コード例 #1
0
def return_grad(test_params, input_x, truth_y):
    tmp = get_params(model_ft.models_stack[-1])
    set_params(model_ft.models_stack[-1], test_params)
    result = numpy.concatenate(
        [numpy.array(i).flatten() for i in fun_grad(input_x, truth_y)])
    set_params(model_ft.models_stack[-1], tmp)
    return result
コード例 #2
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def return_grad(test_params, input_x, truth_y):
    tmp = get_params(model_ft.models_stack[-1])
    set_params(model_ft.models_stack[-1], test_params)
    result = numpy.concatenate([numpy.array(i).flatten() for i in fun_grad(input_x, truth_y)])
    set_params(model_ft.models_stack[-1], tmp)
    return result
コード例 #3
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def return_cost(test_params, input_x, truth_y):
    tmp = get_params(model_ft.models_stack[-1])
    set_params(model_ft.models_stack[-1], test_params)
    result = fun_cost(input_x, truth_y)
    set_params(model_ft.models_stack[-1], tmp)
    return result
コード例 #4
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fun_grad = theano.function(
    [model_ft.varin, model_ft.models_stack[-1].vartruth],
    T.grad(model_ft.models_stack[-1].cost() + model_ft.models_stack[-1].weightdecay(weightdecay),
           model_ft.models_stack[-1].params)
)
def return_grad(test_params, input_x, truth_y):
    tmp = get_params(model_ft.models_stack[-1])
    set_params(model_ft.models_stack[-1], test_params)
    result = numpy.concatenate([numpy.array(i).flatten() for i in fun_grad(input_x, truth_y)])
    set_params(model_ft.models_stack[-1], tmp)
    return result
p, g, numlinesearches = minimize(
    get_params(model_ft.models_stack[-1]), return_cost, return_grad,
    (train_x.get_value(), train_y.get_value()), logreg_epc, verbose=False
)
set_params(model_ft.models_stack[-1], p)
save_params(model_ft, 'ZLIN_4000_1000_4000_1000_4000_1000_4000_10_normhid_nolinb_cae1_dropout.npy')
print "***error rate: train: %f, test: %f" % (
    train_set_error_rate(), test_set_error_rate()
)

#############
# FINE-TUNE #
#############

"""
print "\n\n... fine-tuning the whole network"
truth = T.lmatrix('truth')
trainer = GraddescentMinibatch(
    varin=model_ft.varin, data=train_x, 
    truth=model_ft.models_stack[-1].vartruth, truth_data=train_y,
コード例 #5
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def return_cost(test_params, input_x, truth_y):
    tmp = get_params(model_ft.models_stack[-1])
    set_params(model_ft.models_stack[-1], test_params)
    result = fun_cost(input_x, truth_y)
    set_params(model_ft.models_stack[-1], tmp)
    return result
コード例 #6
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def return_grad(test_params, input_x, truth_y):
    tmp = get_params(model_ft.models_stack[-1])
    set_params(model_ft.models_stack[-1], test_params)
    result = numpy.concatenate(
        [numpy.array(i).flatten() for i in fun_grad(input_x, truth_y)])
    set_params(model_ft.models_stack[-1], tmp)
    return result


p, g, numlinesearches = minimize(get_params(model_ft.models_stack[-1]),
                                 return_cost,
                                 return_grad,
                                 (train_x.get_value(), train_y.get_value()),
                                 logreg_epc,
                                 verbose=False)
set_params(model_ft.models_stack[-1], p)
save_params(
    model_ft,
    'ZLIN_4000_1000_4000_1000_4000_1000_4000_10_normhid_nolinb_cae1_dropout.npy'
)
print "***error rate: train: %f, test: %f" % (train_set_error_rate(),
                                              test_set_error_rate())

#############
# FINE-TUNE #
#############
"""
print "\n\n... fine-tuning the whole network"
truth = T.lmatrix('truth')
trainer = GraddescentMinibatch(
    varin=model_ft.varin, data=train_x, 
コード例 #7
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from copy import deepcopy
from master_file import regret_dgrind, regret_exp3
from params import set_params
import math

num_repetitions = 30
dgrind = []
exp3 = []
min_num_rounds = 0
max_num_rounds = 1000
step = 5
rounds = [T for T in range(min_num_rounds, max_num_rounds)]  #size: 5000 bytes

T = max_num_rounds
(num_agents, dim, x_real, calA, agent_types, true_labels, delta, noise,
 p) = set_params(T)

cp_xreal = deepcopy(x_real)
for delta in [0.05, 0.10, 0.15, 0.3, 0.5]:
    print("Current delta = %.5f" % delta)
    agents_dgrind = [Agent(t, agent_types, cp_xreal, delta) for t in range(T)]
    oracle_dgrind = Oracle(deepcopy(agents_dgrind), calA, T)
    resp_lst_dgrind = oracle_dgrind.compute_responses(dim)
    print("Independence Number")
    # computes approximate independence number in order to tune \eta for GRINDER correctly
    a_G = oracle_dgrind.compute_independence_number(dim, resp_lst_dgrind)
    print a_G

    principal_dgrind = [
        Principal(T, calA, num_repetitions, p, a_G)
        for _ in range(0, num_repetitions)
コード例 #8
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from copy import deepcopy
from master_file import regret_grind, regret_exp3
from params import set_params
import math

num_repetitions = 30
dgrind = []
exp3 = []
min_num_rounds = 0
max_num_rounds = 1000
step = 5
rounds = [T for T in range(min_num_rounds, max_num_rounds)]

T = max_num_rounds
(num_agents, dim, x_real, calA_exp3, calA_grind, agent_types, true_labels,
 delta, noise, prob) = set_params(T, 0.2)

cp_xreal = deepcopy(x_real)
for delta in [0.05, 0.1, 0.15, 0.3, 0.5]:
    print("Current delta = %.5f" % delta)
    agents_grind = [Agent(t, agent_types, cp_xreal, delta) for t in range(T)]
    oracle_grind = Oracle(deepcopy(agents_grind), T)

    principal_grind = [
        Principal_Grind(T, calA_grind, num_repetitions)
        for _ in range(0, num_repetitions)
    ]
    principal_exp3 = [
        Principal_Exp3(T, calA_exp3, num_repetitions, 0)
        for _ in range(0, num_repetitions)
    ]