How is a neural network called that is NOT convolutional

How is a neural network called that is NOT convolutional

By : Leonid Gaidai
Date : November 22 2020, 07:01 PM
this one helps. Non-recurrent networks are called feedforward neural networks. You could add fully connected to emphasize that it is not convolutional.
code :

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Convolutional Neural Network (CNN) with max-pooling

Convolutional Neural Network (CNN) with max-pooling

By : Jesús Pérez
Date : March 29 2020, 07:55 AM
it fixes the issue I've implemented a simple CNN program with Python that can machine learn on the MNIST data set. I've implemented 3 layers: ,
[updated] For forward propagation for max pooling use :
code :
activationPrevious = np.copy(activation)
skimage.measure.block_reduce(activation, block_size=(1, 1, 2, 2), func=np.max)
maxs = activations.repeat(2, axis=2).repeat(2, axis=3)
mask = np.equal(activationPrevious, maxs).astype(int)

delta = delta.repeat(2, axis=2).repeat(2, axis=3)
delta = np.multiply(delta, mask)
Can I view convolutional neural network as fully connected neural network

Can I view convolutional neural network as fully connected neural network

By : Mani
Date : March 29 2020, 07:55 AM
I hope this helps you . In short, yes. You can see CNN as (among other possible interpretations):
neural net with convolutional operation and gradients computed directly for it (typical approach)
code :
X2     h1 = X1v11+X2v21+X3v31
X3     h2 = X1v12+X2v22+X3v32
                   /-patch 1 -- shared fully connected net\ 
Input --splitting----patch 2 -- shared fully connected net--merging---
                   .                                      .
                   .                                      . 
                   \-patch K -- shared fully connected net/ 
How to run a convolutional neural network on new data

How to run a convolutional neural network on new data

By : Keepup
Date : March 29 2020, 07:55 AM
To fix the issue you can do You can not use mnist_classifier.evaluate without a y paramater, because you have nothing to evaluate.
Instead, use y = mnist_classifier.predict(x=x) to get results, and have a look at them yourself to know if they are correct or not.
Convolutional Neural Network Loss

Convolutional Neural Network Loss

By : Repelsteeltje
Date : March 29 2020, 07:55 AM
should help you out No. Tensorflow loss functions typically accept tensors as input and also outputs a tensor. So np.array() wouldn't work.
In case of CNNs, you'd generally come across loss functions like cross-entropy, softmax corss-entropy, sigmoid cross-entropy etc. These are already in-built in tf.losses module. So you can use them directly. The loss function that you're trying to apply looks like a Mean-squared loss. This is built in tf.losses as well. tf.losses.mean_squared_error.
Groups in Convolutional Neural Network / CNN

Groups in Convolutional Neural Network / CNN

By : user2522597
Date : March 29 2020, 07:55 AM
hop of those help? Perhaps you're looking up an older version of the docs. 1.0.1 documentation for nn.Conv2d expands on this.
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