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Visualize network features using deep dream

`I = deepDreamImage(net,layer,channels)`

`I = deepDreamImage(net,layer,channels,Name,Value)`

returns an image with additional options specified by one or more
`I`

= deepDreamImage(`net`

,`layer`

,`channels`

,`Name,Value`

)`Name,Value`

pair arguments.

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`

| `vgg16`

| `vgg19`