Download Google's pretrained network first, and then unpack the tensorflow_inception_graph.pb file from the archive.
You can use the following code in the command line.
wget https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip && unzip inception5h.zipThe network we use here is the GoogLeNet architecture, trained to classify images into one of 1000 categories of the ImageNet dataset. It consists of a set of layers that apply a sequence of transformations to the input image. The parameters of these transformations were determined during the training process by a variant of gradient descent algorithm.
Of course you can use your own model. Put your neural network model here, and set its filename to model_fn variable in dream.py.
Note that you probably also need to update those layers' names and related functions in the code instead of using the ones.