- added cifar_test as jupiter lab

- started torch version of RBM
- enable CUDA in matrix

src/rbm/matrix.py

@@ -1,6 +1,6 @@
 from typing import TypeAlias
 
-USE_CUDA = 0
+USE_CUDA = 1
 if USE_CUDA:
 	import cupy as np
 	Mat: TypeAlias = np.array

src/rbm/torch.py

@@ -0,0 +1,42 @@
+from rbm.entity import Entity
+from rbm.train import cd_binary_binary, cd_binary_gaussian, cd_gaussian_binary, cd_gaussian_gaussian
+from rbm.matrix import Mat, rms_error_accu, np
+
+class LossFunction:
+	def __init__(self, entity_type: Entity.Type):
+		self.func = None
+		if entity_type == Entity.Type.GB_RBM:
+			self.func = cd_gaussian_binary
+		if entity_type == Entity.Type.BB_RBM:
+			self.func = cd_binary_binary
+		if entity_type == Entity.Type.GG_RBM:
+			self.func = cd_gaussian_gaussian
+		if entity_type == Entity.Type.BG_RBM:
+			self.func = cd_binary_gaussian
+
+	def __call__(self, entity: Entity, data: Mat):
+		# Contrastive divergence learning: calculate gradients
+		dwhv, dbv, dbh = self.func(entity, data)
+
+		return dwhv, dbv, dbh
+
+class Optimizer:
+	def __init__(self, entity: Entity):
+		self.loss = LossFunction(entity.type)
+		self.entity = entity
+
+	def zero_grad(self):
+		self.entity.grad_zero()
+
+	def step(self, data: Mat):
+		params = self.entity.training_params
+
+		# Call loss function
+		dwhv, dbv, dbh = self.loss(self.entity, data)
+
+		# Adjust weight and biases
+		grad = self.entity.grad_compute(dbv, dbh, dwhv, learning_rate=params.learning_rate / data.shape[0],
+								   momentum=params.momentum, weight_decay=params.weight_decay)
+		# Adjust weights
+		self.entity.state_adjust(grad)
+