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test_multilayer.py
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test_multilayer.py
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import numpy as np
from psyneulink.core.components.functions.transferfunctions import Logistic
from psyneulink.core.components.mechanisms.processing.transfermechanism import TransferMechanism
from psyneulink.core.components.process import Process
from psyneulink.core.components.projections.pathway.mappingprojection import MappingProjection
from psyneulink.core.components.system import System
from psyneulink.core.globals.keywords import EXECUTION, LEARNING, PROCESSING, SOFT_CLAMP, VALUE
from psyneulink.core.globals.preferences.basepreferenceset import REPORT_OUTPUT_PREF, VERBOSE_PREF
from psyneulink.library.components.mechanisms.processing.objective.comparatormechanism import MSE
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_ports[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_ports[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_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_ports[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], [2], [3], [4], [5]], # TRIAL
# [[1], [1], [1], [1], [1]], # PASS
[[0], [0], [0], [0], [0]], # PASS
# [[1], [1], [1], [1], [1]], # TIME_STEP
[[0], [0], [0], [0], [0]], # 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)
)