I have a model that's trained to detect various concepts on biomedical papers. I'm using the last dense layer before the output to use as vector embeddings for these medical papers. I then use k-means on the embedding vectors to cluster the papers together. I prefer this over a pure unsupervised approach as these embeddings will be forced to focus on biological similarity over biological & semantic. I have about 10M papers and each cluster needs to have 50 elements.

I have a Variational autoencoder model created in Keras.Encoder is built with three 3D Convolutional layers Flatten Dense layer. Decoder is built with three 3D Transposed Convolutional layers to reconstruct the input 3D images. My goal is to replace Flatten and Dense layer in Encoder with 1x1x1 Convolutional layer. Any ideas how to do that?

Artificial neural networks have two main hyperparameters that control the architecture or topology of the network: the number of layers and the number of nodes in each hidden layer. You must specify values for these parameters when configuring your network. The most reliable way to configure these hyperparameters for your specific predictive modeling problem is via systematic experimentation with a robust test harness. This can be a tough pill to swallow for beginners to the field of machine learning, looking for an analytical way to calculate the optimal number of layers and nodes, or easy rules of thumb to follow. In this post, you will discover the roles of layers and nodes and how to approach the configuration of a multilayer perceptron neural network for your predictive modeling problem.

Building neural networks is at the heart of any deep learning technique. Neural networks is a series of forward and backward propagations to train paramters in the model, and it is built on the unit of logistic regression classifiers. This post will expand based on the math of logistic regression to build more advanced neural networks in mathematical terms. A neural network is composed of layers, and there are three types of layers in a neural network: one input layer, one output layer, and one or many hidden layers. Each layer is built based on the same structure of logistic regression classifier, with a linear transformation and an activation function.

In a neural network, each neuron is connected to numerous other neurons, allowing signals to pass in one direction through the network from input to output layers, including through any number of hidden layers in between (see Figure 1). Forward propagation is the the process of multiplying the various input values of a particular neuron by their associated weights, summing the results, and scaling or "squashing" the values back between a given range before passing these signals on to the next layer of neurons. The activation function keeps values forward to subsequent layers within an acceptable and useful range, and forwards the output. Hidden and output layer neurons possess activation functions, but input layer neurons do not.