backprop is single precision now
This commit is contained in:
David Rotermund
GitHub
parent
aac88b41cf
commit
654014b319
5 changed files with 98 additions and 82 deletions
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@ -71,6 +71,7 @@ class LearningParameters:
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learning_rate_threshold_eps_xy: float = field(default=0.00001)
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lr_schedule_name: str = field(default="ReduceLROnPlateau")
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lr_scheduler_use_performance: bool = field(default=True)
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lr_scheduler_factor_w: float = field(default=0.75)
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lr_scheduler_patience_w: int = field(default=-1)
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@ -133,11 +134,12 @@ class Config:
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number_of_cpu_processes: int = field(default=-1)
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number_of_spikes: int = field(default=0)
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cooldown_after_number_of_spikes: int = field(default=0)
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cooldown_after_number_of_spikes: int = field(default=-1)
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weight_path: str = field(default="./Weights/")
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eps_xy_path: str = field(default="./EpsXY/")
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data_path: str = field(default="./")
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results_path: str = field(default="./Results")
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reduction_cooldown: float = field(default=25.0)
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epsilon_0: float = field(default=1.0)
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@ -159,6 +161,7 @@ class Config:
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os.makedirs(self.weight_path, exist_ok=True)
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os.makedirs(self.eps_xy_path, exist_ok=True)
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os.makedirs(self.data_path, exist_ok=True)
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os.makedirs(self.results_path, exist_ok=True)
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self.batch_size = (
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self.batch_size // self.number_of_cpu_processes
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@ -170,6 +173,9 @@ class Config:
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def get_epsilon_t(self):
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"""Generates the time series of the basic epsilon."""
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np_epsilon_t: np.ndarray = np.ones((self.number_of_spikes), dtype=np.float32)
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if (self.cooldown_after_number_of_spikes < self.number_of_spikes) and (
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self.cooldown_after_number_of_spikes >= 0
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):
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np_epsilon_t[
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self.cooldown_after_number_of_spikes : self.number_of_spikes
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] /= self.reduction_cooldown
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94
SbS.py
94
SbS.py
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@ -122,7 +122,7 @@ class SbS(torch.nn.Module):
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self.initialize_epsilon_xy(epsilon_xy_intitial)
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self.epsilon_0 = torch.tensor(epsilon_0, dtype=torch.float64)
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self.epsilon_0 = torch.tensor(epsilon_0, dtype=torch.float32)
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self.number_of_cpu_processes = torch.tensor(
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number_of_cpu_processes, dtype=torch.int64
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@ -130,7 +130,7 @@ class SbS(torch.nn.Module):
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self.number_of_spikes = torch.tensor(number_of_spikes, dtype=torch.int64)
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self.epsilon_t = epsilon_t.type(dtype=torch.float64)
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self.epsilon_t = epsilon_t.type(dtype=torch.float32)
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self.initialize_weights(
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is_pooling_layer=is_pooling_layer,
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@ -155,7 +155,7 @@ class SbS(torch.nn.Module):
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assert value is not None
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assert torch.is_tensor(value) is True
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assert value.dim() == 4
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assert value.dtype == torch.float64
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assert value.dtype == torch.float32
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if self._epsilon_xy_exists is False:
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self._epsilon_xy = torch.nn.parameter.Parameter(
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value.detach().clone(memory_format=torch.contiguous_format),
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@ -176,7 +176,7 @@ class SbS(torch.nn.Module):
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assert value is not None
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assert torch.is_tensor(value) is True
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assert torch.numel(value) == 1
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assert value.dtype == torch.float64
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assert value.dtype == torch.float32
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assert value.item() > 0
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self._epsilon_0 = value.detach().clone(memory_format=torch.contiguous_format)
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self._epsilon_0.requires_grad_(False)
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@ -190,7 +190,7 @@ class SbS(torch.nn.Module):
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assert value is not None
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assert torch.is_tensor(value) is True
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assert value.dim() == 1
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assert value.dtype == torch.float64
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assert value.dtype == torch.float32
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self._epsilon_t = value.detach().clone(memory_format=torch.contiguous_format)
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self._epsilon_t.requires_grad_(False)
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@ -206,9 +206,9 @@ class SbS(torch.nn.Module):
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assert value is not None
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assert torch.is_tensor(value) is True
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assert value.dim() == 2
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assert value.dtype == torch.float64
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assert value.dtype == torch.float32
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temp: torch.Tensor = value.detach().clone(memory_format=torch.contiguous_format)
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temp /= temp.sum(dim=0, keepdim=True, dtype=torch.float64)
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temp /= temp.sum(dim=0, keepdim=True, dtype=torch.float32)
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if self._weights_exists is False:
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self._weights = torch.nn.parameter.Parameter(
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temp,
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@ -402,7 +402,7 @@ class SbS(torch.nn.Module):
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assert input is not None
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assert torch.is_tensor(input) is True
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assert input.dim() == 4
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assert input.dtype == torch.float64
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assert input.dtype == torch.float32
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# Are we happy with the rest of the network?
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assert self._epsilon_xy_exists is True
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@ -499,7 +499,7 @@ class SbS(torch.nn.Module):
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torch.unsqueeze(
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torch.unsqueeze(
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torch.unsqueeze(
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torch.arange(0, int(value[0]), dtype=torch.float64),
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torch.arange(0, int(value[0]), dtype=torch.float32),
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1,
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),
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0,
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@ -516,7 +516,7 @@ class SbS(torch.nn.Module):
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torch.unsqueeze(
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torch.unsqueeze(
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torch.unsqueeze(
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torch.arange(0, int(value[1]), dtype=torch.float64),
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torch.arange(0, int(value[1]), dtype=torch.float32),
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0,
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),
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0,
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@ -537,7 +537,7 @@ class SbS(torch.nn.Module):
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assert torch.numel(noise_amplitude) == 1
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assert noise_amplitude.item() >= 0
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assert noise_amplitude.dtype == torch.float64
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assert noise_amplitude.dtype == torch.float32
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assert self._number_of_neurons is not None
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assert self._number_of_input_neurons is not None
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@ -550,7 +550,7 @@ class SbS(torch.nn.Module):
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int(self._number_of_input_neurons),
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int(self._number_of_neurons),
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),
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dtype=torch.float64,
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dtype=torch.float32,
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)
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torch.nn.init.uniform_(weights, a=1.0, b=(1.0 + noise_amplitude.item()))
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@ -571,7 +571,7 @@ class SbS(torch.nn.Module):
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int(self._number_of_neurons),
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int(self._number_of_neurons),
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),
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dtype=torch.float64,
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dtype=torch.float32,
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)
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for i in range(0, int(self._number_of_neurons)):
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@ -593,7 +593,7 @@ class SbS(torch.nn.Module):
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weights = self._make_pooling_weights()
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else:
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weights = self._initial_random_weights(
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torch.tensor(noise_amplitude, dtype=torch.float64)
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torch.tensor(noise_amplitude, dtype=torch.float32)
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)
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weights = weights.moveaxis(-1, 0).moveaxis(-1, 1)
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@ -628,7 +628,7 @@ class SbS(torch.nn.Module):
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int(self._kernel_size[1]),
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),
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eps_xy_intitial,
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dtype=torch.float64,
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dtype=torch.float32,
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)
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self.epsilon_xy = eps_xy_temp
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@ -660,7 +660,7 @@ class SbS(torch.nn.Module):
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fill_value: float = float(self._epsilon_xy.data.mean())
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self._epsilon_xy.data = torch.full_like(
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self._epsilon_xy.data, fill_value, dtype=torch.float64
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self._epsilon_xy.data, fill_value, dtype=torch.float32
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)
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def threshold_epsilon_xy(self, threshold: float) -> None:
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@ -688,9 +688,9 @@ class SbS(torch.nn.Module):
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temp: torch.Tensor = (
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self._weights.data.detach()
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.clone(memory_format=torch.contiguous_format)
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.type(dtype=torch.float64)
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.type(dtype=torch.float32)
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)
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temp /= temp.sum(dim=0, keepdim=True, dtype=torch.float64)
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temp /= temp.sum(dim=0, keepdim=True, dtype=torch.float32)
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self._weights.data = temp
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def threshold_weights(self, threshold: float) -> None:
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@ -708,11 +708,11 @@ class FunctionalSbS(torch.autograd.Function):
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@staticmethod
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def forward( # type: ignore
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ctx,
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input_float64: torch.Tensor,
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epsilon_xy_float64: torch.Tensor,
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epsilon_0_float64: torch.Tensor,
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epsilon_t_float64: torch.Tensor,
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weights_float64: torch.Tensor,
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input: torch.Tensor,
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epsilon_xy: torch.Tensor,
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epsilon_0: torch.Tensor,
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epsilon_t: torch.Tensor,
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weights: torch.Tensor,
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kernel_size: torch.Tensor,
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stride: torch.Tensor,
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dilation: torch.Tensor,
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@ -724,11 +724,7 @@ class FunctionalSbS(torch.autograd.Function):
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alpha_number_of_iterations: torch.Tensor,
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) -> torch.Tensor:
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input = input_float64.type(dtype=torch.float32)
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epsilon_xy = epsilon_xy_float64.type(dtype=torch.float32)
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weights = weights_float64.type(dtype=torch.float32)
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epsilon_0 = epsilon_0_float64.type(dtype=torch.float32)
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epsilon_t = epsilon_t_float64.type(dtype=torch.float32)
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torch.set_default_dtype(torch.float32)
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assert input.dim() == 4
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assert torch.numel(kernel_size) == 2
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@ -1097,7 +1093,7 @@ class FunctionalSbS(torch.autograd.Function):
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)
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alpha_dynamic = alpha_temp.sum(dim=1, keepdim=True)
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alpha_dynamic += torch.finfo(torch.float32).eps * 1000
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alpha_dynamic += 1e-20
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# Alpha normalization
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alpha_dynamic /= alpha_dynamic.sum(dim=3, keepdim=True).sum(
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@ -1114,13 +1110,11 @@ class FunctionalSbS(torch.autograd.Function):
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# Save the necessary data for the backward pass #
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############################################################
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output = output.type(dtype=torch.float64)
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ctx.save_for_backward(
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input_convolved,
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epsilon_xy_convolved,
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epsilon_0_float64,
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weights_float64,
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epsilon_0,
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weights,
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output,
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kernel_size,
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stride,
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@ -1136,8 +1130,8 @@ class FunctionalSbS(torch.autograd.Function):
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# Get the variables back
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(
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input_float32,
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epsilon_xy_float32,
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input,
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epsilon_xy,
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epsilon_0,
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weights,
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output,
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@ -1148,9 +1142,9 @@ class FunctionalSbS(torch.autograd.Function):
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input_size,
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) = ctx.saved_tensors
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input = input_float32.type(dtype=torch.float64)
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input /= input.sum(dim=1, keepdim=True, dtype=torch.float64)
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epsilon_xy = epsilon_xy_float32.type(dtype=torch.float64)
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torch.set_default_dtype(torch.float32)
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input /= input.sum(dim=1, keepdim=True, dtype=torch.float32)
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# For debugging:
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# print(
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@ -1172,21 +1166,21 @@ class FunctionalSbS(torch.autograd.Function):
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backprop_bigr: torch.Tensor = backprop_r.sum(axis=2)
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temp: torch.Tensor = input / backprop_bigr**2
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temp: torch.Tensor = input / (backprop_bigr**2 + 1e-20)
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backprop_f: torch.Tensor = output.unsqueeze(1) * temp.unsqueeze(2)
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torch.nan_to_num(
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backprop_f, out=backprop_f, nan=1e300, posinf=1e300, neginf=-1e300
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backprop_f, out=backprop_f, nan=1e30, posinf=1e30, neginf=-1e30
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)
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torch.clip(backprop_f, out=backprop_f, min=-1e300, max=1e300)
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torch.clip(backprop_f, out=backprop_f, min=-1e30, max=1e30)
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tempz: torch.Tensor = 1.0 / backprop_bigr
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tempz: torch.Tensor = 1.0 / (backprop_bigr + 1e-20)
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backprop_z: torch.Tensor = backprop_r * tempz.unsqueeze(2)
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torch.nan_to_num(
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backprop_z, out=backprop_z, nan=1e300, posinf=1e300, neginf=-1e300
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backprop_z, out=backprop_z, nan=1e30, posinf=1e30, neginf=-1e30
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)
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torch.clip(backprop_z, out=backprop_z, min=-1e300, max=1e300)
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torch.clip(backprop_z, out=backprop_z, min=-1e30, max=1e30)
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result_omega: torch.Tensor = backprop_bigr.unsqueeze(2) * grad_output.unsqueeze(
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1
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@ -1211,9 +1205,9 @@ class FunctionalSbS(torch.autograd.Function):
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grad_weights = result_omega.sum(0).sum(-1).sum(-1)
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torch.nan_to_num(
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grad_weights, out=grad_weights, nan=1e300, posinf=1e300, neginf=-1e300
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grad_weights, out=grad_weights, nan=1e30, posinf=1e30, neginf=-1e30
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)
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torch.clip(grad_weights, out=grad_weights, min=-1e300, max=1e300)
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torch.clip(grad_weights, out=grad_weights, min=-1e30, max=1e30)
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grad_input = torch.nn.functional.fold(
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torch.nn.functional.unfold(
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@ -1230,9 +1224,9 @@ class FunctionalSbS(torch.autograd.Function):
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stride=stride,
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)
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torch.nan_to_num(
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grad_input, out=grad_input, nan=1e300, posinf=1e300, neginf=-1e300
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grad_input, out=grad_input, nan=1e30, posinf=1e30, neginf=-1e30
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)
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torch.clip(grad_input, out=grad_input, min=-1e300, max=1e300)
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torch.clip(grad_input, out=grad_input, min=-1e30, max=1e30)
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grad_eps_xy_temp = torch.nn.functional.fold(
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result_eps_xy.moveaxis(0, -1)
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@ -1260,9 +1254,9 @@ class FunctionalSbS(torch.autograd.Function):
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.contiguous(memory_format=torch.contiguous_format)
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)
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torch.nan_to_num(
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grad_eps_xy, out=grad_eps_xy, nan=1e300, posinf=1e300, neginf=-1e300
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grad_eps_xy, out=grad_eps_xy, nan=1e30, posinf=1e30, neginf=-1e30
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)
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torch.clip(grad_eps_xy, out=grad_eps_xy, min=-1e300, max=1e300)
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torch.clip(grad_eps_xy, out=grad_eps_xy, min=-1e30, max=1e30)
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grad_epsilon_0 = None
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grad_epsilon_t = None
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42
learn_it.py
42
learn_it.py
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@ -56,6 +56,8 @@ from torch.utils.tensorboard import SummaryWriter
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tb = SummaryWriter()
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torch.set_default_dtype(torch.float32)
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#######################################################################
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# We want to log what is going on into a file and screen #
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#######################################################################
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@ -191,7 +193,7 @@ for id in range(0, len(network)):
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if os.path.exists(filename) is True:
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network[id].weights = torch.tensor(
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np.load(filename),
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dtype=torch.float64,
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dtype=torch.float32,
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)
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wf[id] = np.load(filename)
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@ -206,7 +208,7 @@ for id in range(0, len(network)):
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if os.path.exists(filename) is True:
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network[id].epsilon_xy = torch.tensor(
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np.load(filename),
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dtype=torch.float64,
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dtype=torch.float32,
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)
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eps_xy[id] = np.load(filename)
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@ -225,7 +227,7 @@ for id in range(0, len(network)):
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if len(file_to_load) == 1:
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network[id].weights = torch.tensor(
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np.load(file_to_load[0]),
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dtype=torch.float64,
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dtype=torch.float32,
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)
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wf[id] = np.load(file_to_load[0])
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logging.info(f"File used: {file_to_load[0]}")
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@ -243,7 +245,7 @@ for id in range(0, len(network)):
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if len(file_to_load) == 1:
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network[id].epsilon_xy = torch.tensor(
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np.load(file_to_load[0]),
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dtype=torch.float64,
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dtype=torch.float32,
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)
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eps_xy[id] = np.load(file_to_load[0])
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logging.info(f"File used: {file_to_load[0]}")
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@ -346,7 +348,7 @@ with torch.no_grad():
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h_collection = []
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h_collection.append(
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the_dataset_train.pattern_filter_train(h_x, cfg).type(
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dtype=torch.float64
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dtype=torch.float32
|
||||
)
|
||||
)
|
||||
for id in range(0, len(network)):
|
||||
|
|
@ -365,21 +367,21 @@ with torch.no_grad():
|
|||
target_one_hot = (
|
||||
target_one_hot.unsqueeze(2)
|
||||
.unsqueeze(2)
|
||||
.type(dtype=torch.float64)
|
||||
.type(dtype=torch.float32)
|
||||
)
|
||||
|
||||
# through the loss functions
|
||||
h_y1 = torch.log(h_collection[-1])
|
||||
h_y2 = torch.nan_to_num(h_y1, nan=0.0, posinf=0.0, neginf=0.0)
|
||||
h_y1 = torch.log(h_collection[-1] + 1e-20)
|
||||
|
||||
my_loss: torch.Tensor = (
|
||||
(
|
||||
torch.nn.functional.mse_loss(
|
||||
h_collection[-1], target_one_hot, reduction="none"
|
||||
h_collection[-1],
|
||||
target_one_hot,
|
||||
reduction="none",
|
||||
)
|
||||
* cfg.learning_parameters.loss_coeffs_mse
|
||||
+ torch.nn.functional.kl_div(
|
||||
h_y2, target_one_hot, reduction="none"
|
||||
h_y1, target_one_hot + 1e-20, reduction="none"
|
||||
)
|
||||
* cfg.learning_parameters.loss_coeffs_kldiv
|
||||
)
|
||||
|
|
@ -392,6 +394,7 @@ with torch.no_grad():
|
|||
time_1: float = time.perf_counter()
|
||||
|
||||
my_loss.backward()
|
||||
|
||||
my_loss_float = my_loss.item()
|
||||
time_2: float = time.perf_counter()
|
||||
|
||||
|
|
@ -447,7 +450,7 @@ with torch.no_grad():
|
|||
network[id].norm_weights()
|
||||
else:
|
||||
network[id].weights = torch.tensor(
|
||||
wf[id], dtype=torch.float64
|
||||
wf[id], dtype=torch.float32
|
||||
)
|
||||
|
||||
if cfg.network_structure.eps_xy_trainable[id] is True:
|
||||
|
|
@ -458,7 +461,7 @@ with torch.no_grad():
|
|||
network[id].mean_epsilon_xy()
|
||||
else:
|
||||
network[id].epsilon_xy = torch.tensor(
|
||||
eps_xy[id], dtype=torch.float64
|
||||
eps_xy[id], dtype=torch.float32
|
||||
)
|
||||
|
||||
if cfg.network_structure.w_trainable[id] is True:
|
||||
|
|
@ -504,13 +507,18 @@ with torch.no_grad():
|
|||
# Let the torch learning rate scheduler update the
|
||||
# learning rates of the optimiers
|
||||
if cfg.learning_parameters.lr_scheduler_patience_w > 0:
|
||||
if cfg.learning_parameters.lr_scheduler_use_performance is True:
|
||||
lr_scheduler_wf.step(100.0 - performance)
|
||||
else:
|
||||
lr_scheduler_wf.step(my_loss_for_batch)
|
||||
|
||||
if cfg.learning_parameters.lr_scheduler_patience_eps_xy > 0:
|
||||
if cfg.learning_parameters.lr_scheduler_use_performance is True:
|
||||
lr_scheduler_eps.step(100.0 - performance)
|
||||
else:
|
||||
lr_scheduler_eps.step(my_loss_for_batch)
|
||||
|
||||
tb.add_scalar(
|
||||
"Train Error", 100.0 - performance, cfg.learning_step
|
||||
)
|
||||
tb.add_scalar("Train Error", 100.0 - performance, cfg.learning_step)
|
||||
tb.add_scalar("Train Loss", my_loss_for_batch, cfg.learning_step)
|
||||
tb.add_scalar(
|
||||
"Learning Rate Scale WF",
|
||||
|
|
@ -568,7 +576,7 @@ with torch.no_grad():
|
|||
|
||||
h_h: torch.Tensor = network(
|
||||
the_dataset_test.pattern_filter_test(h_x, cfg).type(
|
||||
dtype=torch.float64
|
||||
dtype=torch.float32
|
||||
)
|
||||
)
|
||||
|
||||
|
|
|
|||
6
test_all.sh
Normal file
6
test_all.sh
Normal file
|
|
@ -0,0 +1,6 @@
|
|||
#!/bin/bash
|
||||
for i in $(seq 1 1 999)
|
||||
do
|
||||
echo $i
|
||||
/home/davrot/P3.10/bin/python3 test_it.py mnist.json $i
|
||||
done
|
||||
12
test_it.py
12
test_it.py
|
|
@ -182,7 +182,7 @@ for id in range(0, len(network)):
|
|||
if len(file_to_load) == 1:
|
||||
network[id].weights = torch.tensor(
|
||||
np.load(file_to_load[0]),
|
||||
dtype=torch.float64,
|
||||
dtype=torch.float32,
|
||||
)
|
||||
wf[id] = np.load(file_to_load[0])
|
||||
logging.info(f"File used: {file_to_load[0]}")
|
||||
|
|
@ -200,7 +200,7 @@ for id in range(0, len(network)):
|
|||
if len(file_to_load) == 1:
|
||||
network[id].epsilon_xy = torch.tensor(
|
||||
np.load(file_to_load[0]),
|
||||
dtype=torch.float64,
|
||||
dtype=torch.float32,
|
||||
)
|
||||
eps_xy[id] = np.load(file_to_load[0])
|
||||
logging.info(f"File used: {file_to_load[0]}")
|
||||
|
|
@ -219,7 +219,7 @@ for id in range(0, len(network)):
|
|||
if os.path.exists(filename) is True:
|
||||
network[id].weights = torch.tensor(
|
||||
np.load(filename),
|
||||
dtype=torch.float64,
|
||||
dtype=torch.float32,
|
||||
)
|
||||
wf[id] = np.load(filename)
|
||||
|
||||
|
|
@ -234,7 +234,7 @@ for id in range(0, len(network)):
|
|||
if os.path.exists(filename) is True:
|
||||
network[id].epsilon_xy = torch.tensor(
|
||||
np.load(filename),
|
||||
dtype=torch.float64,
|
||||
dtype=torch.float32,
|
||||
)
|
||||
eps_xy[id] = np.load(filename)
|
||||
|
||||
|
|
@ -256,7 +256,7 @@ with torch.no_grad():
|
|||
time_0 = time.perf_counter()
|
||||
|
||||
h_h: torch.Tensor = network(
|
||||
the_dataset_test.pattern_filter_test(h_x, cfg).type(dtype=torch.float64)
|
||||
the_dataset_test.pattern_filter_test(h_x, cfg).type(dtype=torch.float32)
|
||||
)
|
||||
|
||||
test_correct += (h_h.argmax(dim=1).squeeze() == h_x_labels).sum().numpy()
|
||||
|
|
@ -271,6 +271,8 @@ with torch.no_grad():
|
|||
f" with {performance/100:^6.2%} \t Time used: {time_measure_a:^6.2f}sec"
|
||||
)
|
||||
)
|
||||
np_performance = np.array(performance)
|
||||
np.save(f"{cfg.results_path}/{cfg.learning_step}.npy", np_performance)
|
||||
|
||||
|
||||
# %%
|
||||
|
|
|
|||
Loading…
Reference in a new issue