I am interested in studying graphs in tensorflow. The number of nodes varies from graph to graph in my dataset. This is problematic because, in my observation, tensorflow does not easily allow me to input data with variable shapes.
More specifically, could anyone suggest an easy way to feed batches of 2d arrays to a tf.Estimator.estimator , when the shapes of arrays are different? I have been using tf.estimator.inputs.numpy_input_fn for a dataset with fixed sizes, but I now need a different approach.
Ragged tensor has been introduced for this purpose.
https://www.tensorflow.org/guide/ragged_tensor
Related
When I decode a tensor using tf.io.parse_tensor, the shape comes out as shape=<unknown>, which makse sense because tensorflow has no way to know the shape of the data that I will pass into this operation. However, if I know that the data I will provide has a certain shape, such as having exactly 2 dimensions, or having 3 rows and an unknown number of columns, how can I "tell" tensorflow this?
I need to do this because I am using functions like padded_batch later on that behave differently for different shapes (producing a ragged tensor versus a dense tensor).
I don't know how to convert the PyTorch method adaptive_avg_pool2d to Keras or TensorFlow. Anyone can help?
PyTorch mehod is
adaptive_avg_pool2d(14,[14])
I tried to use the average pooling, the reshape the tensor in Keras, but got the error:
ValueError: total size of new array must be unchanged
I'm not sure if I understood your question, but in PyTorch, you pass the spatial dimensions to AdaptiveAvgPool2d. For instance, if you want to have an output sized 5x7, you can use nn.AdaptiveAvgPool2d((5,7)).
If you want a global average pooling layer, you can use nn.AdaptiveAvgPool2d(1). In Keras you can just use GlobalAveragePooling2D.
For other output sizes in Keras, you need to use AveragePooling2D, but you can't specify the output shape directly. You need to calculate/define the pool_size, stride, and padding parameters depending on how you want the output shape. If you need help with the calculations, check this page of CS231n course.
For legacy_seq2seq, it just supports the input with a list of 2D Tensors.
If a model has more parameters for each inputs. says,
[[1,2], [2,3], [3,4]...] instead of [1, 2, 3, 4, ....], it cant use legacy_seq2seq to have batch methods.
So, how to implement a multidimensional input rnn in batch method with tensorflow?
Or it doesnt exist?
Multi-dimensional RNNs are well supported in Tensorflow, but not added to the legacy seq2seq interface. Please use the current (dynamic_rnn) API and you'll see that multi-dimensional tensors work fine (there are even pre-made multi-dimensional RNN cells in contrib).
I defined a model of RNN in tensorflow, one of the gradients in compute_gradients is of type IndexedSlices while others are of type tensor. After I session.run(compute_gradients ...), the returned value type of IndexedSlices is IndexedSlicesValue, then I have two questions:
How could I average several IndexedSlicesValue values?
How can I serialize a IndexedSlicesValue and send it to another machine through socket?
Thank you very much!
IndexedSlices is really an encoding of a sparse tensor, using a pair of dense tensors. It probably comes from the gradient of a tf.gather operation. There is some API documentation about IndexedSlices here that may help: https://www.tensorflow.org/api_docs/python/tf/IndexedSlices
I don't know of much code to work with IndexedSlices directly; typically they are an internal detail used as part of gradient code. Depending on the data sizes, the easiest way to work with them might be to convert them into a dense Tensor and process/send that.
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.