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append ( img ) return levels def lap_merge ( levels ): '''Merge Laplacian pyramid''' img = levels for hi in levels : with tf. shape ( img ), ) hi = img - lo2 return lo, hi def lap_split_n ( img, n ): '''Build Laplacian pyramid with n splits''' levels = for i in range ( n ): img, hi = lap_split ( img ) levels. conv2d ( img, k5x5, , 'SAME' ) lo2 = tf. float32 ) def lap_split ( img ): '''Split the image into lo and hi frequency components''' with tf.
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as_graph_def () strip_def = strip_consts ( graph_def, max_const_size = max_const_size ) code = """ function load() ) grad = g return np. input = rename_func ( s ) if s != '^' else '^' + rename_func ( s ) return res_def def show_graph ( graph_def, max_const_size = 32 ): """Visualize TensorFlow graph.""" if hasattr ( graph_def, 'as_graph_def' ): graph_def = graph_def. as_bytes ( "" % size ) return strip_def def rename_nodes ( graph_def, rename_func ): res_def = tf. tensor_content ) if size > max_const_size : tensor. Layers = feature_nums = ) for name in layers ] print ( 'Number of layers', len ( layers )) print ( 'Total number of feature channels:', sum ( feature_nums )) # Helper functions for TF Graph visualization def strip_consts ( graph_def, max_const_size = 32 ): """Strip large constant values from graph_def.""" strip_def = tf.
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Impatient readers can start with exploring the full galleries of images generated by the method described here for GoogLeNet and VGG16 architectures. In this notebook we are going to present a few tricks that allow to make these visualizations both efficient to generate and even beautiful. The internal image representations may seem obscure, but it is possible to visualize and interpret them. The parameters of these transformations were determined during the training process by a variant of gradient descent algorithm. It consists of a set of layers that apply a sequence of transformations to the input image. The network under examination is the GoogLeNet architecture, trained to classify images into one of 1000 categories of the ImageNet dataset.