Initial commit

src/rbm/layer.py

@@ -0,0 +1,50 @@
+import numpy as np
+from collections.abc import Callable
+from params import RbmParams
+from state import RbmState
+from helper import sample, prob, uniform
+
+class RbmLayer:
+	def __init__(self, name: str, num_visible, num_hidden, params: RbmParams):
+		self.name = name
+		self.state = RbmState.from_layer_params(num_visible, num_hidden)
+		self.params = params
+		self.state_filename = f"{self.name}_state.npz"
+
+	def save(self):
+		self.state.to_file(self.state_filename)
+
+	def load(self):
+		self.state = RbmState.from_file(self.state_filename)
+
+	def train_batch(self, v_states: np.ndarray, cd_func: Callable):
+		for epochs in range(self.params.num_epochs):
+			# Contrastive divergence learning: calculate gradients
+			cd_func(v_states, dwhv, dbh, dbv);
+
+
+
+if __name__ == "__main__":
+	p = RbmParams()
+	l1 = RbmLayer("Layer_0", 2, 3, p)
+	l1.save()
+	l1.load()
+	l1.state.init(mu=0.5, std=1.0)
+
+	s1 = RbmState(l1.state.w_hv, l1.state.b_v, l1.state.b_h)
+
+	v0 = uniform((1,2))
+	h0 = uniform((1,3))
+
+	h1 = l1.v_to_h(v0)
+	print(h1.shape)
+	v1 = l1.h_to_v(h1)
+	s_v1 = sample(v1)
+	p_h = prob(s_v1)
+
+	print(v1.shape)
+
+
+
+
+