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You will use InceptionV3 which is similar to the model originally used in DeepDream. Original_img = download(url, max_dim=500)ĭisplay.display(display.HTML('Image cc-by: Von.grzanka'))ĭownload and prepare a pre-trained image classification model. # Downsizing the image makes it easier to work with. Image_path = tf._file(name, origin=url)ĭisplay.display((np.array(img))) # Download an image and read it into a NumPy array. Let's demonstrate how you can make a neural network "dream" and enhance the surreal patterns it sees in an image.įor this tutorial, let's use an image of a labrador.
Deep dreamer app movie#
This process was dubbed "Inceptionism" (a reference to InceptionNet, and the movie Inception). The image is then modified to increase these activations, enhancing the patterns seen by the network, and resulting in a dream-like image. It does so by forwarding an image through the network, then calculating the gradient of the image with respect to the activations of a particular layer. Similar to when a child watches clouds and tries to interpret random shapes, DeepDream over-interprets and enhances the patterns it sees in an image. This tutorial contains a minimal implementation of DeepDream, as described in this blog post by Alexander Mordvintsev.ĭeepDream is an experiment that visualizes the patterns learned by a neural network.