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- 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()
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