input=layers[-1].output,
input_shape=layers[-1].output_shape,
output_shape=11,
dropout_input=layers[-1].dropout_output,
active_func=actfuncs.sigmoid
))
model = NeuralNet(layers, [X, S], layers[-1].output)
model.target = A
model.cost = costfuncs.binxent(layers[-1].dropout_output, A) + \
1e-3 * model.get_norm(2)
model.error = costfuncs.binerr(layers[-1].output, A)
sgd.train(model, dataset, lr=1e-2, momentum=0.9,
batch_size=100, n_epochs=300,
epoch_waiting=10)
return model
if __name__ == '__main__':
dataset_file = 'data_{0}.pkl'.format(args.dataset[0])
out_file = 'model_attr.pkl' if args.output is None else \
'model_attr_{0}.pkl'.format(args.output)
dataset = load_data(dataset_file)
model = train_model(dataset, not args.no_scpool)
save_data(model, out_file)
))
model = NeuralNet(layers, X, layers[-1].output)
model.target = S
'''
model.cost = costfuncs.binxent(layers[-1].dropout_output, S.flatten(2)) + \
1e-3 * model.get_norm(2)
model.error = costfuncs.binerr(layers[-1].output, S.flatten(2))
'''
model.cost = costfuncs.weighted_norm2(
layers[-1].dropout_output, S.flatten(2), 1.0) + \
1e-3 * model.get_norm(2)
model.error = costfuncs.weighted_norm2(
layers[-1].output, S.flatten(2), 1.0)
sgd.train(model, dataset, lr=1e-2, momentum=0.9,
batch_size=100, n_epochs=300,
epoch_waiting=10, never_stop=True)
return model
if __name__ == '__main__':
dataset = load_dataset()
model = train_model(dataset)
save_data(model, 'model_seg_handcrafted.pkl')
t = target[:, j].ravel()
fpr, tpr, thresh = stats.roc(o, t)
auc = stats.auc(fpr, tpr)
ret[j] = (auc, fpr, tpr, thresh)
return ret
def show_stats(ret):
import matplotlib.pyplot as plt
n_cols = 4
n_rows = len(ret) // n_cols + 1
for j, (auc, fpr, tpr, thresh) in enumerate(ret):
# Plot stats
plt.subplot(n_rows, n_cols, j + 1)
plt.plot(fpr, tpr)
plt.title('AUC = {:.2f}%'.format(auc * 100))
plt.show()
matdata = loadmat('svm_result_mix.mat')
target = matdata['targets']
output = matdata['outputs']
ret = compute_stats(output, target)
save_data(ret, 'stats_attr_svm_mix.pkl')
show_stats(ret)
n_cols = 4
n_rows = len(ret) // n_cols + 1
for j, (auc, fpr, tpr, thresh) in enumerate(ret):
# Plot stats
plt.subplot(n_rows, n_cols, j)
plt.plot(fpr, tpr)
plt.title('AUC = {:.2f}%'.format(auc * 100))
plt.show()
if __name__ == '__main__':
dataset_file = 'data_{0}.pkl'.format(args.dataset[0])
model_file = 'model_{0}.pkl'.format(args.model[0])
out_file = 'stats.pkl' if args.output is None else \
'stats_{0}.pkl'.format(args.output)
if not args.display_only:
dataset = load_data(dataset_file)
model = load_data(model_file)
output = compute_output(model, dataset.test)
ret = compute_stats(output, dataset.test.target.cpu_data)
save_data(ret, out_file)
ret = load_data(out_file)
show_stats(ret)
