adagrad | A simple implementation of adagrad | Machine Learning library
kandi X-RAY | adagrad Summary
kandi X-RAY | adagrad Summary
A simple python implementation of adagrad, created with the help of. The code is purposefully kept simple so that it is easy to understand. For efficiency, it may be better to convert some of the data structures that are currently lists to numpy arrays.
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Top functions reviewed by kandi - BETA
- Calculate the gradient of the loss function .
adagrad Key Features
adagrad Examples and Code Snippets
Lines of Code : 62License : Non-SPDX (Apache License 2.0)
Copydef __init__(
self,
learning_rate: float,
initial_accumulator: float = 0.1,
l1_regularization_strength: float = 0.0,
l2_regularization_strength: float = 0.0,
use_gradient_accumulation: bool = True,
clip_weigh def __init__(
self,
learning_rate: float,
momentum: float,
use_nesterov: bool = False,
exponent: float = 2,
beta2: float = 1,
epsilon: float = 1e-10,
use_gradient_accumulation: bool = True,
clip_w 
License : No LicenseCopydef adagrad(cost, params, lr, eps=1e-10):
grads = T.grad(cost, params)
caches = [theano.shared(np.ones_like(p.get_value())) for p in params]
new_caches = [c + g*g for c, g in zip(caches, grads)]
c_update = [(c, new_c) for c, new_c in Community Discussions
Trending Discussions on adagrad
QUESTION
I am trying to extract the last layer of a classification model trained on some data. The first layer is an Embedding layer, followed by the bilstm and the followed by the output dense layer. My code is sown below. I keep getting a 4d output (1,38,300,300) instead of a 3d (1,38,300). 1 is the sample size, 38 is the max length of the sentence, and 300 is the word2vec length.
ANSWER
Answered 2021-Apr-23 at 07:52The correct way to get any intermediate layer output is to create a sub-model that expects the same input of your trained model. In your case, the error raises because you pass to your trained model the 3D embedding matrix while you have to pass the same data you use for training (2D data whit integer-encoded words).
Here I produce a dummy example to extract correctly any intermediate output from your model.
Create dummy data:
QUESTION
Below is the code of what I'm trying to do, but my accuracy is always under 50% so I'm wondering how should I fix this? What I'm trying to do is use the first 1885 daily unit sale data as input and the rest of the daily unit sale data from 1885 as output. After train these data, I need to use it to predict 20 more daily unit sale in the future The data I used here is provided in this link https://drive.google.com/file/d/13qzIZMD6Wz7e1GpOsNw1_9Yq-4PI2HrC/view?usp=sharing
...ANSWER
Answered 2021-Apr-19 at 12:16Two mistakes:
Accuracy is meaningless in regression settings, such as yours here (it is meaningful only for classification ones); see What function defines accuracy in Keras when the loss is mean squared error (MSE)? (the argument is identical when MAE loss is used, like here). Your performance measure here is the same with your loss (i.e. MAE).
We never use
softmaxactivations in anything but the final layer of a classification model; replace bothsoftmaxactivation functions used in your model withrelu(keep the last layer as is, as no activation meanslinear, which is indeed the correct one for regression).
QUESTION
I'm having trouble fine-tuning the decomposable-attention-elmo model. I have been able to download the model: wget https://s3-us-west-2.amazonaws.com/allennlp/models/decomposable-attention-elmo-2018.02.19.tar.gz. I'm trying to load the model and then fine-tune it on my data using the AllenNLP train command line command.
I also created a custom dataset Reader which is similar to the SNLIDatasetReader and it seems to be working well.
I created a .jsonnet file, similar to what is here, but I'm having trouble getting it to work.
When I use this version:
...ANSWER
Answered 2021-Apr-07 at 01:51We found out on GitHub that the problem was the old version of the model that @hockeybro was loading. The latest version right now is at https://storage.googleapis.com/allennlp-public-models/decomposable-attention-elmo-2020.04.09.tar.gz.
QUESTION
I am trying to parallelize a model with embedding layer, on tensorflow version 2.4.1 . But it is throwing me the following error :
...ANSWER
Answered 2021-Mar-22 at 05:17So finally I figured out the problem, if anyone is looking for an answer.
Tensorflow does not have complete GPU implementation of Adagrad optimizer as of now. ResourceSparseApplyAdagradV2 operation gives error on GPU, which is integral to embedding layer. So it can not be used with embedding layer with data parallelism strategies. Using Adam or rmsprop works fine.
QUESTION
My validation data are 150 images, but when i try to use my model to predict them my predictions are of length 22 I don't understand why?
...ANSWER
Answered 2021-Mar-21 at 14:33If you want to have the whole predictions you should store predictions of each individual batch and concatenate them at the end of iterations
QUESTION
i have this CNN model with 3 block of VGG architecture
...ANSWER
Answered 2021-Jan-28 at 08:28- No - because valuation loss not increasing
- Your plots look fine. It is expected that the training process goes slower
- Yes, but it doesn't make sense. If you train any model for infinity - its performance will permanently improved - e.g. you can get 89.5% accuracy (which is better than 89.48%) if you train it for year.
- Try decaying learning rate with different schedules
QUESTION
I would like to use TFP to write a neural network where the output are the probabilities of a categorical variable with 3 classes, and train it using the negative log-likelihood.
As I'm moving my first steps with TF and TFP, I started with a toy model where the input layer has only 1 unit receiving a null input, and the output layer has 3 units with softmax activation function. The idea is that the biases should learn (up to an additive constant) the log of the probabilities.
Here below is my code, true_p are the true parameters I use to generate the data and I would like to learn, while learned_p is what I get from the NN.
ANSWER
Answered 2021-Jan-05 at 19:35I believe the default argument to Categorical is not the vector of probabilities, but the vector of logits (values you'd take softmax of to get probabilities). This is to help maintain precision in internal Categorical computations like log_prob. I think you can simply eliminate the softmax activation function and it should work. Please update if it doesn't!
EDIT: alternatively you can replace the tfd.Categorical with
lambda p: tfd.Categorical(probs=p)
but you'll lose the aforementioned precision gains. Just wanted to clarify that passing probs is an option, just not the default.
QUESTION
Im started to work with Keras a little bit but i run in to this issue where it tells me that no gradients are provided. I know that this question was posted like 100 times before but the solutions are always talking about using GradientTape but i don't see why i should do this (also i don't even understand what it does)
...ANSWER
Answered 2021-Jan-04 at 06:28I fixed your code. When you get that error, there is not path in the graph between your loss function and your trainable variables, which was true in your case.
- You don't have labels to train your autoencoder. I added train_x as your labels.
- I don't think SparseCategoricalCrossentropy would work for the architecture you have defined. So, I changed it to BinaryCrossEntropy
- When you assigned a name to a vector, spaces are not allowed, so I changed "AutoEncoder Input" to "AutoEncoder_Input"
Here is the code
QUESTION
I want to use an already trainned model to build a new one in 2 step.
consider already trained model as model_109 acording to the figure with a first layer of LSTM (Extracteur_LSTM) and a second of dense and a last one of 1 dense output.
my goal is to feed model_109 and get the output (output1). and as second model, use only the head of model_109 (Extracteur_LSTM) output, merge it to output1 to feed a new model of dense. My final output is model_109 AND output of the new dense model.
...ANSWER
Answered 2020-Dec-01 at 19:07Finaly nail it....
QUESTION
the title is a bit self-explanatory. I need to get the value of a variable before each iteration of the optimisation process of fitting a function to experimental data. The variables are c0 and k, which are just scalars. Using .dataSync() I get an error as follows:
Can not find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize().
The code is as follows:
...ANSWER
Answered 2020-Nov-26 at 08:48Please find the explanation directly in the code
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
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Install adagrad
You can use adagrad like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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