hiddenlayer | Neural network graphs and training metrics | Machine Learning library
kandi X-RAY | hiddenlayer Summary
kandi X-RAY | hiddenlayer Summary
A lightweight library for neural network graphs and training metrics for PyTorch, Tensorflow, and Keras. HiddenLayer is simple, easy to extend, and works great with Jupyter Notebook. It's not intended to replace advanced tools, such as TensorBoard, but rather for cases where advanced tools are too big for the task. HiddenLayer was written by Waleed Abdulla and Phil Ferriere, and is licensed under the MIT License.
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Top functions reviewed by kandi - BETA
- Matches given nodes
- Return the id for a node
- Remove a node from the graph
- Return the outgoing edges of a node
- Build a networkx graph
- Imports the given graph into the matching graph
- Return the framework of the given value
- Adds a node to the graph
- Print numpy arrays
- Convert a tensor
- Draw a summary
- Save the graph to a PDF file
- Return training data
- Returns test data
- List of test labels
- List of train labels
- Prints the progress of the current step
- Match node
- Draw an image
- Tag the graph
- Renders the plot
- Record the given metrics
- Return a copy of the graph
- Replaces the graph in the graph
- Plot images
- Print the summary
hiddenlayer Key Features
hiddenlayer Examples and Code Snippets
Lines of Code : 6License : Strong Copyleft (CC BY-SA 4.0)
Copyinp = Input(shape=(...))
x = Conv2D(...)(inp)
x = Activation(...)(x)
x = Conv2D(...)(x)
mymodel = Model(inp, x)
os.system('E:\7B\Informatik\Schlifelner\raspberry\AI_Test.py')
>>> print('E:\7B\Informatik\Schlifelner\raspberry\AI_Test.py')
aspberry\AI_Test.pyifelner
>>> print('E:\\7B\tensor_list =[]
for i,chunk in enumerate(tensor.chunk(100,dim=0)):
output = hiddenlayer(chunk).squeeze()
tensor_list.append(output)
result = torch.reshape(torch.stack(tensor_list,0), (-1, 1))
with tf.Session() as sess:
# initialize all variables
tf.global_variables_initializer().run()
tf.local_variables_initializer().run()
#this is just because some model's count the input layer and others don't
layerCount = len(model.layers)
lastLayer = layerCount - 1;
hiddenLayer = layerCount -2;
#getting the weights:
hiddenWeights = model.layers[hiddenLayer].get_weightsCommunity Discussions
Trending Discussions on hiddenlayer
QUESTION
I've been trying figure out what I have done wrong for many hours, but just can't figure out. I've even looked at other basic Neural Network libraries to make sure that my gradient descent algorithms were correct, but it still isn't working.
I'm trying to teach it XOR but it outputs -
...ANSWER
Answered 2022-Feb-19 at 19:37- All initial weights must be DIFFERENT numbers, otherwise backpropagation will not work. For example, you can replace
1withmath.random() - Increase number of attempts to
10000
With these modifications, your code works fine:
QUESTION
I am trying to create a neural network and train my own Embeddings. The network has the following structure (PyTorch):
...ANSWER
Answered 2021-Dec-16 at 23:45The output of your embedding layer is [batch, seqlen, F], and you can see in the docs for batchnorm1d that you need to have an input of shape [batch, F, seqlen]. You should change transpose this to get the desired output size:
QUESTION
I have created a mutli-class classification neural network. Training, and validation iterators where created with BigBucketIterator method with fields {'text_normalized_tweet':TEXT, 'label': LABEL}
TEXT = a tweet LABEL = a float number (with 3 values: 0,1,2)
Below I execute a dummy example of my neural network:
...ANSWER
Answered 2021-Dec-15 at 16:39You shouldn't be using the squeeze function after the forward pass, that doesn't make sense.
After removing the squeeze function, as you see, the shape of your final output is [320,3] whereas it is expecting [32,3]. One way to fix this is to average out the embeddings you obtain for each word after the self.Embedding function like shown below:
QUESTION
I'm new to machine learning and now working on a project about time series forecasting.I confused why predicted data after training model isn't similar with actual data.
I'm using tensorflow.js with reactjs,Can anyone help me what wrong with model created? Below is code about that model..
Anyone who help me will appreciated..
...ANSWER
Answered 2021-Aug-15 at 08:24I don't see anything wrong here.
Your model is working just fine. Predicted values will never be the same as actual, unless you overfit the hell out of your model (and then it won't generalize). In any case, your graph shows that the model is learning.
Here is what you can do to get better results -
- A bit more training can be done with more epochs to reduce the loss further.
- If the loss doesn't go further down parameters can be added with a few layers, then the model needs more complexity to learn better. Meaning you need more trainable parameters (more layers, larger layers etc)
QUESTION
I'm working on a neuronnal network and I'm trying to do prediction. For that I have an array of array that containt value and I would like to know what will be the next one.
Just to practice I did something really simple but it don't work (the value returned is wrong), Can you explain me wait I'm missing ?
...ANSWER
Answered 2020-Oct-09 at 10:08Changing the number of hidden layer and add some iteration seems to be the solution, my AI wasn't wrong, just not accurate enought
QUESTION
ANSWER
Answered 2020-Sep-10 at 11:50I personally use figma.com and "draw" it myself, but if you want to create it automatically you should check out this github repository, you might find a nice tool.
QUESTION
I am implementing a neural network and I would like to assess its performance with cross validation. Here is my current code:
...ANSWER
Answered 2020-Apr-28 at 22:05cross_val_score is not the appropritate tool here; you should take manual control of your CV procedure. Here is how, combining aspects from my answer in the SO thread you have linked, as well as from Cross-validation metrics in scikit-learn for each data split, and using accuracy just as an example metric:
QUESTION
I have to train two models: modelA and modelB with different optimizer and hiddenLayers. I would like to take outputs as a combination between them, resulting as
ANSWER
Answered 2020-Feb-02 at 17:05Assuming that you will be providing the value of w the following code might help you:
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