alpha_list = [(1 - alpha) / (no_of_task - 1)] * (no_of_task - 1)
alpha_list.append(alpha)
######################### Mean-IMM ##########################
if mean_imm:
print("")
print("Main experiment on Drop-transfer + Mean-IMM, shuffled MNIST")
print("============== Train task #%d (Mean-IMM) ==============" %
no_of_task)
LW = model_utils.UpdateMultiTaskLwWithAlphas(L_copy[0], alpha_list,
no_of_task)
model_utils.AddMultiTaskLayers(sess, L_copy, mlp.Layers, LW,
no_of_task)
ret = mlp.TestTasks(sess, x, y, x_, y_, debug=False)
utils.PrintResults(alpha, ret)
mlp.TestAllTasks(sess, x_, y_)
######################### Mode-IMM ##########################
if mode_imm:
print("")
print("Main experiment on Drop-transfer + Mode-IMM, shuffled MNIST")
print("============== Train task #%d (Mode-IMM) ==============" %
no_of_task)
LW = model_utils.UpdateMultiTaskWeightWithAlphas(
FM, alpha_list, no_of_task)
model_utils.AddMultiTaskLayers(sess, L_copy, mlp.Layers, LW,
no_of_task)
ret = mlp.TestTasks(sess, x, y, x_, y_, debug=False)
no_of_task)
if plotfile is not None:
plotfile.write("\n")
plotfile.write(
"Main experiment on %s + Mean-IMM, alpha=%.03f, shuffled MNIST"
% (optimizer, alpha) + "\n")
plotfile.write(
"============== Train task #%d (Mean-IMM) ==============" %
no_of_task + "\n")
LW = model_utils.UpdateMultiTaskLwWithAlphas(
L_copy[0], alpha_list, no_of_task)
model_utils.AddMultiTaskLayers(sess, L_copy, mlp.Layers, LW,
no_of_task)
ret = mlp.TestTasks(sess, x, y, x_, y_, debug=False)
utils.PrintResults(alpha, ret, logTo=plotfile)
mlp.TestAllTasks(sess, x_, y_, logTo=plotfile)
######################### Mode-IMM ##########################
if mode_imm:
print("")
print(
"Main experiment on %s + Mode-IMM, alpha=%.03f, shuffled MNIST"
% (optimizer, alpha))
print("============== Train task #%d (Mode-IMM) ==============" %
no_of_task)
if plotfile is not None:
plotfile.write("\n")
plotfile.write(
"Main experiment on %s + Mode-IMM, alpha=%.03f, shuffled MNIST"
% (optimizer, alpha) + "\n")
plotfile.write(