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How to view initialized weights (i.e. before training)?

By : Pell79
Date : October 18 2020, 01:08 AM
This might help you I am using Keras to generate a simple single layer feed forward network. I'd like to get a better handle on the values of the weights when they are initialized via the kernel_initializer argument. , Just use get_weights() on the model. For example:
code :
i = Input((2,))
x = Dense(5)(i)

model = Model(i, x)

print model.get_weights()
[array([[-0.46599612,  0.28759909,  0.48267472,  0.55951393,  0.3887372 ],
   [-0.56448901,  0.76363671,  0.88165808, -0.87762225, -0.2169953 ]], dtype=float32), 
 array([ 0.,  0.,  0.,  0.,  0.], dtype=float32)]

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Training a neural network with complex valued weights (initialized complex valued weights real valued inputs)

By : kdh
Date : March 29 2020, 07:55 AM
help you fix your problem The easiest way to do this is to divide your feature into the real and imaginary components. I've done similar work with vector input from a leap motion and it significantly simplifies things if you divide vectors into their component axis.

Is weights are diffrent for each training Example in perceptrons

By : 纪锐淳
Date : March 29 2020, 07:55 AM
To fix this issue Start with a single node in the Neural network. It's output is sigmoid function applied to the linear combination of input as shown below.

Can I add sample weights to the training dataset?

By : floydridley2004
Date : March 29 2020, 07:55 AM
hope this fix your issue Yes, it can be done. If your task is binary classification you can use WeightedLogistic (label, probability, weight).
Typically weight will be an Input() which you can hook up to a stream in your data (assuming you are using CNTKTextFormatReader you can just have a |weight stream).

How to view network weights and bias during training

By : Kelsey Cahill
Date : March 29 2020, 07:55 AM
around this issue Based on the Keras documentation, all you need to do maybe is just run the command line:
tensorboard --logdir=logs

Training with pre-trained weights

By : Rahul Swami
Date : October 03 2020, 12:00 AM
wish help you to fix your issue When you do model2.set_weights, you changed the weights of model2. That's all.
You can see the weights the same way: w2 = model2.get_weights(). Then print w2 in a convenient way.
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