Re-add the final version.

Author: bsamseth

Diff for: tictacnet.py

@@ -10,4 +10,57 @@ def move_accuracy(y_test, y_pred):
     """A predicted move is correct if the largest output is 1 in the test vector."""
     return np.mean(y_test[y_pred == np.max(y_pred, axis=1, keepdims=True)])
 
+
 np.random.seed(1234)
+
+df = pd.read_csv("tictactoe-data.csv")
+print("Scores:", Counter(df["score"]))
+
+# Input is all the board features (2x9 squares) plus the turn.
+X = df.iloc[:, list(range(18)) + [-2]]
+
+# Target variables are the possible move squares.
+moves = df.iloc[:, list(range(18, 27))]
+# To predict score instead, use this as the target:
+# score = pd.get_dummies(df['score'])
+
+X_train, X_test, y_train, y_test = train_test_split(X, moves, test_size=0.2)
+
+print("Train/test shapes:", X_train.shape, X_test.shape, y_train.shape, y_test.shape)
+
+model = tf.keras.Sequential()
+model.add(tf.keras.layers.Dense(128, activation="relu", input_dim=X.shape[1]))
+model.add(tf.keras.layers.Dropout(0.3))
+model.add(tf.keras.layers.Dense(64, activation="relu"))
+model.add(tf.keras.layers.Dropout(0.3))
+model.add(tf.keras.layers.Dense(32, activation="relu"))
+model.add(tf.keras.layers.Dropout(0.3))
+model.add(tf.keras.layers.Dense(moves.shape[1], activation="softmax"))
+
+# For a multi-class classification problem
+model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"])
+
+print(model.summary())
+
+# This is not needed, but lets you view a lot of useful information using
+# > tensorboard --logdir logs
+# at your terminal prompt.
+tensorboard_callback = tf.keras.callbacks.TensorBoard(
+    log_dir="./logs",
+    histogram_freq=1,
+    batch_size=100,
+    write_graph=True,
+    write_grads=True,
+    write_images=True,
+)
+model.fit(
+    X_train,
+    y_train,
+    epochs=100,
+    batch_size=16,
+    validation_data=[X_test, y_test],
+    callbacks=[tensorboard_callback],
+)
+
+print("accuracy:", model.evaluate(X_test, y_test))
+print("Custom accuracy:", move_accuracy(y_test.values, model.predict(X_test)))