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David Rotermund 2023-07-23 14:07:20 +02:00 committed by GitHub
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@ -1,49 +1,57 @@
{ {
"data_path": "/home/kk/Documents/Semester4/code/RenderStimuli/Output/",
"model_continue": false, // true, (false) "model_continue": false, // true, (false)
"save_logging_messages": true, // (true), false "max_epochs": 5000,
"display_logging_messages": true, // (true), false
"batch_size_train": 250, "batch_size_train": 250,
"batch_size_test": 500, "batch_size_test": 500,
"max_epochs": 5000,
"save_model": true, // data source ->
"conv_0_kernel_size": 11, "data_path": "/home/kk/Documents/Semester4/code/RenderStimuli/Output/",
"mp_1_kernel_size": 3,
"mp_1_stride": 2,
"use_plot_intermediate": false, // true, (false)
"stimuli_per_pfinkel": 30000, "stimuli_per_pfinkel": 30000,
"num_pfinkel_start": 0, "num_pfinkel_start": 0,
"num_pfinkel_stop": 10, "num_pfinkel_stop": 10,
"num_pfinkel_step": 10, "num_pfinkel_step": 10,
"precision_100_percent": 4, // (4) "condition": "Coignless",
"train_first_layer": true, // true, (false) "scale_data": 255.0, // (255.0)
"save_ever_x_epochs": 10, // (10) // <- data source
"activation_function": "leaky relu", // tanh, relu, (leaky relu), none
"leak_relu_negative_slope": 0.1, // (0.1) // optimizer ->
"use_adam": true, // (true) => adam, false => SGD
// <- optimizer
// LR Scheduler -> // LR Scheduler ->
"use_scheduler": true, // (true), false "use_scheduler": true, // (true), false
"scheduler_verbose": true, "scheduler_verbose": true,
"scheduler_factor": 0.025, //(0.1) "scheduler_factor": 0.025, //(0.1)
"scheduler_patience": 100, // (10) "scheduler_patience": 10, // (10)
"scheduler_threshold": 1e-5, // (1e-4) "scheduler_threshold": 1e-5, // (1e-5)
"minimum_learning_rate": 1e-10, "minimum_learning_rate": 1e-10,
"learning_rate": 1e-4, "learning_rate": 1e-4,
// <- LR Scheduler // <- LR Scheduler
// type of pooling layer ->
// pooling layer ->
"pooling_type": "max", // (max), average, none "pooling_type": "max", // (max), average, none
// <- type of pooling layer // <- pooling layer
// activation function ->
"activation_function": "leaky relu", // tanh, relu, (leaky relu), none
"leak_relu_negative_slope": 0.1, // (0.1)
// <- activation function
// convolution layer 0 softmax parameters -> // convolution layer 0 softmax parameters ->
"conv_0_enable_softmax": true, // true, (false) "conv_0_enable_softmax": false, // true, (false)
"conv_0_power_softmax": 0.0, // (0.0) = Exp "conv_0_power_softmax": 4.0, // (0.0) = Exp
"conv_0_meanmode_softmax": false, // true, false "conv_0_meanmode_softmax": true, // true, false
"conv_0_no_input_mode_softmax": false, // true, (false) "conv_0_no_input_mode_softmax": false, // true, (false)
// <- convolution layer 0 softmax parameters // <- convolution layer 0 softmax parameters
"use_adam": true, // (true) => adam, false => SGD
"condition": "Coignless", // network definion ->
"scale_data": 255.0, // (255.0) "train_first_layer": true, // true, (false)
"mp_1_kernel_size": 3,
"mp_1_stride": 2,
"conv_0_kernel_size": 11,
"conv_out_channels_list": [ "conv_out_channels_list": [
[ [
32, 8,
8, 8,
8 8
] ]
@ -57,8 +65,24 @@
"conv_stride_sizes": [ "conv_stride_sizes": [
1 1
], ],
// <- network definion
// logging ->
"save_logging_messages": true, // (true), false
"display_logging_messages": true, // (true), false
// <- logging
// saving the model and co ->
"save_model": true,
"use_plot_intermediate": false, // true, (false)
"precision_100_percent": 4, // (4)
"save_ever_x_epochs": 10, // (10)
// <- saving the model and co
// path definitions ->
"initial_model_path": "initial_models", "initial_model_path": "initial_models",
"tb_runs_path": "tb_runs", "tb_runs_path": "tb_runs",
"trained_models_path": "trained_models", "trained_models_path": "trained_models",
"performance_data_path": "performance_data" "performance_data_path": "performance_data"
// <- path definitions
} }