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@ -165,92 +165,244 @@ def norm_weights(self) -> None:
# Parameters in JSON file
```
data_mode: str = field(default="")
```
data_path: str = field(default="./")
```
```
batch_size: int = field(default=500)
```
```
learning_step: int = field(default=0)
```
```
learning_step_max: int = field(default=10000)
```
```
number_of_cpu_processes: int = field(default=-1)
```
```
number_of_spikes: int = field(default=0)
```
```
cooldown_after_number_of_spikes: int = field(default=0)
```
```
weight_path: str = field(default="./Weights/")
eps_xy_path: str = field(default="./EpsXY/")
reduction_cooldown: float = field(default=25.0)
epsilon_0: float = field(default=1.0)
```
```
eps_xy_path: str = field(default="./EpsXY/")
```
```
reduction_cooldown: float = field(default=25.0)
```
```
epsilon_0: float = field(default=1.0)
```
```
update_after_x_batch: float = field(default=1.0)
```
## network_structure (required!)
Parameters of the network. The details about its layers and the number of output neurons.
```
number_of_output_neurons: int = field(default=0)
```
```
forward_neuron_numbers: list[list[int]] = field(default_factory=list)
```
```
is_pooling_layer: list[bool] = field(default_factory=list)
```
```
forward_kernel_size: list[list[int]] = field(default_factory=list)
strides: list[list[int]] = field(default_factory=list)
dilation: list[list[int]] = field(default_factory=list)
padding: list[list[int]] = field(default_factory=list)
```
```
strides: list[list[int]] = field(default_factory=list)
```
```
dilation: list[list[int]] = field(default_factory=list)
```
```
padding: list[list[int]] = field(default_factory=list)
```
```
w_trainable: list[bool] = field(default_factory=list)
```
```
eps_xy_trainable: list[bool] = field(default_factory=list)
```
```
eps_xy_mean: list[bool] = field(default_factory=list)
```
## learning_parameters
Parameter required for training
```
learning_active: bool = field(default=True)
```
```
loss_coeffs_mse: float = field(default=0.5)
```
```
loss_coeffs_kldiv: float = field(default=1.0)
```
```
optimizer_name: str = field(default="Adam")
```
```
learning_rate_gamma_w: float = field(default=-1.0)
```
```
learning_rate_gamma_eps_xy: float = field(default=-1.0)
```
```
learning_rate_threshold_w: float = field(default=0.00001)
```
```
learning_rate_threshold_eps_xy: float = field(default=0.00001)
```
```
lr_schedule_name: str = field(default="ReduceLROnPlateau")
```
```
lr_scheduler_factor_w: float = field(default=0.75)
```
```
lr_scheduler_patience_w: int = field(default=-1)
```
```
lr_scheduler_factor_eps_xy: float = field(default=0.75)
```
```
lr_scheduler_patience_eps_xy: int = field(default=-1)
```
```
number_of_batches_for_one_update: int = field(default=1)
```
```
overload_path: str = field(default="./Previous")
```
```
weight_noise_amplitude: float = field(default=0.01)
```
```
eps_xy_intitial: float = field(default=0.1)
```
```
test_every_x_learning_steps: int = field(default=50)
test_during_learning: bool = field(default=True)
```
```
test_during_learning: bool = field(default=True)
```
```
alpha_number_of_iterations: int = field(default=0)
```
## augmentation
Parameters used for data augmentation.
```
crop_width_in_pixel: int = field(default=2)
```
```
flip_p: float = field(default=0.5)
```
```
jitter_brightness: float = field(default=0.5)
jitter_contrast: float = field(default=0.1)
jitter_saturation: float = field(default=0.1)
jitter_hue: float = field(default=0.15)
```
```
jitter_contrast: float = field(default=0.1)
```
```
jitter_saturation: float = field(default=0.1)
```
```
jitter_hue: float = field(default=0.15)
```
```
use_on_off_filter: bool = field(default=True)
```
## ImageStatistics (please ignore)
(Statistical) information about the input. i.e. mean values and the x and y size of the input
```
mean: list[float] = field(default_factory=list)
```
```
the_size: list[int] = field(default_factory=list)
```