## Hot questions for Using Neural networks in gaussian

Question:

I'm trying to add Gaussian noise to a layer of my network in the following way.

def Gaussian_noise_layer(input_layer, std): noise = tf.random_normal(shape = input_layer.get_shape(), mean = 0.0, stddev = std, dtype = tf.float32) return input_layer + noise

I'm getting the error:

ValueError: Cannot convert a partially known TensorShape to a Tensor: (?, 2600, 2000, 1)

My minibatches need to be of different sizes sometimes, so the size of the input_layer tensor will not be known until the execution time.

If I understand correctly, someone answering Cannot convert a partially converted tensor in TensorFlow suggested to set shape to tf.shape(input_layer). However then, when I try to apply a convolutional layer to that noisy layer I get another error:

ValueError: dims of shape must be known but is None

What is the correct way of achieving my goal of adding Gaussian noise to the input layer of a shape unknown until the execution time?

Answer:

To dynamically get the shape of a tensor with unknown dimensions you need to use `tf.shape()`

For instance

import tensorflow as tf import numpy as np def gaussian_noise_layer(input_layer, std): noise = tf.random_normal(shape=tf.shape(input_layer), mean=0.0, stddev=std, dtype=tf.float32) return input_layer + noise inp = tf.placeholder(tf.float32, shape=[None, 8], name='input') noise = gaussian_noise_layer(inp, .2) noise.eval(session=tf.Session(), feed_dict={inp: np.zeros((4, 8))})

Question:

What is the fastest way to change the standard deviation of a Gaussian noise layer in Keras during training?

Currently I am reloading the whole network with the adapted standard deviation every iteration, which is really slow.

Thank you in advance!

Answer:

Can you try using keras backend variables?

from keras import backend as K self.std=0.5 self.std_var = K.variable(value=std)

When building the network.

# instantiate stddev = std_var(0.8) g = GaussianNoise(stddev)

During training (possibly inside a loop).

K.set_value(stddev.std_var, <new_std_val>)

Try this snippet and see whether it works.

Question:

I was wondering if there is a way in which I can remove a gaussian noise layer

tf.keras.layers.GaussianNoise(0.1),

after using.

model.fit()

so that when using my neural net in applications it will not be affected by such layers.

model.save("network.h5")

Answer:

`tf.keras.layers.GaussianNoise()`

is a regularization layer. You don't need to worry about it during prediction. It is active only during training time.

Question:

I converted a pretrained keras model to use it with Tensorflow.js following the steps in this guide

Now, when I try to import it to javascript using

`const model = tf.loadModel("{% static "keras/model.json" %}");`

The following error shows up:

Uncaught (in promise) Error: Unknown layer: GaussianNoise. This may be due to one of the following reasons: 1. The layer is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code. 2. The custom layer is defined in JavaScript, but is not registered properly with tf.serialization.registerClass(). at new t (errors.ts:48) at deserializeKerasObject (generic_utils.ts:239) at deserialize (serialization.ts:31) at t.fromConfig (models.ts:940) at deserializeKerasObject (generic_utils.ts:274) at deserialize (serialization.ts:31) at models.ts:302 at common.ts:14 at Object.next (common.ts:14) at i (common.ts:14)

I'm using 0.15.3 version of Tensorflow.js, imported this way:

`<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@0.15.3/dist/tf.min.js"></script>`

I trained my neural network with Tensorflow 1.12.0 and Keras 2.2.4

Answer:

You are using the layer `tf.layer.gaussianNoise`

that is not supported yet by tfjs.

Consider changing this layer by another one supported

Question:

I'd like to calculate the receptive fields (e.g. Gaussian) for spiking neural networks in python. Let's say that I want to encode the iris data set and transform it into spike trains. I work with Brian framework, and I'm looking for a way to encode my data sets.

Is there any way to do it automatically? Or even any site explaining the trasnformation process? I've read several papers but this process is explained partially ...

Thanks in advance

Answer:

For overlapping Gaussian RF, you need to know the minimum (`I_min`

) and maximum (`I_max`

) for **each** variable. Then, (again for **each** variable) you create an array of `N`

input neurons located at the peaks of `N`

overlapping Gaussians. Use the following formulas to space out the neurons evenly over the variable range (this is of course pseudocode):

range = I_max - I_min for (i = 1..N) gaussian_i_mean = I_min + range * (2*i - 3) / (2 * (N - 2)) gaussian_i_sd = range / (beta * (N - 2)) end for

`beta`

controls the width of the Gaussian. See this paper for more details.