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Keras - Connect one layer to another in backwards direction, how?
Closed 9 months ago.
i need help with the code of custom layer, i'm trying to connecter layer C to A in backwards direction, here's a picture illustrating :
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How to fix "AttributeError: module 'tensorflow' has no attribute 'get_default_graph'"?
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Closed 1 year ago.
I am not able to create the model in Keras as I am running into some errors. Please help
Error while implementing model
Tensorflow 2.0 does not have 'get_default graph'. So, remove it from the function 'create model'. It should work then.
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I have been looking into deep learning frameworks lately and have been wondering about the origin of the name of PyTorch.
With Keras, their home page nicely explains the name's origin, and with something like TensorFlow, the reasoning behind the name seems rather clear. For PyTorch, however, I cannot seem to come across why it is so named.
Of course, I understand the "Py-" prefix and also know that PyTorch is a successor in some sense of Torch. But I am still wondering: what is the original idea behind the "-Torch" part? Is it known what the origin of the name is?
Here a short answer, formed as another question:
Torch, SMORCH ???
PyTorch developed from Torch7. A precursor to the original Torch was a library called SVM-Torch, which was developed around 2001. The SVM stands for Support Vector Machines.
SVM-Torch is a decomposition algorithm similar to SVM-Light, but adapted to regression problems, according to this paper.
Also around this time, G.W.Flake described the sequential minimal optimization algorithm (SMO), which could be used to train SVMs on sparse data sets, and this was incorporated into NODElib.
Interestingly, this was called the SMORCH algorithm.
You can find out more about SMORCH in the NODElib docs
Optimization of the SVMs is:
performed by a variation of John Platt's sequential minimal
optimization (SMO) algorithm. This version of SMO is generalized
for regression, uses kernel caching, and incorporates several
heuristics; for these reasons, we refer to the optimization
algorithm as SMORCH.
So SMORCH =
Sequential
Minimal
Optimization
Regression
Caching
Heuristics
I can't answer definitively, but my thinking is "Torch" is a riff or evolution of "Light" from SVM-Light combined with a large helping of SMORCHiness. You'd need to check in with the authors of SVMTorch and SVM-Light to confirm that this is indeed what "sparked" the name. It is reasonable to assume that the "TO" of Torch stands for some other optimization, rather than SMO, such as Tensor Optimization, but I haven't found any direct reference... yet.
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Closed 4 years ago.
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I have my data already standardized with the help of StandardScaler() in Python. while applying Lasso Regression do I need to set the normalize parameter True or not and why?
from sklearn import StandardScaler()
scaler=StandardScaler()
x_new=scaler.fit_transform(x)
Now, i want to use Lasso Regression.
from sklearn.linear_model import Lasso
lreg=Lasso(alpha=0.1,max_iter=100,normalize=True)
I want to know if 'normalize=True' is still needed or not?
Standarize and Normalize are two different actions. If you do both without knowing what they do and why you do it, you'll end up loosing accuracy.
Standarization is removing the mean and dividing by the deviation. Normalization is putting everything between 0 and 1.
Depending on the penalisation (lasso,ridge, elastic net) you'll prefer one over the other, but it's not recommended to to do both.
So no, it's not needed.
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How to understand the format type of libsvm of Spark MLlib?
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How can I read LIBSVM models (saved using LIBSVM) into PySpark?
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Closed 4 years ago.
I am reading Binary classification used in SparkML data. I read the JavaCode of Spark, I am also aware of Binary classification but I am not able to understand, how these data are generated. for example https://github.com/apache/spark/blob/master/data/mllib/sample_binary_classification_data.txt
this link is sample for binary_classifcation if I want to generate these type of data, how to do that?
Usually, the first column is the class label (in this case 0 / 1), the others columns are the values of the features.
To generate the data yourself, you can use a random generator, for instance.
But it is depend on the problem you are working on.
If you need to download datasets to apply classification algorithms you can use repositories, such as: UCI Machine Learning Repository https://archive.ics.uci.edu/ml/index.php
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Classifying sequences of different lengths [duplicate]
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Closed 5 years ago.
Suppose the input to the model is a series of vectors, each with equal length. However, the number of vectors in each input can change. I want to make an LSTM model using Keras, but if I were to write
input = keras.layers.input(dims)
img_out = keras.layers.recurrent.LSTM(16)
Then what would I put for "dims"? Thanks so much.
You can fix an upper bound for dims. When the input is less than dims, you can pad the rest with zero vector.