c4a1737fa7
In der config Datei kann man nun einstellen, ob während des Training mit leaky relu, bei einer Performance von 100% auf relu geswitched wird (d.h. leaky relu mit slope = 0.0). In der cnn_trainin.py musste ich beim Lesen und Laden der config.json aufgrund eines komischen Errors beim Ausführen der .sh-file was ändern.
83 lines
No EOL
1.9 KiB
JSON
83 lines
No EOL
1.9 KiB
JSON
{
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"data_path": "/home/kk/Documents/Semester4/code/RenderStimuli/Output/",
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"save_logging_messages": true, // (true), false
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"display_logging_messages": true, // (true), false
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"batch_size_train": 500,
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"batch_size_test": 250,
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"max_epochs": 2000,
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"save_model": true,
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"conv_0_kernel_size": 11,
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"mp_1_kernel_size": 3,
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"mp_1_stride": 2,
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"use_plot_intermediate": true, // true, (false)
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"stimuli_per_pfinkel": 10000,
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"num_pfinkel_start": 0,
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"num_pfinkel_stop": 100,
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"num_pfinkel_step": 10,
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"precision_100_percent": 4, // (4)
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"train_first_layer": true, // true, (false)
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"save_ever_x_epochs": 10, // (10)
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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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"switch_leakyR_to_relu": true,
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// LR Scheduler ->
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"use_scheduler": true, // (true), false
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"scheduler_verbose": true,
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"scheduler_factor": 0.1, //(0.1)
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"scheduler_patience": 10, // (10)
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"scheduler_threshold": 1e-5, // (1e-4)
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"minimum_learning_rate": 1e-8,
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"learning_rate": 0.0001,
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// <- LR Scheduler
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"pooling_type": "max", // (max), average, none
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"conv_0_enable_softmax": false, // true, (false)
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"use_adam": true, // (true) => adam, false => SGD
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"condition": "Coignless",
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"scale_data": 255.0, // (255.0),
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"conv_out_channels_list": [
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[
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32,
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8,
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8
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],
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[
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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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6,
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8,
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8
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],
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[
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4,
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8,
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8
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],
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[
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3,
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8,
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8
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],
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[
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2,
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8,
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],
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[
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1,
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8,
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8
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]
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],
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"conv_kernel_sizes": [
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[
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7,
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15
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]
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],
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"conv_stride_sizes": [
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1
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]
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} |