Visualize network features using deep dream

This function implements a version of deep dream that uses a multi-resolution image pyramid and Laplacian Pyramid Gradient Normalization to generate high-resolution images. For more information on Laplacian Pyramid Gradient Normalization, see this blog post: DeepDreaming with TensorFlow.

All functions for deep learning training, prediction, and validation in
Deep Learning
Toolbox™ perform computations using single-precision, floating-point arithmetic.
Functions for deep learning include `trainNetwork`

, `predict`

,
`classify`

, and
`activations`

.
The software uses single-precision arithmetic when you train networks using both CPUs and
GPUs.

[1] *DeepDreaming with TensorFlow*.
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/deepdream/deepdream.ipynb

`activations`

| `alexnet`

| `googlenet`

| `squeezenet`

| `vgg16`

| `vgg19`