add: NeuralSolver

NeuralSolver.py

@@ -0,0 +1,74 @@
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+
+
+class NeuralSolver:
+    def __init__(self, width, height, quotientX=False, quotientY=False):
+        self.Width = width
+        self.Height = height
+        self.QuotientX = quotientX
+        self.QuotientY = quotientY
+        self._build_forward_model()
+        self._build_reverse_model()
+
+    def _build_forward_model(self):
+        inputs = keras.Input(shape=(self.Width, self.Height, 1), name="InitialState")
+        hidden = keras.Conv2D(32, 3, padding="same", activation="relu")(inputs)
+        hidden = keras.layers.Conv2D(32, 3, padding="same", activation="relu")(hidden)
+        outputs = keras.Conv2D(1, 1, padding="same", activation="sigmoid")(hidden)
+        self.ForwardModel = keras.Model(inputs, outputs, name="ForwardModel")
+        self.ForwardModel.compile(
+            optimizer=keras.optimizers.Adam(learning_rate=0.001),
+            loss=keras.losses.BinaryCrossentropy(),
+            metrics=["accuracy"],
+        )
+
+    def _build_reverse_model(self):
+        inputs = keras.Input(shape=(self.Width, self.Height, 1), name="FinalState")
+        hidden = keras.Conv2D(32, 3, padding="same", activation="relu")(inputs)
+        hidden = keras.layers.Conv2D(32, 3, padding="same", activation="relu")(hidden)
+        outputs = keras.Conv2D(1, 1, padding="same", activation="sigmoid")(hidden)
+        self.ReverseModel = keras.Model(inputs, outputs, name="ReverseModel")
+
+
+    def train_forward(self, dataset, batch_size=8, epochs=10):
+        x, y = dataset
+        self.ForwardModel.fit(
+            x=x,
+            y=y,
+            batch_size=batch_size,
+            epochs=epochs,
+            verbose=1
+        )
+
+    def train_backward(self, dataset, batch_size=8, epochs=10):
+        x, y = dataset
+        reverse_inputs = self.ReverseModel.inputs
+        reverse_outputs = self.ReverseModel.outputs
+        forward_outputs = self.ForwardModel(reverse_outputs)
+        composite_model = keras.Model(reverse_inputs, forward_outputs, name="CompositeModel")
+        composite_model.compile(
+            optimizer=keras.optimizers.Adam(learning_rate=0.001),
+            loss=keras.losses.BinaryCrossentropy(),
+            metrics=["accuracy"]
+        )
+        composite_model.fit(
+            x = y,
+            y = y,
+            batch_size=batch_size,
+            epochs=epochs,
+            verbose=1
+        )
+
+    def predict_forward(self, b):
+        if len(b.shape) == 2:
+            b = b[None, ..., None]
+        preds = self.ForwardModel.predict(b)
+        return preds > 0.5
+
+    def predict_reverse(self, b):
+        if len(b.shape) == 2:
+            b = b[None, ..., None]
+        preds = self.ReverseModel.predict(b)
+        return preds > 0.5

requirements.txt

@@ -0,0 +1,2 @@
+numpy~=2.0.2
+tensorflow~=2.18.0