def test_kwta_log_gain_offset(self):
K = KWTAMechanism(name='K',
size=2,
function=Logistic(gain=-.2, offset=4),
k_value=1)
val = K.execute(input=[.1, -4])
assert np.allclose(val, [[0.017636340339722684, 0.039165722796764356]])
def test_recurrent_mech_integrator(self, benchmark, mode):
R = RecurrentTransferMechanism(size=2,
function=Logistic(),
hetero=-2.0,
integrator_mode=True,
integration_rate=0.01,
output_states = [RECURRENT_OUTPUT.RESULT])
if mode == 'Python':
EX = R.execute
elif mode == 'LLVM':
e = pnlvm.execution.MechExecution(R)
EX = e.execute
elif mode == 'PTX':
e = pnlvm.execution.MechExecution(R)
EX = e.cuda_execute
val1 = EX([[1.0, 2.0]])
val2 = EX([[1.0, 2.0]])
# execute 10 times
for i in range(10):
val = EX([[1.0, 2.0]])
benchmark(EX, [[1.0, 2.0]])
assert np.allclose(val1, [[0.50249998, 0.50499983]])
assert np.allclose(val2, [[0.50497484, 0.50994869]])
assert np.allclose(val, [[0.52837327, 0.55656439]])
def test_create_scheduler_from_system_StroopDemo(self):
Color_Input = TransferMechanism(name='Color Input',
function=Linear(slope=0.2995))
Word_Input = TransferMechanism(name='Word Input',
function=Linear(slope=0.2995))
# Processing Mechanisms (Control)
Color_Hidden = TransferMechanism(
name='Colors Hidden',
function=Logistic(gain=(1.0, ControlProjection)),
)
Word_Hidden = TransferMechanism(
name='Words Hidden',
function=Logistic(gain=(1.0, ControlProjection)),
)
Output = TransferMechanism(
name='Output',
function=Logistic(gain=(1.0, ControlProjection)),
)
# Decision Mechanisms
Decision = DDM(
function=DriftDiffusionAnalytical(
drift_rate=(1.0),
threshold=(0.1654),
noise=(0.5),
starting_point=(0),
t0=0.25,
),
name='Decision',
)
# Outcome Mechanism:
Reward = TransferMechanism(name='Reward')
myComposition = Composition(
pathways=[[Color_Input, Color_Hidden, Output, Decision],
[Word_Input, Word_Hidden, Output, Decision], [Reward]])
sched = Scheduler(composition=myComposition)
expected_consideration_queue = [{Color_Input, Word_Input, Reward},
{Color_Hidden, Word_Hidden}, {Output},
{Decision}]
assert sched.consideration_queue == expected_consideration_queue
def test_kwta_log_gain(self):
K = KWTAMechanism(
name='K',
size=3,
function=Logistic(gain=2),
k_value=2
)
val = K.execute(input = [1, 2, 3])
assert np.allclose(val, [[0.2689414213699951, 0.7310585786300049, 0.9525741268224334]])
def test_recurrent_mech_function_logistic(self):
R = RecurrentTransferMechanism(
name='R',
size=10,
function=Logistic(gain=2, offset=1)
)
val = R.execute(np.ones(10))
np.testing.assert_allclose(val, [np.full(10, 0.7310585786300049)])
def test_kwta_function_various_spec(self):
specs = [
Logistic, Linear,
Linear(slope=3),
Logistic(gain=2, offset=-4.2)
]
for s in specs:
K = KWTAMechanism(name='K', size=5, function=s, k_value=4)
K.execute([1, 2, 5, -2, .3])
def test_kwta_log_offset(self):
K = KWTAMechanism(
name='K',
size=3,
function=Logistic(offset=-.2),
k_value=2
)
val = K.execute(input=[1, 2, 3])
assert np.allclose(val, [[0.425557483188341, 0.6681877721681662, 0.84553473491646523]])
def test_recurrent_mech_function_psyneulink(self):
a = Logistic(gain=2, offset=1)
R = RecurrentTransferMechanism(
name='R',
size=7,
function=a
)
val = R.execute(np.zeros(7))
np.testing.assert_allclose(val, [np.full(7, 0.2689414213699951)])
def test_udf_with_pnl_func(self):
L = Logistic(gain=2)
def myFunction(variable, params, context):
return L(variable) + 2
U = UserDefinedFunction(custom_function=myFunction, default_variable=[[0, 0, 0]])
myMech = ProcessingMechanism(function=myFunction, size=3, name='myMech')
val1 = myMech.execute(input=[1, 2, 3])
val2 = U.execute(variable=[[1, 2, 3]])
assert np.allclose(val1, val2)
assert np.allclose(val1, L([1, 2, 3]) + 2)
def test_multilayer_log():
Input_Layer = TransferMechanism(
name='Input Layer',
function=Logistic,
default_variable=np.zeros((2, )),
)
Hidden_Layer_1 = TransferMechanism(
name='Hidden Layer_1',
function=Logistic(),
# default_variable=np.zeros((5,)),
size=5)
Hidden_Layer_2 = TransferMechanism(
name='Hidden Layer_2',
function=Logistic(),
default_variable=[0, 0, 0, 0],
)
Output_Layer = TransferMechanism(
name='Output Layer',
function=Logistic,
default_variable=[0, 0, 0],
)
Input_Weights_matrix = (np.arange(2 * 5).reshape((2, 5)) + 1) / (2 * 5)
Middle_Weights_matrix = (np.arange(5 * 4).reshape((5, 4)) + 1) / (5 * 4)
Output_Weights_matrix = (np.arange(4 * 3).reshape((4, 3)) + 1) / (4 * 3)
# TEST PROCESS.LEARNING WITH:
# CREATION OF FREE STANDING PROJECTIONS THAT HAVE NO LEARNING (Input_Weights, Middle_Weights and Output_Weights)
# INLINE CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
# NO EXPLICIT CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
# This projection will be used by the process below by referencing it in the process' pathway;
# note: sender and receiver args don't need to be specified
Input_Weights = MappingProjection(
name='Input Weights',
matrix=Input_Weights_matrix,
)
# This projection will be used by the process below by assigning its sender and receiver args
# to mechanismss in the pathway
Middle_Weights = MappingProjection(
name='Middle Weights',
sender=Hidden_Layer_1,
receiver=Hidden_Layer_2,
matrix=Middle_Weights_matrix,
)
# Commented lines in this projection illustrate variety of ways in which matrix and learning signals can be specified
Output_Weights = MappingProjection(
name='Output Weights',
sender=Hidden_Layer_2,
receiver=Output_Layer,
matrix=Output_Weights_matrix,
)
p = Process(
# default_variable=[0, 0],
size=2,
pathway=[
Input_Layer,
# The following reference to Input_Weights is needed to use it in the pathway
# since it's sender and receiver args are not specified in its declaration above
Input_Weights,
Hidden_Layer_1,
# No projection specification is needed here since the sender arg for Middle_Weights
# is Hidden_Layer_1 and its receiver arg is Hidden_Layer_2
# Middle_Weights,
Hidden_Layer_2,
# Output_Weights does not need to be listed for the same reason as Middle_Weights
# If Middle_Weights and/or Output_Weights is not declared above, then the process
# will assign a default for missing projection
# Output_Weights,
Output_Layer
],
clamp_input=SOFT_CLAMP,
learning=LEARNING,
learning_rate=1.0,
target=[0, 0, 1],
prefs={
VERBOSE_PREF: False,
REPORT_OUTPUT_PREF: False
},
)
Middle_Weights.set_log_conditions(('mod_matrix', PROCESSING))
stim_list = {Input_Layer: [[-1, 30]]}
target_list = {Output_Layer: [[0, 0, 1]]}
def show_target():
i = s.input
t = s.target_input_states[0].parameters.value.get(s)
print('\nOLD WEIGHTS: \n')
print('- Input Weights: \n', Input_Weights.get_mod_matrix(s))
print('- Middle Weights: \n', Middle_Weights.get_mod_matrix(s))
print('- Output Weights: \n', Output_Weights.get_mod_matrix(s))
print('\nSTIMULI:\n\n- Input: {}\n- Target: {}\n'.format(i, t))
print('ACTIVITY FROM OLD WEIGHTS: \n')
print('- Middle 1: \n', Hidden_Layer_1.parameters.value.get(s))
print('- Middle 2: \n', Hidden_Layer_2.parameters.value.get(s))
print('- Output:\n', Output_Layer.parameters.value.get(s))
s = System(
processes=[p],
targets=[0, 0, 1],
learning_rate=1.0,
)
s.run(
num_trials=10,
inputs=stim_list,
targets=target_list,
call_after_trial=show_target,
)
expected_log_val = np.array(
[['System-0'],
[[[[0], [0], [0], [0], [0], [0], [0], [0], [0], [0]],
[[0], [1], [2], [3], [4], [5], [6], [7], [8], [9]],
[[0], [0], [0], [0], [0], [0], [0], [0], [0], [0]],
[[2], [2], [2], [2], [2], [2], [2], [2], [2], [2]],
[[[0.05, 0.1, 0.15, 0.2], [0.25, 0.3, 0.35, 0.4],
[0.45, 0.5, 0.55, 0.6], [0.65, 0.7, 0.75, 0.8],
[0.85, 0.9, 0.95, 1.]],
[[0.04789907, 0.09413833, 0.14134241, 0.18938924],
[0.24780811, 0.29388455, 0.34096758, 0.38892985],
[0.44772121, 0.49364209, 0.54060947, 0.58849095],
[0.64763875, 0.69341202, 0.74026967, 0.78807449],
[0.84756101, 0.89319513, 0.93994932, 0.98768187]],
[[0.04738148, 0.08891106, 0.13248753, 0.177898],
[0.24726841, 0.28843403, 0.33173452, 0.37694783],
[0.44716034, 0.48797777, 0.53101423, 0.57603893],
[0.64705774, 0.6875443, 0.73032986, 0.77517531],
[0.84696096, 0.88713512, 0.92968378, 0.97435998]],
[[0.04937771, 0.08530344, 0.12439361, 0.16640433],
[0.24934878, 0.28467436, 0.32329947, 0.36496974],
[0.44932147, 0.48407216, 0.52225175, 0.56359587],
[0.64929589, 0.68349948, 0.72125508, 0.76228876],
[0.84927212, 0.88295836, 0.92031297, 0.96105307]],
[[0.05440291, 0.08430585, 0.1183739, 0.15641064],
[0.25458348, 0.28363519, 0.3170288, 0.35455942],
[0.45475764, 0.48299299, 0.51573974, 0.55278488],
[0.65492462, 0.68238209, 0.7145124, 0.75109483],
[0.85508376, 0.88180465, 0.91335119, 0.94949538]],
[[0.06177218, 0.0860581, 0.11525064, 0.14926369],
[0.26225812, 0.28546004, 0.31377611, 0.34711631],
[0.46272625, 0.48488774, 0.51236246, 0.54505667],
[0.66317453, 0.68434373, 0.7110159, 0.74309381],
[0.86360121, 0.88382991, 0.9097413, 0.94123489]],
[[0.06989398, 0.08959148, 0.11465594, 0.14513241],
[0.27071639, 0.2891398, 0.31315677, 0.34281389],
[0.47150846, 0.48870843, 0.5117194, 0.54058946],
[0.67226675, 0.68829929, 0.71035014, 0.73846891],
[0.87298831, 0.88791376, 0.90905395, 0.93646]],
[[0.07750784, 0.09371987, 0.11555569, 0.143181],
[0.27864693, 0.29343991, 0.31409396, 0.3407813],
[0.47974374, 0.49317377, 0.5126926, 0.53847878],
[0.68079346, 0.69292265, 0.71135777, 0.73628353],
[0.88179203, 0.89268732, 0.91009431, 0.93420362]],
[[0.0841765, 0.09776672, 0.11711835, 0.14249779],
[0.28559463, 0.29765609, 0.31572199, 0.34006951],
[0.48695967, 0.49755273, 0.51438349, 0.5377395],
[0.68826567, 0.69745713, 0.71310872, 0.735518],
[0.88950757, 0.89736946, 0.91190228, 0.93341316]],
[[0.08992499, 0.10150104, 0.11891032, 0.14250149],
[0.29158517, 0.30154765, 0.31758943, 0.34007336],
[0.49318268, 0.50159531, 0.51632339, 0.5377435],
[0.69471052, 0.70164382, 0.71511777, 0.73552215],
[0.8961628, 0.90169281, 0.91397691, 0.93341744]]]]]],
dtype=object)
log_val = Middle_Weights.log.nparray(entries='mod_matrix', header=False)
assert log_val[0] == expected_log_val[0]
for i in range(1, len(log_val)):
try:
np.testing.assert_allclose(log_val[i], expected_log_val[i])
except TypeError:
for j in range(len(log_val[i])):
np.testing.assert_allclose(
np.array(log_val[i][j][0]),
np.array(expected_log_val[i][j][0]),
atol=1e-08,
err_msg='Failed on test item {0} of logged values'.format(
i))
Middle_Weights.log.print_entries()
# Test Programatic logging
Hidden_Layer_2.log.log_values(VALUE, s)
log_val = Hidden_Layer_2.log.nparray(header=False)
expected_log_val = np.array([['System-0'],
[[[[1]], [[0]], [[0]], [[0]],
[[[
0.8565238418942037, 0.8601053239957609,
0.8662098921116546, 0.8746933736954071
]]]]]],
dtype=object)
assert log_val[0] == expected_log_val[0]
for i in range(1, len(log_val)):
try:
np.testing.assert_allclose(log_val[i], expected_log_val[i])
except TypeError:
for j in range(len(log_val[i])):
np.testing.assert_allclose(
np.array(log_val[i][j][0]),
np.array(expected_log_val[i][j][0]),
atol=1e-08,
err_msg='Failed on test item {0} of logged values'.format(
i))
Hidden_Layer_2.log.print_entries()
# Clear log and test with logging of weights set to LEARNING for another 5 trials of learning
Middle_Weights.log.clear_entries(entries=None, confirm=False)
Middle_Weights.set_log_conditions(('mod_matrix', LEARNING))
s.run(
num_trials=5,
inputs=stim_list,
targets=target_list,
)
log_val = Middle_Weights.log.nparray(entries='mod_matrix', header=False)
expected_log_val = np.array(
[
['System-0'],
[[
[[1], [1], [1], [1], [1]], # RUN
[[0], [1], [2], [3], [4]], # TRIAL
[[1], [1], [1], [1], [1]], # PASS
[[1], [1], [1], [1], [1]], # TIME_STEP
[[[
0.09925812411381937, 0.1079522130303428,
0.12252820028789306, 0.14345816973727732
],
[
0.30131473371328343, 0.30827285172236585,
0.3213609999139731, 0.3410707131678078
],
[
0.5032924245149345, 0.5085833053183328,
0.5202423523987703, 0.5387798509126243
],
[
0.70518251216691, 0.7088822116145151, 0.7191771716324874,
0.7365956448426355
],
[
0.9069777724600303, 0.9091682860319945,
0.9181692763668221, 0.93452610920817
]],
[[
0.103113468050986, 0.11073719161508278,
0.12424368674464399, 0.14415219181047598
],
[
0.3053351724284921, 0.3111770895557729,
0.3231499474835138, 0.341794454877438
],
[
0.5074709829757806, 0.5116017638574931,
0.5221016574478528, 0.5395320566440044
],
[
0.7095115080472698, 0.7120093413898914,
0.7211034158081356, 0.7373749316571768
],
[
0.9114489813353512, 0.9123981459792809,
0.9201588001021687, 0.935330996581107
]],
[[
0.10656261740658036, 0.11328192907953168,
0.12587702586370172, 0.14490737831188183
],
[
0.30893272045369513, 0.31383131362555394,
0.32485356055342113, 0.3425821330631872
],
[
0.5112105492674988, 0.5143607671543178,
0.5238725230390068, 0.5403508295336265
],
[
0.7133860755337162, 0.7148679468096026,
0.7229382109974996, 0.7382232628724675
],
[
0.9154510531345043, 0.9153508224199809,
0.9220539747533424, 0.936207244690072
]],
[[
0.10967776822419642, 0.11562091141141007,
0.12742795007904037, 0.14569308665620523
],
[
0.3121824816018084, 0.316271366885665,
0.3264715025259811, 0.34340179304134666
],
[
0.5145890402653069, 0.5168974760377518,
0.5255545550838675, 0.5412029579613059
],
[
0.7168868378231593, 0.7174964619674593,
0.7246811176253708, 0.7391062307617761
],
[
0.9190671994078436, 0.9180659725806082,
0.923854327015523, 0.9371193149131859
]],
[[
0.11251466428344682, 0.11778293740676549,
0.12890014813698167, 0.14649079441816393
],
[
0.31514245505635713, 0.3185271913574249,
0.328007571201157, 0.3442341089776976
],
[
0.5176666356203712, 0.5192429413004418,
0.5271516632648602, 0.5420683480396268
],
[
0.7200760707077265, 0.7199270072739019,
0.7263361597421493, 0.7400030122347587
],
[
0.922361699102421, 0.9205767427437028,
0.9255639970037588, 0.9380456963960624
]]]
]]
],
dtype=object)
assert log_val.shape == expected_log_val.shape
assert log_val[0] == expected_log_val[0]
assert len(log_val[1]) == len(expected_log_val[1]) == 1
for i in range(len(log_val[1][0])):
try:
np.testing.assert_allclose(log_val[1][0][i],
expected_log_val[1][0][i])
except TypeError:
for j in range(len(log_val[1][0][i])):
np.testing.assert_allclose(
np.array(log_val[1][0][i][j]),
np.array(expected_log_val[1][0][i][j]),
atol=1e-08,
err_msg='Failed on test item {0} of logged values'.format(
i))
def test_multilayer():
Input_Layer = TransferMechanism(
name='Input Layer',
function=Logistic,
default_variable=np.zeros((2, )),
)
Hidden_Layer_1 = TransferMechanism(
name='Hidden Layer_1',
function=Logistic(),
# default_variable=np.zeros((5,)),
size=5)
Hidden_Layer_2 = TransferMechanism(
name='Hidden Layer_2',
function=Logistic(),
default_variable=[0, 0, 0, 0],
)
Output_Layer = TransferMechanism(
name='Output Layer',
function=Logistic,
default_variable=[0, 0, 0],
)
Input_Weights_matrix = (np.arange(2 * 5).reshape((2, 5)) + 1) / (2 * 5)
Middle_Weights_matrix = (np.arange(5 * 4).reshape((5, 4)) + 1) / (5 * 4)
Output_Weights_matrix = (np.arange(4 * 3).reshape((4, 3)) + 1) / (4 * 3)
# TEST PROCESS.LEARNING WITH:
# CREATION OF FREE STANDING PROJECTIONS THAT HAVE NO LEARNING (Input_Weights, Middle_Weights and Output_Weights)
# INLINE CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
# NO EXPLICIT CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
# This projection will be used by the process below by referencing it in the process' pathway;
# note: sender and receiver args don't need to be specified
Input_Weights = MappingProjection(
name='Input Weights',
matrix=Input_Weights_matrix,
)
# This projection will be used by the process below by assigning its sender and receiver args
# to mechanismss in the pathway
Middle_Weights = MappingProjection(
name='Middle Weights',
sender=Hidden_Layer_1,
receiver=Hidden_Layer_2,
matrix=Middle_Weights_matrix,
)
# Commented lines in this projection illustrate variety of ways in which matrix and learning signals can be specified
Output_Weights = MappingProjection(
name='Output Weights',
sender=Hidden_Layer_2,
receiver=Output_Layer,
matrix=Output_Weights_matrix,
)
p = Process(
# default_variable=[0, 0],
size=2,
pathway=[
Input_Layer,
# The following reference to Input_Weights is needed to use it in the pathway
# since it's sender and receiver args are not specified in its declaration above
Input_Weights,
Hidden_Layer_1,
# No projection specification is needed here since the sender arg for Middle_Weights
# is Hidden_Layer_1 and its receiver arg is Hidden_Layer_2
# Middle_Weights,
Hidden_Layer_2,
# Output_Weights does not need to be listed for the same reason as Middle_Weights
# If Middle_Weights and/or Output_Weights is not declared above, then the process
# will assign a default for missing projection
# Output_Weights,
Output_Layer
],
clamp_input=SOFT_CLAMP,
learning=LEARNING,
learning_rate=1.0,
target=[0, 0, 1],
prefs={
VERBOSE_PREF: False,
REPORT_OUTPUT_PREF: False
},
)
stim_list = {Input_Layer: [[-1, 30]]}
target_list = {Output_Layer: [[0, 0, 1]]}
def show_target():
i = s.input
t = s.target_input_states[0].parameters.value.get(s)
print('\nOLD WEIGHTS: \n')
print('- Input Weights: \n', Input_Weights.get_mod_matrix(s))
print('- Middle Weights: \n', Middle_Weights.get_mod_matrix(s))
print('- Output Weights: \n', Output_Weights.get_mod_matrix(s))
print('\nSTIMULI:\n\n- Input: {}\n- Target: {}\n'.format(i, t))
print('ACTIVITY FROM OLD WEIGHTS: \n')
print('- Middle 1: \n', Hidden_Layer_1.parameters.value.get(s))
print('- Middle 2: \n', Hidden_Layer_2.parameters.value.get(s))
print('- Output:\n', Output_Layer.parameters.value.get(s))
s = System(
processes=[p],
targets=[0, 0, 1],
learning_rate=1.0,
)
# s.reportOutputPref = True
results = s.run(
num_trials=10,
inputs=stim_list,
targets=target_list,
call_after_trial=show_target,
)
objective_output_layer = s.mechanisms[4]
results_list = []
for elem in s.results:
for nested_elem in elem:
nested_elem = nested_elem.tolist()
try:
iter(nested_elem)
except TypeError:
nested_elem = [nested_elem]
results_list.extend(nested_elem)
expected_output = [
(Output_Layer.get_output_values(s),
[np.array([0.22686074, 0.25270212, 0.91542149])]),
(objective_output_layer.output_states[MSE].parameters.value.get(s),
np.array(0.04082589331852094)),
(Input_Weights.get_mod_matrix(s),
np.array([
[0.09900247, 0.19839653, 0.29785764, 0.39739191, 0.49700232],
[0.59629092, 0.69403786, 0.79203411, 0.89030237, 0.98885379],
])),
(Middle_Weights.get_mod_matrix(s),
np.array([
[0.09490249, 0.10488719, 0.12074013, 0.1428774],
[0.29677354, 0.30507726, 0.31949676, 0.3404652],
[0.49857336, 0.50526254, 0.51830509, 0.53815062],
[0.70029406, 0.70544225, 0.71717037, 0.73594383],
[0.90192903, 0.90561554, 0.91609668, 0.93385292],
])),
(Output_Weights.get_mod_matrix(s),
np.array([
[-0.74447522, -0.71016859, 0.31575293],
[-0.50885177, -0.47444784, 0.56676582],
[-0.27333719, -0.23912033, 0.8178167],
[-0.03767547, -0.00389039, 1.06888608],
])),
(results, [[np.array([0.8344837, 0.87072018, 0.89997433])],
[np.array([0.77970193, 0.83263138, 0.90159627])],
[np.array([0.70218502, 0.7773823, 0.90307765])],
[np.array([0.60279149, 0.69958079, 0.90453143])],
[np.array([0.4967927, 0.60030321, 0.90610082])],
[np.array([0.4056202, 0.49472391, 0.90786617])],
[np.array([0.33763025, 0.40397637, 0.90977675])],
[np.array([0.28892812, 0.33633532, 0.9117193])],
[np.array([0.25348771, 0.28791896, 0.9136125])],
[np.array([0.22686074, 0.25270212, 0.91542149])]]),
]
# Test nparray output of log for Middle_Weights
for i in range(len(expected_output)):
val, expected = expected_output[i]
# setting absolute tolerance to be in accordance with reference_output precision
# if you do not specify, assert_allcose will use a relative tolerance of 1e-07,
# which WILL FAIL unless you gather higher precision values to use as reference
np.testing.assert_allclose(
val,
expected,
atol=1e-08,
err_msg='Failed on expected_output[{0}]'.format(i))
def test_gating():
Input_Layer = TransferMechanism(name='Input Layer',
function=Logistic,
default_variable=np.zeros((2, )))
Hidden_Layer_1 = TransferMechanism(name='Hidden Layer_1',
function=Logistic(),
default_variable=np.zeros((5, )))
Hidden_Layer_2 = TransferMechanism(name='Hidden Layer_2',
function=Logistic(),
default_variable=[0, 0, 0, 0])
Output_Layer = TransferMechanism(name='Output Layer',
function=Logistic,
default_variable=[0, 0, 0])
Gating_Mechanism = GatingMechanism(
# default_gating_allocation=0.0,
size=[1],
gating_signals=[
Hidden_Layer_1,
Hidden_Layer_2,
Output_Layer,
])
Input_Weights_matrix = (np.arange(2 * 5).reshape((2, 5)) + 1) / (2 * 5)
Middle_Weights_matrix = (np.arange(5 * 4).reshape((5, 4)) + 1) / (5 * 4)
Output_Weights_matrix = (np.arange(4 * 3).reshape((4, 3)) + 1) / (4 * 3)
# TEST PROCESS.LEARNING WITH:
# CREATION OF FREE STANDING PROJECTIONS THAT HAVE NO LEARNING (Input_Weights, Middle_Weights and Output_Weights)
# INLINE CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
# NO EXPLICIT CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and
# Output_Weights)
# This projection will be used by the process below by referencing it in the process' pathway;
# note: sender and receiver args don't need to be specified
Input_Weights = MappingProjection(name='Input Weights',
matrix=Input_Weights_matrix)
# This projection will be used by the process below by assigning its sender and receiver args
# to mechanismss in the pathway
Middle_Weights = MappingProjection(name='Middle Weights',
sender=Hidden_Layer_1,
receiver=Hidden_Layer_2,
matrix={
VALUE: Middle_Weights_matrix,
FUNCTION: AccumulatorIntegrator,
FUNCTION_PARAMS: {
DEFAULT_VARIABLE:
Middle_Weights_matrix,
INITIALIZER:
Middle_Weights_matrix,
RATE: Middle_Weights_matrix
},
})
Output_Weights = MappingProjection(name='Output Weights',
sender=Hidden_Layer_2,
receiver=Output_Layer,
matrix=Output_Weights_matrix)
z = Process(
# default_variable=[0, 0],
size=2,
pathway=[
Input_Layer,
# The following reference to Input_Weights is needed to use it in the pathway
# since it's sender and receiver args are not specified in its
# declaration above
Input_Weights,
Hidden_Layer_1,
# No projection specification is needed here since the sender arg for Middle_Weights
# is Hidden_Layer_1 and its receiver arg is Hidden_Layer_2
# Middle_Weights,
Hidden_Layer_2,
# Output_Weights does not need to be listed for the same reason as Middle_Weights
# If Middle_Weights and/or Output_Weights is not declared above, then the process
# will assign a default for missing projection
# Output_Weights,
Output_Layer
],
clamp_input=SOFT_CLAMP,
learning=LEARNING,
learning_rate=1.0,
target=[0, 0, 1],
prefs={
VERBOSE_PREF: False,
REPORT_OUTPUT_PREF: True
})
g = Process(default_variable=[1.0], pathway=[Gating_Mechanism])
stim_list = {Input_Layer: [[-1, 30]], Gating_Mechanism: [1.0]}
target_list = {Output_Layer: [[0, 0, 1]]}
def print_header():
print("\n\n**** TRIAL: ", 'time_placeholder')
def show_target():
i = s.input
t = s.target_input_states[0].value
print('\nOLD WEIGHTS: \n')
print('- Input Weights: \n', Input_Weights.matrix)
print('- Middle Weights: \n', Middle_Weights.matrix)
print('- Output Weights: \n', Output_Weights.matrix)
print('\nSTIMULI:\n\n- Input: {}\n- Target: {}\n'.format(i, t))
print('ACTIVITY FROM OLD WEIGHTS: \n')
print('- Middle 1: \n', Hidden_Layer_1.value)
print('- Middle 2: \n', Hidden_Layer_2.value)
print('- Output:\n', Output_Layer.value)
s = System(processes=[z, g], targets=[0, 0, 1], learning_rate=1.0)
s.reportOutputPref = True
# s.show_graph(show_learning=True)
results = s.run(
num_trials=10,
inputs=stim_list,
targets=target_list,
call_before_trial=print_header,
call_after_trial=show_target,
)
def test_danglingControlledMech():
#
# first section is from Stroop Demo
#
Color_Input = TransferMechanism(name='Color Input',
function=Linear(slope=0.2995))
Word_Input = TransferMechanism(name='Word Input',
function=Linear(slope=0.2995))
# Processing Mechanisms (Control)
Color_Hidden = TransferMechanism(
name='Colors Hidden',
function=Logistic(gain=(1.0, ControlProjection)),
)
Word_Hidden = TransferMechanism(
name='Words Hidden',
function=Logistic(gain=(1.0, ControlProjection)),
)
Output = TransferMechanism(
name='Output',
function=Logistic(gain=(1.0, ControlProjection)),
)
# Decision Mechanisms
Decision = DDM(
function=DriftDiffusionAnalytical(
drift_rate=(1.0),
threshold=(0.1654),
noise=(0.5),
starting_point=(0),
t0=0.25,
),
name='Decision',
)
# Outcome Mechanisms:
Reward = TransferMechanism(name='Reward')
# Processes:
ColorNamingProcess = Process(
default_variable=[0],
pathway=[Color_Input, Color_Hidden, Output, Decision],
name='Color Naming Process',
)
WordReadingProcess = Process(
default_variable=[0],
pathway=[Word_Input, Word_Hidden, Output, Decision],
name='Word Reading Process',
)
RewardProcess = Process(
default_variable=[0],
pathway=[Reward],
name='RewardProcess',
)
# add another DDM but do not add to system
second_DDM = DDM(
function=DriftDiffusionAnalytical(drift_rate=(
1.0,
ControlProjection(
function=Linear,
control_signal_params={
ALLOCATION_SAMPLES: np.arange(0.1, 1.01, 0.3)
},
),
),
threshold=(
1.0,
ControlProjection(
function=Linear,
control_signal_params={
ALLOCATION_SAMPLES:
np.arange(
0.1, 1.01, 0.3)
},
),
),
noise=(0.5),
starting_point=(0),
t0=0.45),
name='second_DDM',
)
# System:
mySystem = System(
processes=[ColorNamingProcess, WordReadingProcess, RewardProcess],
controller=EVCControlMechanism,
enable_controller=True,
# monitor_for_control=[Reward, (DDM_PROBABILITY_UPPER_THRESHOLD, 1, -1)],
name='EVC Gratton System',
)
def test_stroop_model_learning(self):
process_prefs = {
REPORT_OUTPUT_PREF: True,
VERBOSE_PREF: False,
}
system_prefs = {
REPORT_OUTPUT_PREF: True,
VERBOSE_PREF: False,
}
colors = TransferMechanism(
default_variable=[0, 0],
function=Linear,
name="Colors",
)
words = TransferMechanism(
default_variable=[0, 0],
function=Linear,
name="Words",
)
hidden = TransferMechanism(
default_variable=[0, 0],
function=Logistic,
name="Hidden",
)
response = TransferMechanism(
default_variable=[0, 0],
function=Logistic(),
name="Response",
)
TransferMechanism(
default_variable=[0, 0],
function=Logistic,
name="Output",
)
CH_Weights_matrix = np.arange(4).reshape((2, 2))
WH_Weights_matrix = np.arange(4).reshape((2, 2))
HO_Weights_matrix = np.arange(4).reshape((2, 2))
CH_Weights = MappingProjection(
name='Color-Hidden Weights',
matrix=CH_Weights_matrix,
)
WH_Weights = MappingProjection(
name='Word-Hidden Weights',
matrix=WH_Weights_matrix,
)
HO_Weights = MappingProjection(
name='Hidden-Output Weights',
matrix=HO_Weights_matrix,
)
color_naming_process = Process(
default_variable=[1, 2.5],
pathway=[colors, CH_Weights, hidden, HO_Weights, response],
learning=LEARNING,
target=[2, 2],
name='Color Naming',
prefs=process_prefs,
)
word_reading_process = Process(
default_variable=[.5, 3],
pathway=[words, WH_Weights, hidden],
name='Word Reading',
learning=LEARNING,
target=[3, 3],
prefs=process_prefs,
)
s = System(
processes=[color_naming_process, word_reading_process],
targets=[20, 20],
name='Stroop Model',
prefs=system_prefs,
)
def show_target():
print('\nColor Naming\n\tInput: {}\n\tTarget: {}'.format([
np.ndarray.tolist(item.parameters.value.get(s))
for item in colors.input_states
], s.targets))
print('Wording Reading:\n\tInput: {}\n\tTarget: {}\n'.format([
np.ndarray.tolist(item.parameters.value.get(s))
for item in words.input_states
], s.targets))
print('Response: \n',
response.output_state.parameters.value.get(s))
print('Hidden-Output:')
print(HO_Weights.get_mod_matrix(s))
print('Color-Hidden:')
print(CH_Weights.get_mod_matrix(s))
print('Word-Hidden:')
print(WH_Weights.get_mod_matrix(s))
stim_list_dict = {colors: [[1, 1]], words: [[-2, -2]]}
target_list_dict = {response: [[1, 1]]}
results = s.run(
num_trials=2,
inputs=stim_list_dict,
targets=target_list_dict,
call_after_trial=show_target,
)
results_list = []
for elem in s.results:
for nested_elem in elem:
nested_elem = nested_elem.tolist()
try:
iter(nested_elem)
except TypeError:
nested_elem = [nested_elem]
results_list.extend(nested_elem)
objective_response = s.mechanisms[3]
objective_hidden = s.mechanisms[7]
from pprint import pprint
pprint(CH_Weights.__dict__)
print(CH_Weights._parameter_states["matrix"].value)
print(CH_Weights.get_mod_matrix(s))
expected_output = [
(colors.output_states[0].parameters.value.get(s),
np.array([1., 1.])),
(words.output_states[0].parameters.value.get(s),
np.array([-2., -2.])),
(hidden.output_states[0].parameters.value.get(s),
np.array([0.13227553, 0.01990677])),
(response.output_states[0].parameters.value.get(s),
np.array([0.51044657, 0.5483048])),
(objective_response.output_states[0].parameters.value.get(s),
np.array([0.48955343, 0.4516952])),
(objective_response.output_states[MSE].parameters.value.get(s),
np.array(0.22184555903789838)),
(CH_Weights.get_mod_matrix(s),
np.array([
[0.02512045, 1.02167245],
[2.02512045, 3.02167245],
])),
(WH_Weights.get_mod_matrix(s),
np.array([
[-0.05024091, 0.9566551],
[1.94975909, 2.9566551],
])),
(HO_Weights.get_mod_matrix(s),
np.array([
[0.03080958, 1.02830959],
[2.00464242, 3.00426575],
])),
(results, [[np.array([0.50899214, 0.54318254])],
[np.array([0.51044657, 0.5483048])]]),
]
for i in range(len(expected_output)):
val, expected = expected_output[i]
# setting absolute tolerance to be in accordance with reference_output precision
# if you do not specify, assert_allcose will use a relative tolerance of 1e-07,
# which WILL FAIL unless you gather higher precision values to use as reference
np.testing.assert_allclose(
val,
expected,
atol=1e-08,
err_msg='Failed on expected_output[{0}]'.format(i))
# KDM 10/16/18: Comparator Mechanism for Hidden is not executed by the system, because it's not associated with
# an output mechanism. So it actually should be None instead of previously [0, 0] which was likely
# a side effect with of conflation of different execution contexts
assert objective_hidden.output_states[0].parameters.value.get(
s) is None
def test_create_scheduler_from_system_StroopDemo(self):
Color_Input = TransferMechanism(name='Color Input',
function=Linear(slope=0.2995))
Word_Input = TransferMechanism(name='Word Input',
function=Linear(slope=0.2995))
# Processing Mechanisms (Control)
Color_Hidden = TransferMechanism(
name='Colors Hidden',
function=Logistic(gain=(1.0, ControlProjection)),
)
Word_Hidden = TransferMechanism(
name='Words Hidden',
function=Logistic(gain=(1.0, ControlProjection)),
)
Output = TransferMechanism(
name='Output',
function=Logistic(gain=(1.0, ControlProjection)),
)
# Decision Mechanisms
Decision = DDM(
function=DriftDiffusionAnalytical(
drift_rate=(1.0),
threshold=(0.1654),
noise=(0.5),
starting_point=(0),
t0=0.25,
),
name='Decision',
)
# Outcome Mechanisms:
Reward = TransferMechanism(name='Reward')
# Processes:
ColorNamingProcess = Process(
default_variable=[0],
pathway=[Color_Input, Color_Hidden, Output, Decision],
name='Color Naming Process',
)
WordReadingProcess = Process(
default_variable=[0],
pathway=[Word_Input, Word_Hidden, Output, Decision],
name='Word Reading Process',
)
RewardProcess = Process(
default_variable=[0],
pathway=[Reward],
name='RewardProcess',
)
# System:
mySystem = System(
processes=[ColorNamingProcess, WordReadingProcess, RewardProcess],
controller=EVCControlMechanism,
enable_controller=True,
# monitor_for_control=[Reward, (PROBABILITY_UPPER_THRESHOLD, 1, -1)],
name='EVC Gratton System',
)
sched = Scheduler(system=mySystem)
integrator_ColorInputPrediction = mySystem.execution_list[7]
integrator_RewardPrediction = mySystem.execution_list[8]
integrator_WordInputPrediction = mySystem.execution_list[9]
objective_EVC_mech = mySystem.execution_list[10]
expected_consideration_queue = [
{
Color_Input, Word_Input, Reward,
integrator_ColorInputPrediction,
integrator_WordInputPrediction, integrator_RewardPrediction
},
{Color_Hidden, Word_Hidden},
{Output},
{Decision},
{objective_EVC_mech},
]
assert sched.consideration_queue == expected_consideration_queue
def test_multilayer(self):
Input_Layer = TransferMechanism(
name='Input Layer',
function=Logistic,
default_variable=np.zeros((2, )),
)
Hidden_Layer_1 = TransferMechanism(
name='Hidden Layer_1',
function=Logistic(),
default_variable=np.zeros((5, )),
)
Hidden_Layer_2 = TransferMechanism(
name='Hidden Layer_2',
function=Logistic(),
default_variable=[0, 0, 0, 0],
)
Output_Layer = TransferMechanism(
name='Output Layer',
function=Logistic,
default_variable=[0, 0, 0],
)
Input_Weights_matrix = (np.arange(2 * 5).reshape((2, 5)) + 1) / (2 * 5)
# TEST PROCESS.LEARNING WITH:
# CREATION OF FREE STANDING PROJECTIONS THAT HAVE NO LEARNING (Input_Weights, Middle_Weights and Output_Weights)
# INLINE CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
# NO EXPLICIT CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
# This projection will be used by the process below by referencing it in the process' pathway;
# note: sender and receiver args don't need to be specified
Input_Weights = MappingProjection(
name='Input Weights',
matrix=Input_Weights_matrix,
)
p = Process(
default_variable=[0, 0],
pathway=[
Input_Layer,
# The following reference to Input_Weights is needed to use it in the pathway
# since it's sender and receiver args are not specified in its declaration above
Input_Weights,
Hidden_Layer_1,
# No projection specification is needed here since the sender arg for Middle_Weights
# is Hidden_Layer_1 and its receiver arg is Hidden_Layer_2
# Middle_Weights,
Hidden_Layer_2,
# Output_Weights does not need to be listed for the same reason as Middle_Weights
# If Middle_Weights and/or Output_Weights is not declared above, then the process
# will assign a default for missing projection
# Output_Weights,
Output_Layer
],
clamp_input=SOFT_CLAMP,
target=[0, 0, 1],
prefs={
VERBOSE_PREF: False,
REPORT_OUTPUT_PREF: True
})
s = System(processes=[p])
s.reportOutputPref = True
stim_list = {Input_Layer: [[-1, 30]]}
s.run(
num_trials=10,
inputs=stim_list,
)
expected_Output_Layer_output = [
np.array([0.97988347, 0.97988347, 0.97988347])
]
np.testing.assert_allclose(expected_Output_Layer_output,
Output_Layer.get_output_values(s))
key_size = n_hidden
val_size = n_hidden
max_entries = 7
# training params
num_epochs = 3
learning_rate = .1
wts_init_scale = .1
# layers
input = pnl.TransferMechanism(name='input', default_variable=np.zeros(n_input))
hidden = pnl.TransferMechanism(name='hidden',
default_variable=np.zeros(n_hidden),
function=Logistic())
output = pnl.TransferMechanism(name='output',
default_variable=np.zeros(n_output),
function=Logistic())
# weights
w_ih = pnl.MappingProjection(name='input_to_hidden',
matrix=np.random.randn(n_input, n_hidden) *
wts_init_scale,
sender=input,
receiver=hidden)
w_ho = pnl.MappingProjection(name='hidden_to_output',
matrix=np.random.randn(n_hidden, n_output) *
wts_init_scale,
