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							|  | @ -65,7 +65,7 @@ def main(opt): | |||
|         #         p.requires_grad = False | ||||
| 
 | ||||
|         # Set optimizer | ||||
|         # optimizer = torch.optim.SGD(model.parameters(), lr=.001, momentum=.9, weight_decay=0.0005 * 0, nesterov=True) | ||||
|         # optimizer = torch.optim.SGD(model.parameters(), lr=.001, momentum=.9, weight_decay=5e-4, nesterov=True) | ||||
|         # optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters())) | ||||
|         optimizer = torch.optim.Adam(model.parameters()) | ||||
|         optimizer.load_state_dict(checkpoint['optimizer']) | ||||
|  |  | |||
|  | @ -368,10 +368,10 @@ def plotResults(): | |||
|     plt.figure(figsize=(16, 8)) | ||||
|     s = ['X', 'Y', 'Width', 'Height', 'Objectness', 'Classification', 'Total Loss', 'Precision', 'Recall'] | ||||
|     for f in ('/Users/glennjocher/Downloads/results.txt', | ||||
|               '/Users/glennjocher/Downloads/resultsBCE2.txt'): | ||||
|               '/Users/glennjocher/Downloads/resultsBCE.txt'): | ||||
|         results = np.loadtxt(f, usecols=[2, 3, 4, 5, 6, 7, 8, 9, 10]).T | ||||
|         for i in range(9): | ||||
|             plt.subplot(2, 5, i + 1) | ||||
|             plt.plot(results[i, :], marker='.', label=f) | ||||
|             plt.title(s[i]) | ||||
|         plt.legend(cocococosadfc) | ||||
|         plt.legend() | ||||
|  |  | |||
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