# deepDreamImage

Visualize network features using deep dream

## Description

returns an array of images that strongly activate the channels
`I`

= deepDreamImage(`net`

,`layer`

,`channelIdx`

)`channels`

within the network `net`

of the
layer with numeric index or name given by `layer`

. These images
highlight the features learned by a network.

returns an image with additional options specified by one or more name-value
arguments.`I`

= deepDreamImage(___,`Name,Value`

)

## Examples

## Input Arguments

## Output Arguments

## Algorithms

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.

By default, the software performs computations using single-precision, floating-point arithmetic to train a neural network using the `trainnet`

function. The `trainnet`

function returns a network with single-precision learnables and state parameters.

When you use prediction or validation functions with a `dlnetwork`

object with single-precision learnable and state parameters, the software performs the computations using single-precision, floating-point arithmetic.

When you use prediction or validation functions with a `dlnetwork`

object with double-precision learnable and state parameters:

If the input data is single precision, the software performs the computations using single-precision, floating-point arithmetic.

If the input data is double precision, the software performs the computations using double-precision, floating-point arithmetic.

## References

[1] *DeepDreaming with TensorFlow*.
https://github.com/tensorflow/docs/blob/master/site/en/tutorials/generative/deepdream.ipynb

## Version History

**Introduced in R2017a**

## See Also

`imagePretrainedNetwork`

| `dlnetwork`

| `trainingOptions`

| `trainnet`

| `testnet`

| `predict`

| `forward`

| `minibatchpredict`

| `scores2label`