Visualizing learned features of a caffe neural network

Learned features of a caffe convolutional neural network

Learned features of a caffe convolutional neural network

After training a convolutional neural network, one often wants to see what the network has learned. The following python function creates and displays an image with all convolutions of a specific layer as shown above. If you supply the function with a filename the image will also be saved on disk.

import numpy as np
import matplotlib.pyplot as plt
 
 
def visualize_weights(net, layer_name, padding=4, filename=''):
    # The parameters are a list of [weights, biases]
    data = np.copy(net.params[layer_name][0].data)
    # N is the total number of convolutions
    N = data.shape[0]*data.shape[1]
    # Ensure the resulting image is square
    filters_per_row = int(np.ceil(np.sqrt(N)))
    # Assume the filters are square
    filter_size = data.shape[2]
    # Size of the result image including padding
    result_size = filters_per_row*(filter_size + padding) - padding
    # Initialize result image to all zeros
    result = np.zeros((result_size, result_size))
 
    # Tile the filters into the result image
    filter_x = 0
    filter_y = 0
    for n in range(data.shape[0]):
        for c in range(data.shape[1]):
            if filter_x == filters_per_row:
                filter_y += 1
                filter_x = 0
            for i in range(filter_size):
                for j in range(filter_size):
                    result[filter_y*(filter_size + padding) + i, filter_x*(filter_size + padding) + j] = data[n, c, i, j]
            filter_x += 1
 
    # Normalize image to 0-1
    min = result.min()
    max = result.max()
    result = (result - min) / (max - min)
 
    # Plot figure
    plt.figure(figsize=(10, 10))
    plt.axis('off')
    plt.imshow(result, cmap='gray', interpolation='nearest')
 
 
    # Save plot if filename is set
    if filename != '':
        plt.savefig(filename, bbox_inches='tight', pad_inches=0)
 
    plt.show()

The following is an example of how to use this function

from visualize_caffe import *
import sys
 
# Make sure caffe can be found
sys.path.append('../caffe/python/')
 
import caffe
 
# Load model
net = caffe.Net('/home/smistad/vessel_net/deploy.prototxt',
                '/home/smistad/vessel_net/snapshot_iter_3800.caffemodel',
                caffe.TEST)
 
visualize_weights(net, 'conv1', filename='conv1.png')
visualize_weights(net, 'conv2', filename='conv2.png')

As usual the code can be found on my GitHub page

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6 Responses

  1. Anonymous says:

    when trying to visualize last 3 layers of, it gives following error.

    line 13, in visualize_weights
    filter_size = data.shape[2]
    IndexError: tuple index out of range

    How can I visualize last layers?

    • Erik Smistad says:

      Your last three layers are probably fully connected/dense layers and not convolutional layers. These layers have less dimensions, thus for these types of layers it doesn’t make sense to use the code above.

  2. John says:

    It is very useful for me. Thanks for this code.
    I am using 3D-Unet (3D convolution). I want to visualize the Learned features of this network. The blob of 3D Unet is 5D blobs arranged as (#of samples, #of channels, depth, height, width). I guess we need to change something from your code, could you give me some advice?

    • Erik Smistad says:

      Visualising 3D convolutions is not so straightforward as with 2D, you might need to do some 3D volume rendering, or show the 3D convolution weights slice by slice. Or you can apply the convolution one by one on a volume and look at the effects.

  3. Anonymous says:

    how to visualize only the first 30 filters instead of all the filters in a conv1 layer

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