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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.

When you train a network using the `trainNetwork`

function, or when you use prediction or validation functions
with `DAGNetwork`

and
`SeriesNetwork`

objects, the software performs these computations using single-precision, floating-point
arithmetic. Functions for training, prediction, and validation 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/docs/blob/master/site/en/tutorials/generative/deepdream.ipynb

`activations`

| `alexnet`

| `vgg16`

| `vgg19`

| `googlenet`

| `squeezenet`