I generated my results on an Amazon EC2 g2.2xlarge instance with GPU support enabled so the script finished up within 30 minutes. base-model $CAFFE_ROOT/caffe/models/bvlc_googlenet \Īnd the visualization process will kick off. I then executed the Python script using the following command: $ python visualize_layers.py \ This script requires three command line arguments: the -base-model directory where our Caffe model lives, the path to our input -image, and finally the -output directory where our images will be stored after being passed through the network.Īs you’ll also see, I am using a try/except block to catch any layers that cannot be used for visualization.īelow is the image that I inputted to the network: Figure 1: The iconic input image of Dr. Print(" processing layer ``".format(layer)) # perform visualizing using the current layer # extract the filename and extension of the input imageįilename = args.image # filter warnings, initialize bat country, and grab the layer names of # construct the argument parser and parse the argumentsĪp.add_argument("-b", "-base-model", required=True, help="base model path")Īp.add_argument("-i", "-image", help="path to image file")Īp.add_argument("-o", "-output", help="path to output directory") Looking for the source code to this post? Jump Right To The Downloads Section Visualizing every layer of GoogLeNet with Pythonīelow follows my Python script to load an image, loop over every layer of the network, and then write each output image to file: # import the necessary packages
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