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**Language Proof And Logic Homework Help**

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The idea of having to write tests of a neural network is new to me so any help would be great. Basically, the tests I need to write is as follows. I am running a neural network and want to find the most accurate model which I can identify in an image based on the weights and biases (i.e. the activation function). What method should I use? I am basically using the following data set (I'm only up to round 2 so far): I thought I should get a prediction for the 5th image, train on all the previous images (so I have 10 samples for the first 2 tests) and then test on the 5th sample. I used the accuracy metric to train and test as follows (I've included a line with some outputs that I got from Keras): I get a bit confused on how to get the prediction for the 5th image. I have a data set where there are 50 images of a tree and the network has a hidden layer size of 8. The output layer size is 1 (there is only one class) Based on what I've seen I would think that the output layer would be shape (50,1) First, thanks for doing this! It is a great help. The hidden layer size of 8 confused me a little and the output layer size of 1 confused me a bit more as it is a binary classification problem. So based on what you've said, it would seem as if the output layer should be (50,8) Basically I want to output to say 5 if the tree is a tree and 0 if it is not. It is important to note that these are just labels from a training set, not an output from the network - just a category (1 or 0) which can be predicted. I'm having trouble understanding the activation function. I understand that the sigmoid would activate every neuron of the hidden layer but why would I then use the'softmax'? Your first question about the hidden layer size is spot on. Hidden layers should be either the same size of the inputs or 2 times their size (why twice their size is beyond me). For a problem of 50 inputs the hidden layer should be 50 x 8 = 400 The activation function for the hidden layer is simply an activation function that applies to every neuron. The sigmoid is simply the most commonly used example. I would say that the

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