Attention vector for sequence 2 sequence model is basically a array of shape [batch_size, time_step,1], which indicates the weighs of a particular time step.
But if I use tf.summary.histogram to show it on tensorboard, tensorflow will only show the distributions of weights, I can't tell the which time step is more important. I can use tf.summary.scalar, but length of my source sequence is 128 , it is too much plots. The most nature way of show this kind of data it a picture like this, but how can I do it in tensorboad?
Tensorboard does not currently support visualizing tensor summaries. There is a summary op for it, but Tensorboard will just skip it when reading summaries from disk at the moment. I am also not aware of any third party plugins that support this, though it is very much doable.
In your plot, it seems like there are only 19 time steps. One way is to create 19 scalar summaries. Alternatively, you can use tf.summary.tensor_summary op, but process the tensorboard event file (containing this data) with your own script.
Related
I'm currently facing a Machine Learning problem and I've reached a point where I need some help to proceed.
I have various time series of positional (x, y, z) data tracked by sensors. I've developed some more features. For example, I rasterized the whole 3D space and calculated a cell_x, cell_y and cell_z for every time step. The time series itself have variable lengths.
My goal is to build a model which classifies every time step with the labels 0 or 1 (binary classification based on past and future values). Therefore I have a lot of training time series where the labels are already set.
One thing which could be very problematic is that there are very few 1's labels in the data (for example only 3 of 800 samples are labeled with 1).
It would be great if someone can help me in the right direction because there are too many possible problems:
Wrong hyperparameters
Incorrect model
Too few 1's labels, but I think that's not a big problem because I only need the model to suggests the right time steps. So I would only use the peaks of the output.
Bad or too less training data
Bad features
I appreciate any help and tips.
Your model seems very strange. Why only use 2 units in lstm layer? Also your problem is a binary classification. In this case you should choose only one neuron in your output layer (try to insert one additional dense layer between and lstm layer and try dropout layers between them).
Binary crossentropy does not make much sense with 2 output neurons, if you don't have a multi label problem. But if you're switching to one output neuron it's the right one. You also need sigmoid then as activation function.
As last advice: Try class weights.
http://scikit-learn.org/stable/modules/generated/sklearn.utils.class_weight.compute_class_weight.html
This can make a huge difference, if you're label are unbalanced.
You can create the model using tensorflow BasicLSTMCell, the shape of your data fits for BasicLSTMCell in TensorFlow you can find Documentation for BasicLSTMCell here and for creating the model this Documentation contain code that will help to build BasicLstmCell model . Hope this will help you, Cheers.
Many thanks for support!
I currently use TF Slim - and TF Hub seems like a very useful addition for transfer learning. However the following things are not clear from the documentation:
1. Is preprocessing done implicitly? Is this based on "trainable=True/False" parameter in constructor of module?
module = hub.Module("https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1", trainable=True)
When I use Tf-slim I use the preprocess method:
inception_preprocessing.preprocess_image(image, img_height, img_width, is_training)
2.How to get access to AuxLogits for an inception model? Seems to be missing:
import tensorflow_hub as hub
import tensorflow as tf
img = tf.random_uniform([10,299,299,3])
module = hub.Module("https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1", trainable=True)
outputs = module(dict(images=img), signature="image_feature_vector", as_dict=True)
The output is
dict_keys(['InceptionV3/Mixed_6b', 'InceptionV3/MaxPool_5a_3x3', 'InceptionV3/Mixed_6c', 'InceptionV3/Mixed_6d', 'InceptionV3/Mixed_6e', 'InceptionV3/Mixed_7a', 'InceptionV3/Mixed_7b', 'InceptionV3/Conv2d_2a_3x3', 'InceptionV3/Mixed_7c', 'InceptionV3/Conv2d_4a_3x3', 'InceptionV3/Conv2d_1a_3x3', 'InceptionV3/global_pool', 'InceptionV3/MaxPool_3a_3x3', 'InceptionV3/Conv2d_2b_3x3', 'InceptionV3/Conv2d_3b_1x1', 'default', 'InceptionV3/Mixed_5b', 'InceptionV3/Mixed_5c', 'InceptionV3/Mixed_5d', 'InceptionV3/Mixed_6a'])
These are excellent questions; let me try to give good answers also for readers less familiar with TF-Slim.
1. Preprocessing is not done by the module, because it is a lot about your data, and not so much about the CNN architecture within the module. The module only handles transforming input values from the canonical [0,1] range into whatever the pre-trained CNN within the module expects.
Lengthy rationale: Preprocessing of images for CNN training usually consists of decoding the input JPEG (or whatever), selecting a (reasonably large) random crop from it, random photometric and geometric transformations (distort colors, flip left/right, etc.), and resizing to the common image size for a batch of training inputs. The TensorFlow Hub modules that implement https://tensorflow.org/hub/common_signatures/images leave all of that to your code around the module.
The primary reason is that the suitable random transformations depend a lot on your training task, but not on the architecture or trained state weights of the module. For example, color distortions will help if you classify cars vs dogs, but probably not for ripe vs unripe bananas, and so on.
Also, a batch of images that have been decoded but not yet cropped/resized are hard to represent as a single tensor (unless you make it a 1-D tensor of encoded strings, but that brings other problems, such as breaking backprop into module inputs for advanced uses).
Bottom line: The Python code using the module needs to do image preprocessing (except scaling values), for example, as in https://github.com/tensorflow/hub/blob/master/examples/image_retraining/retrain.py
The slim preprocessing methods conflate the dataset-specific random transformations (tuned for Imagenet!) with the re-scaling to the architecture's value range (which the Hub module does for you). That means they are not directly applicable here.
2. Indeed, auxiliary heads are missing from the initial set of modules published under tfhub.dev/google/..., but I expect them to work fine for re-training anyways.
More details: Not all architectures have auxiliary heads, and even the original Inception paper says their effect was "relatively minor" [Szegedy&al. 2015; ยง5]. Using an image feature vector module for a custom classification task would burden the module consumer code with checking for aux features and, if found, putting aux logits and a loss term on top.
This complication did not seem to pull its weight, but more experiments might refute that assessment. (Please share in a GitHub issue if you know of any.)
For now, the only way to put an aux head onto https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1 is to copy&paste some lines from https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v3.py (search "Auxiliary head logits") and apply that to the "Inception_V3/Mixed_6e" output that you saw.
3. You didn't ask, but: For training, the module's documentation recommends to pass hub.Module(..., tags={"train"}), or else batch norm operates in inference mode (and dropout, if the module had any).
Hope this explains how and why things are.
Arno (from the TensorFlow Hub developers)
I'm using Tensorflow's 1.3 Estimator API to perform some image classification. Since I have a considerable amount of data, I gave the TFRecords a go. Saved the file and can read the examples to a Dataset using a parser function inside the input_fn of the estimator model. So far so good.
The issue is when I want to do some image augmentation (rotating and shearing in this case).
1) I tried using the tf.contrib.keras.preprocessing.image.random_shearand the likes. Turns out Keras doesn't like the format of TF's shape ('Dimension') and I can't cast it to a list because its arguments are the axis indexes not the actual value.
2) Then I tried using the tf.contrib.image.rotate and tf.contrib.image.transform with random values in my chosen range. This time I get an error of NotFoundError: Op type not registered 'ImageProjectiveTransform' in binary running on MYPC. Make sure the Op and Kernel are registered in the binary running in this process. which is an open issue (https://github.com/tensorflow/tensorflow/issues/9672). At the moment I can't move from Windows, so I would very interested in possible alternatives.
3) Searched for a way to read TFRecords and transform it to numpy array and do the augmentation with other tools, but can't find a way from within the input_fn from where I can't access the session.
Thanks!
Have you tried using function from the answer to the question below?tensorflow: how to rotate an image for data augmentation?
Working on simple softmax tensorflow classification with 4 features/inputs and 3 outputs.
To save results : input vs predicted output vs actual output , doing tf.concat across all tensors and print(Tensor.eval()) to review results.
Wondering in real use case what are the best ways to review results which are not scalar(So tensorboard scalar not an option) , but N-D type results. Any tool to plot graph with tensor outputs? Dump to file(Data could be huge , hard to analyze)? Dump to database? Depends on real time requirements , but looking for any tools to plug in output of tensorflow.
Please share thoughts. Thanks!
You can of course request any tensor and get it back in numpy format, from there you can do what you like with it.
Tensorboard is the main visualization tool in tensorflow. It allows you to display scalar and histogram data types, as well as images and audio clips as you train.
Here are a few images from tensorboard of scalar values, distribution, and histogram
Scalar values are showing error rates
Distribution shows the distribution of gradient magnitudes
Histogram shows a 3D histogram of gradient magnitudes over time
I'm trying to use TensorFlow to train output servo commands given an input image.
I plan on using a file as #mrry suggested in this question, with the images like so:
../some/path/some_img.JPG *some_label*
My question is, what are the label formats I can provide to TensorFlow and what structures are suggested?
My data is basically n servo commands from 0-10 seconds. A vector would work great:
[0,2,4,3]
or similarly:
[0,.25,.4,.3]
I couldn't find much about labels in the docs. Can anyone shed any light on TensorFlow labels?
And a very related question is what is the best way to structure these for TensorFlow to properly learn from them?
In Tensorflow Labels are just generic tensor. You can use any kind of tensor to store your labels. In your case a 1-D tensor with shape (4,) seems to be desired.
Labels do only differ from the rest of the data by its use in the computational graph. (Usually) labels should only be used inside the loss function while you propagate the other data through the whole network. For your problem a 4-d regression function should work.
Also, look at my newest comment to the (old) question. Using the slice_input_producer seems to be preferable in your case.