- state load/save revised
- num gibbs samples is also part of entity - working 3-layer deep test model
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+8
-3
@@ -2,10 +2,11 @@ from .state import RbmState
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from .matrix import prob, Mat
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class EntityParams:
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def __init__(self, do_gaussian_visible: bool = False, do_gaussian_hidden: bool = False):
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def __init__(self, do_gaussian_visible: bool = False, do_gaussian_hidden: bool = False, num_gibbs_samples: int = 1):
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# Entity parameters
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self.do_gaussian_visible = do_gaussian_visible
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self.do_gaussian_hidden = do_gaussian_hidden
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self.num_gibbs_samples = num_gibbs_samples
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@classmethod
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def from_dict(cls, params: dict):
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@@ -14,6 +15,8 @@ class EntityParams:
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obj.do_gaussian_visible = params["doGaussianVisible"]
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if "doGaussianHidden" in params:
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obj.do_gaussian_hidden = params["doGaussianHidden"]
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if "num_gibbs_samples" in params:
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obj.num_gibbs_samples = params["num_gibbs_samples"]
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return obj
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@@ -44,7 +47,8 @@ class Entity:
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self.state.b_h += grad.b_h
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self.state.w_hv += grad.w_hv
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def forward(self, v: Mat, num_gibbs: int = 1) -> Mat:
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def forward(self, v: Mat, num_gibbs: int = 0) -> Mat:
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num_gibbs = self.params.num_gibbs_samples if num_gibbs == 0 else num_gibbs
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h = self._v_to_ph(v)
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for i in range(num_gibbs-1):
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h = self._h_to_pv(h)
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@@ -52,7 +56,8 @@ class Entity:
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return h
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def reconstruct(self, h: Mat, num_gibbs: int = 1) -> Mat:
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def reconstruct(self, h: Mat, num_gibbs: int = 0) -> Mat:
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num_gibbs = self.params.num_gibbs_samples if num_gibbs == 0 else num_gibbs
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v = self._h_to_pv(h)
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for i in range(num_gibbs-1):
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v = self._v_to_ph(v)
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+2
-5
@@ -13,13 +13,10 @@ class Layer:
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self.entity.state.init(mu=0, std=std)
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def save(self, filename: str = None):
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self.entity.state.to_file(filename)
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self.entity.state.save(filename)
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def load(self, filename: str = None):
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state = RbmState.from_file(filename)
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if state is not None:
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self.entity.state = state
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self.entity.state.load(filename)
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+7
-7
@@ -5,13 +5,13 @@ from .entity import Entity
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from .train import train, TrainingParams, cd_jens
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from .matrix import Mat, np
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from .status import Status
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from .state import RbmState
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class Model(ABC):
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known_classes = [Entity]
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def __init__(self, name: str = "myStack", work_dir: str = "."):
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self.name = name
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self.work_dir = work_dir
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os.makedirs(self.work_dir, exist_ok=True)
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def objects(self, obj_type: type = Entity) -> list[Entity]:
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obj_list: list[type[obj_type]] = []
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@@ -24,7 +24,7 @@ class Model(ABC):
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_batch = np.copy(batch)
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for entity in self.objects(Entity):
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train(entity, _batch, params, Status(), cd_jens)
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_batch = entity(_batch)
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_batch = entity.forward(_batch, num_gibbs=params.num_gibbs_samples)
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@abstractmethod
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def forward(self, x: Mat) -> Mat:
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@@ -33,13 +33,13 @@ class Model(ABC):
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def save(self):
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for index, entity in enumerate(self.objects(Entity)):
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filepath = os.path.join(self.work_dir, f"{self.name}-{index}-state.npz")
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entity.state.to_file(filepath)
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entity.state.save(filepath)
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def load(self):
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for index, entity in enumerate(self.objects(Entity)):
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filepath = os.path.join(self.work_dir, f"{self.name}-{index}-state.npz")
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state = RbmState.from_file(filepath)
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if state is not None:
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entity.state = state
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entity.state.load(filepath)
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def init(self, std: float):
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for index, entity in enumerate(self.objects(Entity)):
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entity.state.init(std=std)
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+16
-1
@@ -36,7 +36,22 @@ class RbmState:
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def h_to_v(self, hidden: Mat) -> Mat:
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return np.dot(hidden, np.transpose(self.w_hv)) + self.b_v
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def to_file(self, filename: str):
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def load(self, filename: str):
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try:
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with np.load(filename) as X:
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w_hv, b_v, b_h = [X[i] for i in ('whv', 'bv', 'bh')]
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if w_hv.shape == self.w_hv.shape and b_v.shape == self.b_v.shape and b_h.shape == self.b_h.shape:
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self.w_hv = w_hv
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self.b_v = b_v
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self.b_h = b_h
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print(f"{filename} loaded successfully!")
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except FileNotFoundError:
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pass
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except KeyError:
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pass
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def save(self, filename: str):
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np.savez(filename, whv=self.w_hv, bv=self.b_v, bh=self.b_h)
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print(f"{filename} saved successfully!")
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