
When you wanna sample an action, use the function tensorflow gives:
sampled_action = normal_dist.sample(1)
The full code of Pendulum can be found in my Github. https://github.com/MorvanZhou/tutorials/blob/master/Reinforcement_learning_TUT/10_A3C/A3C_continuous_action.py
I was hung up on this for a long time, hopefully this helps someone in my shoes:
Advantage Actor-critic in discrete spaces is easy: if your actor does better than you expect, increase the probability of doing that move. If it does worse, decrease it.
In continuous spaces though, how do you do this? The entire vector your policy function outputs is your move -- if you are on-policy, and you do better than expected, there's no way of saying "let's output that action even more!" because you're already outputting exactly that vector.
That's where Morvan's answer comes into play. Instead of outputting just an action, you output a mean and a std-dev for each output-feature. To choose an action, you pass your inputs in to create a mean/stddev for each output-feature, and then sample each feature from this normal distribution.
If you do well, you adjust the weights of your policy network to change the mean/stddev to encourage this action. If you do poorly, you do the opposite.