aboutsummaryrefslogtreecommitdiff
diff options
context:
space:
mode:
Diffstat
-rw-r--r--imago/engine/keras/convNeuralNetwork.py51
-rw-r--r--imago/engine/keras/denseNeuralNetwork.py28
-rw-r--r--imago/engine/keras/keras.py8
-rw-r--r--imago/engine/keras/neuralNetwork.py31
-rwxr-xr-xtestKeras.py7
5 files changed, 98 insertions, 27 deletions
diff --git a/imago/engine/keras/convNeuralNetwork.py b/imago/engine/keras/convNeuralNetwork.py
new file mode 100644
index 0000000..2534db6
--- /dev/null
+++ b/imago/engine/keras/convNeuralNetwork.py
@@ -0,0 +1,51 @@
+"""Convolutional neural network."""
+
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Conv2D, Conv2DTranspose, Reshape, Dense
+from tensorflow.keras.optimizers import Adam
+
+from imago.engine.keras.neuralNetwork import NeuralNetwork
+
+defaultModelFile = 'models/imagoConvKerasModel.h5'
+
+class ConvNeuralNetwork(NeuralNetwork):
+
+ def _initModel(self, boardSize=NeuralNetwork.DEF_BOARD_SIZE):
+ model = Sequential([
+ Conv2D(
+ filters=32,
+ kernel_size=(3),
+ activation='relu',
+ padding='same',
+ input_shape=(boardSize,boardSize,1)
+ ),
+ Conv2D(
+ filters=64,
+ kernel_size=(3),
+ activation='relu',
+ padding='same'
+ ),
+ #Conv2DTranspose(
+ # filters=64,
+ # kernel_size=(3),
+ # activation='relu',
+ # padding='same'
+ #),
+ Dense(
+ units=1,
+ activation='softmax'
+ ),
+ Reshape(
+ (boardSize,boardSize)
+ ),
+ ])
+
+ model.summary()
+
+ model.compile(
+ optimizer=Adam(learning_rate=0.0001),
+ loss='categorical_crossentropy',
+ metrics=['accuracy']
+ )
+
+ return model
diff --git a/imago/engine/keras/denseNeuralNetwork.py b/imago/engine/keras/denseNeuralNetwork.py
new file mode 100644
index 0000000..ff2efa8
--- /dev/null
+++ b/imago/engine/keras/denseNeuralNetwork.py
@@ -0,0 +1,28 @@
+"""Dense neural network."""
+
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense
+from tensorflow.keras.optimizers import Adam
+
+from imago.engine.keras.neuralNetwork import NeuralNetwork
+
+defaultModelFile = 'models/imagoDenseKerasModel.h5'
+
+class DenseNeuralNetwork(NeuralNetwork):
+
+ def _initModel(self, boardSize=NeuralNetwork.DEF_BOARD_SIZE):
+ model = Sequential([
+ Dense(units=32, activation='sigmoid', input_shape=(boardSize,boardSize)),
+ Dense(units=64, activation='relu'),
+ Dense(units=boardSize, activation='softmax')
+ ])
+
+ model.summary()
+
+ model.compile(
+ optimizer=Adam(learning_rate=0.0001),
+ loss='categorical_crossentropy',
+ metrics=['accuracy']
+ )
+
+ return model
diff --git a/imago/engine/keras/keras.py b/imago/engine/keras/keras.py
index a6aa913..80b6647 100644
--- a/imago/engine/keras/keras.py
+++ b/imago/engine/keras/keras.py
@@ -3,16 +3,18 @@
from imago.gameLogic.gameMove import GameMove
from imago.gameLogic.gameBoard import GameBoard
from imago.engine.decisionAlgorithm import DecisionAlgorithm
-from imago.engine.keras.neuralNetwork import NeuralNetwork
+from imago.engine.keras.denseNeuralNetwork import DenseNeuralNetwork
+from imago.engine.keras.convNeuralNetwork import ConvNeuralNetwork
-MODEL_FILE = "models/testModel.h5"
+MODEL_FILE = "" # Use network's default model file
class Keras(DecisionAlgorithm):
def __init__(self, move):
self.currentMove = move
self.boardSize = move.board.getBoardHeight()
- self.nn = NeuralNetwork(MODEL_FILE, self.boardSize)
+ #self.nn = NeuralNetwork(MODEL_FILE, self.boardSize)
+ self.nn = ConvNeuralNetwork(MODEL_FILE, self.boardSize)
def forceNextMove(self, coords):
"""Selects given move as next move."""
diff --git a/imago/engine/keras/neuralNetwork.py b/imago/engine/keras/neuralNetwork.py
index 1ec0efd..a52a3af 100644
--- a/imago/engine/keras/neuralNetwork.py
+++ b/imago/engine/keras/neuralNetwork.py
@@ -6,18 +6,18 @@ import os.path
import numpy
from matplotlib import pyplot
-from tensorflow.keras.models import Sequential, load_model
-from tensorflow.keras.layers import Dense
-from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.models import load_model
from imago.data.enums import Player
defaultModelFile = 'models/imagoKerasModel.h5'
-DEF_BOARD_SIZE = 9
-PLAYER_ID = 1
-OPPONENT_ID = -1
class NeuralNetwork:
+
+ DEF_BOARD_SIZE = 9
+ PLAYER_ID = 1
+ OPPONENT_ID = -1
+
def __init__(self, modelPath="", boardSize=DEF_BOARD_SIZE):
self.boardSize = boardSize
self.path = defaultModelFile
@@ -29,21 +29,7 @@ class NeuralNetwork:
self.model = self._initModel(boardSize)
def _initModel(self, boardSize=DEF_BOARD_SIZE):
- model = Sequential([
- Dense(units=16, activation='relu', input_shape=(boardSize,boardSize)),
- Dense(units=32, activation='relu'),
- Dense(units=boardSize, activation='softmax')
- ])
-
- model.summary()
-
- model.compile(
- optimizer=Adam(learning_rate=0.0001),
- loss='categorical_crossentropy',
- metrics=['accuracy']
- )
-
- return model
+ raise NotImplementedError("Tried to directly use NeuralNetwork class. Use one of the subclasses instead.")
def trainModel(self, games):
trainMoves = []
@@ -70,7 +56,8 @@ class NeuralNetwork:
if os.path.isfile(modelPath):
return load_model(modelPath)
else:
- raise FileNotFoundError("Keras neural network file not found at %s" % modelPath)
+ raise FileNotFoundError("Keras neural network model file not found at %s"
+ % modelPath)
def saveModel(self, modelPath=""):
if modelPath != "":
diff --git a/testKeras.py b/testKeras.py
index 3b0b3f5..0f25b21 100755
--- a/testKeras.py
+++ b/testKeras.py
@@ -6,6 +6,7 @@ import sys
from imago.sgfParser.sgf import loadGameTree
from imago.engine.keras.neuralNetwork import NeuralNetwork
+from imago.engine.keras.convNeuralNetwork import ConvNeuralNetwork
def main():
games = []
@@ -15,8 +16,10 @@ def main():
matches = [game.getMainLineOfPlay() for game in games]
- modelFile = "models/testModel.h5"
- nn = NeuralNetwork(modelFile, 9)
+ modelFile = ""
+ boardsize = 9
+ nn = NeuralNetwork(modelFile, boardsize)
+ #nn = ConvNeuralNetwork(modelFile, boardsize)
nn.trainModel(matches)
nn.saveModel()
generated by cgit v1.2.1 (git 2.18.0) at 2024-09-19 16:30:07 +0000