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kandi X-RAY | elman Summary
A script for full text searching Linux man pages with Elasticsearch. It has been developed to play around with the idea described in this post.
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Lines of Code : 119License : Permissive (MIT License)
Copydef md5me(testString):
"""[summary]
Returns a 32-bit hash code of the string 'testString'
Arguments:
testString {[string]} -- [message]
"""
bs = ""
for i in testString:
bs += format(ord(i), "08b")
bs def hashFile(filename):
# For large files, if we read it all together it can lead to memory overflow, So we take a blocksize to read at a time
BLOCKSIZE = 65536
hasher = hashlib.md5()
with open(filename, 'rb') as file:
# Reads Community Discussions
Trending Discussions on elman
QUESTION
I am learning about RNNs, and am trying to code one up using PyTorch. I have some trouble understanding the output dimensions
Here is some code for a simple RNN architecture
...ANSWER
Answered 2020-Jun-17 at 14:51In the PyTorch API, the output is a sequence of hidden states during the RNN computation, i.e., there is one hidden state vector per input vector. The hidden state is the last hidden state, the state the RNN ends with after processing the input, so test_out[:, -1, :] = test_h.
Vector y in your diagrams is the same as a hidden state Ht, it indeed has 4 numbers, but the state is different for every time step, so you have 4 number for every time step.
The reason why PyTorch separates the sequence of outputs = hidden states (it's not the same in LSTMs, though) is that you can have a batch of sequences of different lengths. In that case, the final state is not simply test_out[:, -1, :], because you need to select final states based on the lengths of individual sequences.
QUESTION
I am reproducing the original paper of Elman networks (Elman, 1990) – together with Jordan networks, known as Simple Recurrent Networks (SRN). As far as I can understand, my code correctly implements the forward propagation, while the learning phase is incomplete. I am implementing the network using the low-level API of TensorFlow, in Python.
The Elman network is an artificial neural network composed of two layers, where the hidden layer gets copied as a "context layer," which concatenates with the inputs the next time we run forward propagate the network. Initially, the context layer is initialized with activation = 0.5 and has a fixed weight of 1.0.
My question is on the calculation of gradients, in the backpropagation of the network. In my code, I use tf.assign to update context units with the activations from the hidden layer. Before adding the assignment operator to the graph, TensorBoard shows that GradientDescentOptimizer will learn gradients from all the variables in the graph. After I include this statement, gradients don't show up for the variables in nodes coming "before" the assignment. In other words, I would expect b_1, w_x, w_c, and a_1 to show up in the list of gradients learned by the optimizer, even with the assignment in the graph.
I believe my implementation for the forward propagation is correct because I compared final values for activations using tf.assign and values from another implementation, using plain Numpy arrays. The values are equal.
Finally: is this behavior intentional or am I doing something wrong?
Here's a notebook with the implementation of the network as I described:
https://gist.github.com/Irio/d00b9661023923be7c963395483dfd73
References
Elman, J. L. (1990). Finding Structure in Time. Cognitive Science, 14(2), 179–211. Retrieved from https://crl.ucsd.edu/~elman/Papers/fsit.pdf
...ANSWER
Answered 2019-Jul-08 at 09:22No, assign operations do not backpropagate a gradient. That is on purpose, as assigning a value to a variable is not a differentiable operation. However, you probably do not want the gradient of the assignment, but the gradient of the new value of the variable. You can use that gradient, just do not use it as the output of an assignment operation. For example, you can do something like this:
QUESTION
EncogModel is extremely useful thanks to its use of VersatileMLDataSet and the ability to perform cross validation.
However I can't see a way to create an Elman, Jordan or other RNN. Is this possible using EncogModel?
...ANSWER
Answered 2019-Feb-14 at 02:38EncogModel does not support recurrent neural networks. It would be a decent change to make it do this, because recurrent neural networks are time-series, the EncogModel class would need to be extended to support sequences.
QUESTION
I am trying to build an Elman simple RNN as described here.
I've built my model using Keras as follows:
...ANSWER
Answered 2019-Jan-11 at 17:24- SimpleRNN layer expects the inputs of dimensions
(seq_length, input_dim)that is (7,7) in your case. - Also if you want output at each time-step, you need to use
return_sequence=True, by default which isfalse. This way you can compare the output at time-step.
So the model architecture will be something like this:
QUESTION
I'm very new to neural networks, and I'm trying to make an Elman RNN which generates text. I'm using Encog in Java. No matter what I feed the network, it takes a very long time to train, and it always falls into a repeating sequence of characters. I'm sort of new to neural networks, so I just want to make sure I have the concept correct. I'm not going to bother with sharing code because Encog does all the hard stuff anyway.
The way I'm training the network is I'm making a data pair for every character in the training data, in which the input is the character, and the output is the next character. All of that is in one training set. That's pretty much all that I had to write, because Encog handles everything else. Then I just feed a character into the network and it returns a character, then feed that one in, and the next one and so on. I'm assuming people usually have an end character or something so that the network tells you when to stop, but I just make it stop at 1000 characters to get a good sample of text. I know that Elman networks are supposed to have context nodes, but I think Encog is handling that for me. The context nodes must be doing something because the same character doesn't always have the same output.
...ANSWER
Answered 2018-Aug-03 at 06:30In a small dataset, RNNs perform poorly since it has to learn everything from start. So if you have a small dataset (usually a training set less than 10 million characters is considered as small) then gather more data. If you're getting a repeating sequence of characters, it okay. All you need to do is to train longer.
One more suggestion is to switch from character level to word level model. You will be able to generate less gibberish output from it.
QUESTION
...coming from TensorFlow, where pretty much any shape and everything is defined explicitly, I am confused about Keras' API for recurrent models. Getting an Elman network to work in TF was pretty easy, but Keras resists to accept the correct shapes...
For example:
...ANSWER
Answered 2018-May-31 at 08:38The documentation touches on the expected shapes of recurrent components in Keras, let's look at your case:
- Any RNN layer in Keras expects a 3D shape
(batch_size, timesteps, features). This means you have timeseries data. - The RNN layer then iterates over the second, time dimension of the input using a recurrent cell, the actual recurrent computation.
- If you specify
return_sequencesthen you collect the output for every timestep getting another 3D tensor(batch_size, timesteps, units)otherwise you only get the last output which is(batch_size, units).
Now returning to your questions:
- You mention vectors but
shape=(2,)is a vector so this doesn't work.shape=(2,1)works because now you have 2 vectors of size 1, these shapes excludebatch_size. So to feed vectors of size to you needshape=(how_many_vectors, 2)where the first dimension is the number of vectors you want your RNN to process, the timesteps in this case. - To chain RNN layers you need to feed 3D data because that what RNNs expect. When you specify
return_sequencesthe RNN layer returns output at every timestep so that can be chained to another RNN layer. - States are collection of vectors that a RNN cell uses, LSTM uses 2, GRU has 1 hidden state which is also the output. They default to 0s but can be specified when calling the layer using
initial_states=[...]as a list of tensors.
There is already a post about the difference between RNN layers and RNN cells in Keras which might help clarify the situation further.
QUESTION
I am having problems with reproducing example from Julia Elman's book models.py
...ANSWER
Answered 2018-May-22 at 08:25Yes,what Alexandr Tartanov suggested works fine.We need to pass arguments with source
QUESTION
I am training an Elman network (a specific type of Recurrent Neural Network) and for that reason my datasets (input/target) need to be cell arrays (so that the examples are considered as a sequence by the train function).
But, I don't manage to trigger the use of a validation and test set by the train function.
Here is an example, where I want a validation and test set to be used but the train function is not using any (I know that by looking at the performance plot from the 'nntraintool' wizard or by looking at the content of the 'tr' variable in my example below). It seems the "divideind" property and indexes are ignored.
...ANSWER
Answered 2018-Apr-04 at 15:43I found the answer, I need to add:
QUESTION
I want to implement a CNTK Network that is able to learn a linear motion model. Similar to a Kalman Filter task the Network will receive measurement data from an accelerometer and should output a change in the current position.
dx = v_0 * dt + 1/2 * a * dt²
...ANSWER
Answered 2017-Jul-18 at 01:56Since it was not clear to me whether you want to be summing the dv or not, I will describe the case where you want v = cumsum(dv); you can easily replace that with a learnable function. I'm assuming 3-d acceleration data.
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