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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,161 @@ | ||
| import numpy as np | ||
| from PsyNeuLink.Components.Projections.TransmissiveProjections.MappingProjection import MappingProjection | ||
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|
||
| from PsyNeuLink.Components.Functions.Function import ConstantIntegrator, Logistic | ||
| from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.GatingMechanisms.GatingMechanism import GatingMechanism | ||
| from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism | ||
| from PsyNeuLink.Components.Process import process | ||
| from PsyNeuLink.Components.System import system | ||
| from PsyNeuLink.Globals.Keywords import FUNCTION, FUNCTION_PARAMS, INITIALIZER, LEARNING, RATE, SOFT_CLAMP, VALUE | ||
| from PsyNeuLink.Globals.Preferences.ComponentPreferenceSet import REPORT_OUTPUT_PREF, VERBOSE_PREF | ||
| from PsyNeuLink.Globals.TimeScale import CentralClock | ||
|
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||
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| def test_gating(): | ||
| Input_Layer = TransferMechanism( | ||
| name='Input Layer', | ||
| function=Logistic, | ||
| default_input_value=np.zeros((2,)) | ||
| ) | ||
|
|
||
| Hidden_Layer_1 = TransferMechanism( | ||
| name='Hidden Layer_1', | ||
| function=Logistic(), | ||
| default_input_value=np.zeros((5,)) | ||
| ) | ||
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||
| Hidden_Layer_2 = TransferMechanism( | ||
| name='Hidden Layer_2', | ||
| function=Logistic(), | ||
| default_input_value=[0, 0, 0, 0] | ||
| ) | ||
|
|
||
| Output_Layer = TransferMechanism( | ||
| name='Output Layer', | ||
| function=Logistic, | ||
| default_input_value=[0, 0, 0] | ||
| ) | ||
|
|
||
| Gating_Mechanism = GatingMechanism( | ||
| default_gating_policy=0.0, | ||
| 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: ConstantIntegrator, | ||
| FUNCTION_PARAMS: { | ||
| 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_input_value=[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, | ||
| learning=LEARNING, | ||
| learning_rate=1.0, | ||
| target=[0, 0, 1], | ||
| prefs={ | ||
| VERBOSE_PREF: False, | ||
| REPORT_OUTPUT_PREF: True | ||
| } | ||
| ) | ||
|
|
||
| g = process( | ||
| default_input_value=[1.0], | ||
| pathway=[Gating_Mechanism] | ||
| ) | ||
|
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||
| stim_list = { | ||
| Input_Layer: [[-1, 30]], | ||
| Gating_Mechanism: [1.0] | ||
| } | ||
| target_list = { | ||
| Output_Layer: [[0, 0, 1]] | ||
| } | ||
|
|
||
| def print_header(): | ||
| print("\n\n**** TRIAL: ", CentralClock.trial) | ||
|
|
||
| def show_target(): | ||
| i = s.input | ||
| t = s.targetInputStates[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) | ||
|
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||
| s = system( | ||
| processes=[z, g], | ||
| targets=[0, 0, 1], | ||
| learning_rate=1.0 | ||
| ) | ||
|
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||
| s.reportOutputPref = True | ||
| # s.show_graph(show_learning=True) | ||
|
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| results = s.run( | ||
| num_executions=10, | ||
| inputs=stim_list, | ||
| targets=target_list, | ||
| call_before_trial=print_header, | ||
| call_after_trial=show_target, | ||
| ) |