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import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

# Make sure that caffe is on the python path:
caffe_root = './'  # this file is expected to be in {caffe_root}/examples
import sys
sys.path.insert(0, caffe_root + 'python')

import caffe

# configure plotting
plt.rcParams['figure.figsize'] = (10, 10)
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'


    

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# Load the net, list its data and params, and filter an example image.
caffe.set_mode_gpu()
net = caffe.Net('test_templates/conv.prototxt', caffe.TEST)
print("blobs {}\nparams {}".format(net.blobs.keys(), net.params.keys()))

# load image and prepare as a single input batch for Caffe
im = np.array(caffe.io.load_image('test_templates/cat_gray.jpg', color=False)).squeeze()
plt.title("original image")
plt.imshow(im)
plt.axis('off')

im_input = im[np.newaxis, np.newaxis, :, :]
net.blobs['data'].reshape(*im_input.shape)
net.blobs['data'].data[...] = im_input


    

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# helper show filter outputs
def show_filters(net):
    net.forward()
    plt.figure()
    filt_min, filt_max = net.blobs['conv'].data.min(), net.blobs['conv'].data.max()
    for i in range(3):
        plt.subplot(1,4,i+2)
        plt.title("filter #{} output".format(i))
        plt.imshow(net.blobs['conv'].data[0, i], vmin=filt_min, vmax=filt_max)
        plt.tight_layout()
        plt.axis('off')

# filter the image with initial 
show_filters(net)


    

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