model.load_state_dict(
torch.load("../model/model_final.pth")["model_state_dict"])
layers = [[model.conv1_1, model.conv1_2], [model.conv2_1, model.conv2_2],
[model.conv3_1, model.conv3_2]]
shapes = [[4, 8], [8, 8], [8, 16]]
for i, layer in enumerate(layers):
for j, conv in enumerate(layer):
print("conv{}_{}.png".format(i + 1, j + 1))
distributions = conv.distribution.detach().numpy()
print(distributions.shape)
figure, graphs = plt.subplots(*shapes[i])
print(distributions.shape)
print(graphs.shape)
print(shapes[i][0] * shapes[i][1])
for graph, distribution in zip(
graphs.reshape(shapes[i][0] * shapes[i][1]), distributions):
graph.matshow(distribution)
graph.set_xticklabels([])
graph.set_yticklabels([])
graph.axis("off")
plt.subplots_adjust(wspace=0, hspace=0)
figure.savefig("../image/distribution_trained_conv{}_{}.png".format(
i + 1, j + 1))
plot_distribution(distributions[0], name="distribution_trained.png")
plot_distribution_2d(distributions[0], name="distribution_trained_2d.png")
# layers = [
# [model.conv1_1, model.conv1_2],
# [model.conv2_1, model.conv2_2],
# [model.conv3_1, model.conv3_2]
# ]
# shapes = [[4, 8], [8, 8], [8, 16]]
# for i, layer in enumerate(layers):
# for j, conv in enumerate(layer):
# print("conv{}_{}.png".format(i + 1, j + 1))
# distributions = conv.distribution.detach().numpy()
# print(distributions.shape)
# figure, graphs = plt.subplots(*shapes[i])
# print(distributions.shape)
# print(graphs.shape)
# print(shapes[i][0] * shapes[i][1])
# for graph, distribution in zip(graphs.reshape(shapes[i][0] * shapes[i][1]), distributions):
# graph.matshow(distribution)
# graph.set_xticklabels([])
# graph.set_yticklabels([])
# graph.axis("off")
# plt.subplots_adjust(wspace=0, hspace=0)
# figure.savefig("../image/distribution_trained_conv{}_{}.png".format(i + 1, j + 1))
distribution = model.conv1_1.distribution.detach().numpy()[25]
plot_distribution(distribution, name="distribution_trained_conv1_1_25.png")
# plot_distribution_2d(distributions[0], name="distribution_trained_2d.png")
import torch
from distribution_3d import plot_distribution
from network import DistributionConvolutionModelKReluGradientMuRandom
import matplotlib.pyplot as plt
model = DistributionConvolutionModelKReluGradientMuRandom()
model.load_state_dict(torch.load("../model/model_final.pth"), strict=False)
distribution = model.conv3_1.distribution.detach().numpy()[2]
plot_distribution(distribution)
# layers = [
# [model.conv1_1, model.conv1_2],
# [model.conv2_1, model.conv2_2],
# [model.conv3_1, model.conv3_2]
# ]
# shapes = [[4, 8], [8, 8], [8, 16]]
# for i, layer in enumerate(layers):
# for j, conv in enumerate(layer):
# print("conv{}_{}.png".format(i + 1, j + 1))
# distributions = conv.distribution.detach().numpy()
# print(distributions.shape)
# figure, graphs = plt.subplots(*shapes[i])
# print(distributions.shape)
# print(graphs.shape)
# print(shapes[i][0] * shapes[i][1])
# for graph, distribution in zip(graphs.reshape(shapes[i][0] * shapes[i][1]), distributions):
# graph.matshow(distribution)
# graph.set_xticklabels([])
# graph.set_yticklabels([])
# graph.axis("off")
# plt.subplots_adjust(wspace=0, hspace=0)
# figure.savefig("../image/distribution_conv{}_{}.png".format(i + 1, j + 1))
plot_distribution(model.conv1_1.distribution.detach().numpy()[4],
name="distribution_trained_4.png")
# plot_distribution_2d(model.conv1_1.distribution.detach().numpy()[1], name="distribution_trained_2d.png")