I'm using tensorboard (tensorflow 1.1.0) to show the result of my CNN classifier.
I added some output vector as tf.summary.histogram in order to show the counts of output in each bin, but tensorboard seems to automatically compute interpolation and show them as (somehow) smoothed distribution
(and therefore I can not find the exact counts for the bins).
Could someone tell me how can I avoid the interpolation and show usual histograms using bars?
I not sure that there is easy way to do it.
I very unsure in below text, correct me if I wrong.
From this file https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/histogram/vz_histogram_timeseries/index.html it seems that histogram comes to tensorboard in double values.
Summary op uses either histogram from https://github.com/tensorflow/tensorflow/blob/r1.2/tensorflow/python/ops/histogram_ops.py (1) or https://github.com/tensorflow/tensorflow/blob/r1.2/tensorflow/core/lib/histogram/histogram.cc (2)
I suppose that it uses 2nd because here https://github.com/tensorflow/tensorflow/blob/r1.2/tensorflow/python/summary/summary.py#L189 it calls function from generated file. In my package code in this generated file there is another function call:
result = _op_def_lib.apply_op("HistogramSummary", tag=tag, values=values,
name=name)
I have grep all repo and seems like there is no other python code which define something with "HistogramSummary", so it seems like it's really defined here https://github.com/tensorflow/tensorflow/blob/r1.2/tensorflow/core/kernels/summary_op.cc and this code uses code mentioned above (2).
So, it seems to me that histogram which is used now is buried deep inside of framework and I not sure that it's easy to rewrite it.
In this page there is email for support https://github.com/tensorflow/tensorflow/tree/master/tensorflow/python/summary . I suppose that it's better to contact this person or make issue on github.
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I am training the Tacotron2 model using TensorflowTTS for a new language.
I managed to train the model (performed pre-processing, normalization, and decoded the few generated output files)
The files in the output directory are .npy files. Which makes sense as they are mel-spectograms.
I am trying to find a way to convert said files to a .wav file in order to check if my work has been fruitfull.
I used this :
melspectrogram = librosa.feature.melspectrogram(
"/content/prediction/tacotron2-0/paol_wavpaol_8-norm-feats.npy", sr=22050,
window=scipy.signal.hanning, n_fft=1024, hop_length=256)
print('melspectrogram.shape', melspectrogram.shape)
print(melspectrogram)
audio_signal = librosa.feature.inverse.mel_to_audio(
melspectrogram, sr22050, n_fft=1024, hop_length=256, window=scipy.signal.hanning)
print(audio_signal, audio_signal.shape)
sf.write('test.wav', audio_signal, sample_rate)
But it is given me this error : Audio data must be of type numpy.ndarray.
Although I am already giving it a numpy.ndarray file.
Does anyone know where the issue might be, and if anyone knows a better way to do it?
I'm not sure what your error is, but the output of a Tacotron 2 system are log Mel spectral features and you can't just apply the inverse Fourier transform to get a waveform because you are missing the phase information and because the features are not invertible. You can learn about why this is at places like Speech.Zone (https://speech.zone/courses/)
Instead of using librosa like you are doing, you need to use a vocoder like HiFiGan (https://github.com/jik876/hifi-gan) that is trained to reconstruct a waveform from log Mel spectral features. You can use a pre-trained model, and most off-the-shelf vocoders, but make sure that the sample rate, Mel range, FFT, hop size and window size are all the same between your Tacotron2 feature prediction network and whatever vocoder you choose otherwise you'll just get noise!
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.
Im trying to implement the following project into Tensorflow/Keras.
https://github.com/jacobgil/pytorch-pruning
Im having a hard time understanding what register_hook does? It can be found in finetune.py, row 66.
x.register_hook(self.compute_rank)
I've searched for clear explanations regarding this function and tried to find Keras-equivalents, without any luck. Do you have answers to these questions?
First things first, here's the documentation:
http://pytorch.org/docs/master/autograd.html#torch.autograd.Variable.register_hook
This allows you to register a method to a Variable that is called whenever the Variable's .grad is updated, i.e. in a backward pass, and takes the grad as input. The method can return a Variable that would replace the original .grad or None if you just want to read the gradients to do something else.
If you update the gradients this way, the nodes further down in the compute graph see the new updated gradient in the backward pass and will have their respective gradients calculated with the updated value.
I'm not a Tensorflow expert, but the RegisterGradient decorators (documentation) seem to be able to do the same, for an example see this answer.
TensorFlow provides a tf.summary.tensor_summary() function that appears to be a multidimensional variant of tf.summary.scalar():
tf.summary.tensor_summary(name, tensor, summary_description=None, collections=None)
I thought it could be useful for summarizing inferred probabilities per class ... somewhat like
op_summary = tf.summary.tensor_summary('classes', some_tensor)
# ...
summary = sess.run(op_summary)
writer.add_summary(summary)
However it appears that TensorBoard doesn't provide a way to display these summaries at all. How are they meant to be used?
I cannot get it to work either. It seems like that feature is still under development. See this video from the TensorFlow Dev Summit that states that the tensor_summary is still under development (starting at 9:17): https://youtu.be/eBbEDRsCmv4?t=9m17s. It will probably be better defined and examples should be provided in the future.
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.