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74
config.json
74
config.json
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@ -1,49 +1,57 @@
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{
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{
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"data_path": "/home/kk/Documents/Semester4/code/RenderStimuli/Output/",
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"model_continue": false, // true, (false)
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"model_continue": false, // true, (false)
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"save_logging_messages": true, // (true), false
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"max_epochs": 5000,
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"display_logging_messages": true, // (true), false
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"batch_size_train": 250,
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"batch_size_train": 250,
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"batch_size_test": 500,
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"batch_size_test": 500,
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"max_epochs": 5000,
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"save_model": true,
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// data source ->
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"conv_0_kernel_size": 11,
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"data_path": "/home/kk/Documents/Semester4/code/RenderStimuli/Output/",
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"mp_1_kernel_size": 3,
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"mp_1_stride": 2,
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"use_plot_intermediate": false, // true, (false)
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"stimuli_per_pfinkel": 30000,
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"stimuli_per_pfinkel": 30000,
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"num_pfinkel_start": 0,
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"num_pfinkel_start": 0,
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"num_pfinkel_stop": 10,
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"num_pfinkel_stop": 10,
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"num_pfinkel_step": 10,
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"num_pfinkel_step": 10,
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"precision_100_percent": 4, // (4)
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"condition": "Coignless",
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"train_first_layer": true, // true, (false)
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"scale_data": 255.0, // (255.0)
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"save_ever_x_epochs": 10, // (10)
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// <- data source
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"activation_function": "leaky relu", // tanh, relu, (leaky relu), none
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"leak_relu_negative_slope": 0.1, // (0.1)
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// optimizer ->
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"use_adam": true, // (true) => adam, false => SGD
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// <- optimizer
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// LR Scheduler ->
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// LR Scheduler ->
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"use_scheduler": true, // (true), false
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"use_scheduler": true, // (true), false
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"scheduler_verbose": true,
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"scheduler_verbose": true,
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"scheduler_factor": 0.025, //(0.1)
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"scheduler_factor": 0.025, //(0.1)
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"scheduler_patience": 100, // (10)
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"scheduler_patience": 10, // (10)
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"scheduler_threshold": 1e-5, // (1e-4)
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"scheduler_threshold": 1e-5, // (1e-5)
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"minimum_learning_rate": 1e-10,
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"minimum_learning_rate": 1e-10,
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"learning_rate": 1e-4,
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"learning_rate": 1e-4,
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// <- LR Scheduler
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// <- LR Scheduler
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// type of pooling layer ->
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// pooling layer ->
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"pooling_type": "max", // (max), average, none
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"pooling_type": "max", // (max), average, none
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// <- type of pooling layer
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// <- pooling layer
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// activation function ->
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"activation_function": "leaky relu", // tanh, relu, (leaky relu), none
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"leak_relu_negative_slope": 0.1, // (0.1)
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// <- activation function
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// convolution layer 0 softmax parameters ->
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// convolution layer 0 softmax parameters ->
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"conv_0_enable_softmax": true, // true, (false)
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"conv_0_enable_softmax": false, // true, (false)
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"conv_0_power_softmax": 0.0, // (0.0) = Exp
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"conv_0_power_softmax": 4.0, // (0.0) = Exp
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"conv_0_meanmode_softmax": false, // true, false
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"conv_0_meanmode_softmax": true, // true, false
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"conv_0_no_input_mode_softmax": false, // true, (false)
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"conv_0_no_input_mode_softmax": false, // true, (false)
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// <- convolution layer 0 softmax parameters
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// <- convolution layer 0 softmax parameters
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"use_adam": true, // (true) => adam, false => SGD
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"condition": "Coignless",
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// network definion ->
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"scale_data": 255.0, // (255.0)
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"train_first_layer": true, // true, (false)
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"mp_1_kernel_size": 3,
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"mp_1_stride": 2,
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"conv_0_kernel_size": 11,
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"conv_out_channels_list": [
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"conv_out_channels_list": [
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[
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[
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32,
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8,
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8,
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8,
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8
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8
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]
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]
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"conv_stride_sizes": [
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"conv_stride_sizes": [
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1
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1
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],
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],
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// <- network definion
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// logging ->
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"save_logging_messages": true, // (true), false
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"display_logging_messages": true, // (true), false
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// <- logging
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// saving the model and co ->
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"save_model": true,
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"use_plot_intermediate": false, // true, (false)
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"precision_100_percent": 4, // (4)
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"save_ever_x_epochs": 10, // (10)
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// <- saving the model and co
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// path definitions ->
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"initial_model_path": "initial_models",
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"initial_model_path": "initial_models",
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"tb_runs_path": "tb_runs",
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"tb_runs_path": "tb_runs",
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"trained_models_path": "trained_models",
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"trained_models_path": "trained_models",
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"performance_data_path": "performance_data"
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"performance_data_path": "performance_data"
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// <- path definitions
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}
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}
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