I’m a bit confused about the number of layers that are used in Keras models. The documentation is rather opaque on the matter. According to Jason Brownlee the first layer technically consists of two layers, the input layer, specified by input_dim and a hidden layer. See the first questions on his blog. In all of the Keras documentation the first
Tag: neural-network
Tensorflow Executor failed to create kernel. Unimplemented: Cast string to float is not supported
I’m trying to build a custom CNN classifier for a load of cancer images (.png) using Tensorflow 1.1.0 and TFLearn 0.3.1 by largely following someone else’s CNN classifier here, however when I try to fit my model Tensorflow is throwing out the following errors: I am using tflearn.data_utils.image_preloader to read the png files in however I have also tried using
Export weights of neural network using tensorflow
I wrote neural-network using tensorflow tools. everything working and now I want to export the final weights of my neural network to make a single prediction method. How can I do this? Answer You will need to save your model at the end of training by using the tf.train.Saver class. While initializing the Saver object, you will need to pass
Ordering of batch normalization and dropout?
The original question was in regard to TensorFlow implementations specifically. However, the answers are for implementations in general. This general answer is also the correct answer for TensorFlow. When using batch normalization and dropout in TensorFlow (specifically using the contrib.layers) do I need to be worried about the ordering? It seems possible that if I use dropout followed immediately by
Knight’s Tour using a Neural Network
I was looking at the knights tour problem and decided to have a go at implementing it in python using a neural network to find solutions. The general explanation of the method can be found on Wikipedia While I think I have implemented it correctly (I can’t see anything else that is wrong), it doesn’t work, it updates a few