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getconvolutional.py

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+#!/usr/bin/python
+# -*- coding: utf-8 -*- 
+
+__author__ = "Aleksey Lobanov"
+__copyright__ = "Copyright 2016, Aleksey Lobanov"
+__credits__ = ["Aleksey Lobanov"]
+__license__ = "MIT"
+__maintainer__ = "Aleksey Lobanov"
+__email__ = "i@likemath.ru"
+
+from datetime import datetime
+
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Activation, Flatten
+from keras.layers import Convolution2D
+
+import numpy as np
+
+from sklearn.cross_validation import train_test_split
+
+from neurallife import generateData, getAccuracies, saveKeras
+
+
+def getModel(n):
+    nn = Sequential()
+    nn.add(Convolution2D(64, 3, 3, border_mode='same', input_shape=(1, n, n)))
+    nn.add(Activation('relu'))
+    nn.add(Dropout(0.25))
+    nn.add(Flatten())
+    nn.add(Dense(4*n**2,init="normal", activation="sigmoid"))
+    nn.add(Dropout(0.15))
+    nn.add(Dense(n**2,init="normal", activation="sigmoid"))
+    nn.compile(loss="MSE", optimizer="nadam", metrics=[])
+    return nn
+
+N = 9  # board size
+
+if __name__ == "__main__":
+
+    X_train, X_test, Y_train, Y_test = train_test_split(
+        *generateData(N, 2*10**5),
+        test_size=0.5,
+        random_state=23
+    )
+    X_train = X_train.reshape((X_train.shape[0], 1, N, N))
+    X_test = X_test.reshape((X_test.shape[0], 1, N, N))
+    
+    nn = getModel(N)
+    
+    cur_time = datetime.now()
+    
+    nn.fit(X_train, Y_train, nb_epoch=20, shuffle=False, verbose=1)
+    
+    cellAcc, boardAcc = getAccuracies(nn, X_test, Y_test)
+    
+    print(("for board {}x{} with train size={} cell accuracy is {:.5f}%, " +
+    "board accuracy is {:.5f}% and delta with theoretical board accuracy " +
+    "is {:.8f}%  it takes {}").format(
+        N,
+        N,
+        X_train.shape[0],
+        100 * cellAcc,
+        100 * boardAcc,
+        100 * abs(boardAcc - cellAcc**(N**2)),
+        datetime.now() - cur_time
+    ))
+    saveKeras(nn, "models/convolutional_{}_{}".format(N, X_train.shape[0]))