diff --git a/bigneurallife.py b/bigneurallife.py
new file mode 100644
index 0000000..a9c80af
--- /dev/null
+++ b/bigneurallife.py
@@ -0,0 +1,178 @@
+#!/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"
+
+import sys
+from copy import deepcopy
+from datetime import datetime
+import logging
+
+import numpy as np
+
+from sklearn.cross_validation import train_test_split
+
+import keras
+from keras.models import Sequential
+from keras.layers import Dense, Dropout
+
+import matplotlib.pyplot as plt
+import matplotlib.cm as cm
+import matplotlib.patches as mpatches
+
+
+def initLogging():
+ logger = logging.getLogger()
+ logger.setLevel(logging.DEBUG)
+
+ formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
+ fh = logging.FileHandler('neurallife.log')
+ fh.setLevel(logging.DEBUG)
+ fh.setFormatter(formatter)
+ logger.addHandler(fh)
+ ch = logging.StreamHandler()
+ ch.setLevel(logging.DEBUG)
+ ch.setFormatter(formatter)
+ logger.addHandler(ch)
+
+
+def neighbors(field, i, j, fsize):
+ nsum = 0
+ for l in range(1, 10):
+ x = i - 1 + (l - 1) // 3
+ y = j - 1 + (l + 2) % 3
+ if -1 < x < fsize and -1 < y < fsize and field[x][y] == 1:
+ nsum += 1
+ nsum -= field[i][j]
+ return nsum
+
+
+def nextGen(field, fsize):
+ tmp_field = deepcopy(field)
+ for i in range(fsize):
+ for j in range(fsize):
+ neighb = neighbors(tmp_field, i, j, fsize)
+ if field[i][j] == 1 and not (2 <= neighb <= 3):
+ field[i][j] = 0
+ elif field[i][j] == 0 and neighb == 3:
+ field[i][j] = 1
+
+
+def uniqueRows(data):
+ uniq = np.unique(data.view(data.dtype.descr * data.shape[1]))
+ return uniq.view(data.dtype).reshape(-1, data.shape[1])
+
+
+def generateData(board_size, count=10**5):
+ assert(2**(board_size**2) >= count)
+ X = np.random.randint(2, size=(int(count*1.2),board_size*board_size))
+ X = uniqueRows(X)[:count]
+ Y = []
+ for row in X:
+ tmp_list = row.reshape((board_size,board_size)).tolist()
+ nextGen(tmp_list, board_size)
+ Y.append(tmp_list)
+ return (X, np.asarray(Y).reshape(X.shape))
+
+
+def loadKeras(path):
+ model = keras.models.model_from_json(open(path + '.json').read())
+ model.load_weights(path + '.h5')
+ model.compile(loss='MSE', optimizer='nadam', metrics=[])
+
+ logging.debug("Keras model loaded from {}".format(path))
+ return model
+
+
+def saveKeras(model, path):
+ json_architecture = model.to_json()
+ json_path = path + '.json'
+ with open(json_path, 'w') as f:
+ f.write(json_architecture)
+ weights_path = path + '.h5'
+ model.save_weights(weights_path, overwrite=True)
+
+
+def getModel(n):
+ nn = Sequential()
+ nn.add(Dense(8*n**2, input_dim=n**2, init="normal", activation="sigmoid"))
+ nn.add(Dense(5*n**2,init="normal", activation="sigmoid"))
+ nn.add(Dense(n**2,init="normal", activation="sigmoid"))
+ nn.compile(loss="MSE", optimizer="nadam", metrics=[])
+ return nn
+
+
+def getAccuracies(model,x_test,y_test):
+ preds = model.predict(x_test)
+ preds = np.rint(preds).astype("int")
+ acc_square = 1.0 * (preds == y_test).sum() / y_test.size
+ acc_boards = 0
+ for pred, real in zip(preds, y_test):
+ if (pred != real).sum() == 0:
+ acc_boards += 1
+ acc_boards = 1.0 * acc_boards / y_test.shape[0]
+ return (acc_square, acc_boards)
+
+
+
+META_PARAMETERS = {
+ 9:[409600],
+}
+
+
+if __name__ == "__main__":
+ initLogging()
+
+ plt.title("Neural Life")
+
+ plt.xscale("log")
+
+ plt.xlabel("Train size")
+ plt.ylabel("Cell accuracy")
+
+ plt_patches = []
+ for meta_ind,N in enumerate(META_PARAMETERS):
+ points_x = []
+ points_y = []
+ for data_size in META_PARAMETERS[N]:
+ cur_time = datetime.now()
+
+ X_train, X_test, Y_train, Y_test = train_test_split(
+ *generateData(N, data_size), # X and Y
+ test_size=0.6,
+ random_state=23
+ )
+
+ train_size = X_train.shape[0]
+
+ nn = getModel(N)
+
+ nn.fit(X_train, Y_train, nb_epoch=40, shuffle=False, verbose=1)
+
+ cellAcc, boardAcc = getAccuracies(nn, X_test, Y_test)
+
+ points_x.append(train_size)
+ points_y.append(cellAcc)
+
+ logging.info(("BIG model: 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,
+ train_size,
+ 100 * cellAcc,
+ 100 * boardAcc,
+ 100 * abs(boardAcc - cellAcc**(N**2)),
+ datetime.now() - cur_time
+ ))
+ saveKeras(nn, "models/bigmodel_{}_{}".format(N,train_size))
+ plt.plot(points_x, points_y, "o", linestyle="-", color=cm.ocean(meta_ind/len(META_PARAMETERS)))
+ plt_patches.append(mpatches.Patch(color=cm.ocean(meta_ind/len(META_PARAMETERS)), label="N={}".format(N)))
+
+ plt.legend(handles=plt_patches)
+ plt.savefig("biggraphics.svg")
diff --git a/getconvolutional.py b/getconvolutional.py
new file mode 100644
index 0000000..78e9337
--- /dev/null
+++ b/getconvolutional.py
@@ -0,0 +1,67 @@
+#!/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]))
diff --git a/gifcreator.py b/gifcreator.py
new file mode 100644
index 0000000..d007110
--- /dev/null
+++ b/gifcreator.py
@@ -0,0 +1,157 @@
+#!/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"
+
+import sys
+from copy import deepcopy
+
+import numpy as np
+
+import keras
+from keras.models import Sequential
+from keras.layers import Dense
+
+import imageio # for gifs
+
+from PIL import Image, ImageDraw
+
+from neurallife import nextGen
+
+
+"""
+# from original article
+start_pos = [
+ [0,0,0,0,1,0,1,0,0],
+ [0,1,0,0,1,0,0,1,0],
+ [0,1,1,0,1,1,0,1,0],
+ [1,0,0,1,1,0,0,0,0],
+ [0,1,1,1,0,1,0,1,0],
+ [0,0,1,0,1,0,0,0,0],
+ [0,0,1,1,0,0,1,0,0],
+ [0,1,1,0,1,1,0,0,0],
+ [0,0,0,0,0,0,0,0,0]
+]
+"""
+
+# about 27 original positions
+start_pos = [
+ [0, 0, 0, 0, 0, 0, 0, 1, 0],
+ [0, 0, 0, 0, 0, 0, 1, 1, 0],
+ [0, 0, 1, 0, 0, 1, 1, 1, 0],
+ [1, 0, 1, 0, 1, 0, 0, 0, 0],
+ [1, 0, 1, 0, 1, 1, 0, 0, 0],
+ [0, 1, 0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 0, 0, 0, 1, 0, 0, 0],
+ [0, 0, 0, 0, 0, 1, 1, 0, 0]
+]
+
+
+"""
+Code for good, long start positions:
+
+def getBest(max_cnt):
+ cur_max = 0
+ cur_best = None
+ for i in range(max_cnt):
+ cur_field = np.random.randint(2, size=(9,9)).tolist()
+ cur_cnt = getCnt(cur_field)
+ if cur_cnt > cur_max:
+ cur_max = getCnt(cur_field, 200)
+ cur_best = cur_field
+ return (cur_best,cur_max)
+
+
+def getCnt(pos, max_cnt=100):
+ cnt = 0
+ ker_pred = pos
+ cur_pos = pos
+ while True:
+ cnt += 1
+ ker_pred = nn.predict(np.asarray(ker_pred).reshape((1,1,9,9)))
+ ker_pred = np.rint(ker_pred).astype("int").reshape((9,9)).tolist()
+
+ old_cur_pos = deepcopy(cur_pos)
+ next_gen(cur_pos, len(cur_pos))
+ if np.asarray(old_cur_pos).sum() == np.asarray(cur_pos).sum():
+ break
+ if ker_pred != cur_pos:
+ break
+ if cnt > max_cnt:
+ return 0
+ return cnt
+"""
+
+LINE_SIZE = 2
+SQUARE_SIZE = 18
+FRAME_COUNT = 30
+FRAME_DELAY = 0.3 # in seconds
+
+
+def loadKeras(path):
+ model = keras.models.model_from_json(open(path + '.json').read())
+ model.load_weights(path + '.h5')
+ model.compile(loss='MSE', optimizer='nadam', metrics=[ ])
+ return model
+
+
+def imageFromList(l):
+ global LINE_SIZE, SQUARE_SIZE
+ height = LINE_SIZE * (len(l) + 1) + SQUARE_SIZE * len(l) # =height
+ width = LINE_SIZE * (len(l[0]) + 1) + SQUARE_SIZE * len(l[0])
+ tmp_img = Image.new('RGB', (width, height), (0, 0, 0))
+ pil_draw = ImageDraw.Draw(tmp_img)
+ for y in range(len(l)):
+ for x in range(len(l[0])):
+ if l[y][x] == 0:
+ pil_draw.rectangle((
+ x * (LINE_SIZE + SQUARE_SIZE) + LINE_SIZE,
+ y * (LINE_SIZE + SQUARE_SIZE) + LINE_SIZE,
+ (x + 1) * (LINE_SIZE + SQUARE_SIZE)-1,
+ (y + 1) * (LINE_SIZE + SQUARE_SIZE)-1,
+ ), fill=(255, 255, 255))
+ return tmp_img
+
+N = 9 # board size
+
+if __name__ == '__main__':
+ nn = loadKeras(sys.argv[1])
+ nn_frames = []
+ real_frames = []
+ ker_pred = cur_pos = start_pos
+
+ for i in range(FRAME_COUNT):
+ #ker_pred = cur_pos
+
+ nn_frames.append(imageFromList(ker_pred))
+ ker_pred = nn.predict(np.asarray(ker_pred).reshape((1, N**2)))
+ ker_pred = np.rint(ker_pred).astype("int").reshape((N, N)).tolist()
+
+ real_frames.append(imageFromList(cur_pos))
+ old_pos = deepcopy(cur_pos)
+ nextGen(cur_pos, len(start_pos))
+
+ # because need some pause at end
+ #if cur_pos == old_pos:
+ # break
+ width, height = nn_frames[0].size
+
+ imageio.mimsave(
+ 'gif_neural.gif',
+ [np.asarray(img.getdata()).reshape((width, height, 3)) for img in nn_frames],
+ fps=1/FRAME_DELAY
+ )
+ imageio.mimsave(
+ 'gif_real.gif',
+ [np.asarray(img.getdata()).reshape((width, height, 3)) for img in real_frames],
+ fps=1/FRAME_DELAY
+ )
+
+
+
diff --git a/graphics.gif b/graphics.gif
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diff --git a/graphics.svg b/graphics.svg
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--- /dev/null
+++ b/graphics.svg
@@ -0,0 +1,2415 @@
+
+
+
+
diff --git a/images/gif_neural.gif b/images/gif_neural.gif
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diff --git a/images/graphics_mini.png b/images/graphics_mini.png
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diff --git a/images/graphicsboard.svg b/images/graphicsboard.svg
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--- /dev/null
+++ b/images/graphicsboard.svg
@@ -0,0 +1,2350 @@
+
+
+
+
diff --git a/images/graphicsboard_mini.png b/images/graphicsboard_mini.png
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diff --git a/models/bigmodel_9_163840.h5 b/models/bigmodel_9_163840.h5
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diff --git a/models/bigmodel_9_163840.json b/models/bigmodel_9_163840.json
new file mode 100644
index 0000000..a4f58a1
--- /dev/null
+++ b/models/bigmodel_9_163840.json
@@ -0,0 +1 @@
+{"keras_version": "1.0.5", "sample_weight_mode": null, "class_name": "Sequential", "optimizer": {"name": "Nadam", "beta_1": 0.8999999761581421, "beta_2": 0.9990000128746033, "lr": 0.0020000000949949026, "epsilon": 1e-08, "schedule_decay": 0.004}, "loss": "MSE", "config": [{"class_name": "Dense", "config": {"init": "normal", "b_constraint": null, "input_dim": 81, "trainable": true, "batch_input_shape": [null, 81], "W_regularizer": null, "b_regularizer": null, "W_constraint": null, "name": "dense_1", "activation": "sigmoid", "input_dtype": "float32", "output_dim": 648, "bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"init": "normal", "b_constraint": null, "input_dim": null, "trainable": true, "b_regularizer": null, "W_regularizer": null, "W_constraint": null, "name": "dense_2", "activation": "sigmoid", "output_dim": 405, "bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"init": "normal", "b_constraint": null, "input_dim": null, "trainable": true, "b_regularizer": null, "W_regularizer": null, "W_constraint": null, "name": "dense_3", "activation": "sigmoid", "output_dim": 81, "bias": true, "activity_regularizer": null}}]}
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diff --git a/models/convolution_8_51200.h5 b/models/convolution_8_51200.h5
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diff --git a/models/convolution_8_51200.json b/models/convolution_8_51200.json
new file mode 100644
index 0000000..18c93b6
--- /dev/null
+++ b/models/convolution_8_51200.json
@@ -0,0 +1 @@
+{"config": [{"class_name": "Convolution2D", "config": {"W_regularizer": null, "nb_row": 3, "name": "convolution2d_6", "input_dtype": "float32", "b_regularizer": null, "nb_col": 3, "trainable": true, "subsample": [1, 1], "bias": true, "W_constraint": null, "border_mode": "same", "activity_regularizer": null, "b_constraint": null, "activation": "linear", "batch_input_shape": [null, 1, 8, 8], "init": "glorot_uniform", "nb_filter": 40, "dim_ordering": "th"}}, {"class_name": "Activation", "config": {"activation": "relu", "trainable": true, "name": "activation_4"}}, {"class_name": "Dropout", "config": {"p": 0.25, "trainable": true, "name": "dropout_4"}}, {"class_name": "Flatten", "config": {"trainable": true, "name": "flatten_5"}}, {"class_name": "Dense", "config": {"input_dim": null, "W_regularizer": null, "output_dim": 256, "name": "dense_6", "b_regularizer": null, "trainable": true, "bias": true, "W_constraint": null, "init": "normal", "activity_regularizer": null, "activation": "sigmoid", "b_constraint": null}}, {"class_name": "Dense", "config": {"input_dim": null, "W_regularizer": null, "output_dim": 64, "name": "dense_7", "b_regularizer": null, "trainable": true, "bias": true, "W_constraint": null, "init": "normal", "activity_regularizer": null, "activation": "sigmoid", "b_constraint": null}}], "sample_weight_mode": null, "loss": "MSE", "class_name": "Sequential", "keras_version": "1.0.5", "optimizer": {"lr": 0.0020000000949949026, "name": "Nadam", "beta_1": 0.8999999761581421, "epsilon": 1e-08, "beta_2": 0.9990000128746033, "schedule_decay": 0.004}}
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diff --git a/models/convolutional_9_100000.h5 b/models/convolutional_9_100000.h5
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diff --git a/models/convolutional_9_100000.json b/models/convolutional_9_100000.json
new file mode 100644
index 0000000..33368f2
--- /dev/null
+++ b/models/convolutional_9_100000.json
@@ -0,0 +1 @@
+{"optimizer": {"epsilon": 1e-08, "beta_1": 0.8999999761581421, "lr": 0.0020000000949949026, "name": "Nadam", "schedule_decay": 0.004, "beta_2": 0.9990000128746033}, "class_name": "Sequential", "keras_version": "1.0.5", "loss": "MSE", "sample_weight_mode": null, "config": [{"class_name": "Convolution2D", "config": {"batch_input_shape": [null, 1, 9, 9], "bias": true, "init": "glorot_uniform", "nb_row": 3, "W_constraint": null, "name": "convolution2d_1", "b_constraint": null, "activity_regularizer": null, "b_regularizer": null, "activation": "linear", "nb_col": 3, "trainable": true, "nb_filter": 64, "W_regularizer": null, "subsample": [1, 1], "input_dtype": "float32", "border_mode": "same", "dim_ordering": "th"}}, {"class_name": "Activation", "config": {"activation": "relu", "name": "activation_1", "trainable": true}}, {"class_name": "Dropout", "config": {"name": "dropout_1", "p": 0.25, "trainable": true}}, {"class_name": "Flatten", "config": {"name": "flatten_1", "trainable": true}}, {"class_name": "Dense", "config": {"bias": true, "init": "normal", "b_regularizer": null, "name": "dense_1", "b_constraint": null, "activity_regularizer": null, "W_constraint": null, "activation": "sigmoid", "input_dim": null, "trainable": true, "W_regularizer": null, "output_dim": 324}}, {"class_name": "Dropout", "config": {"name": "dropout_2", "p": 0.15, "trainable": true}}, {"class_name": "Dense", "config": {"bias": true, "init": "normal", "b_regularizer": null, "name": "dense_2", "b_constraint": null, "activity_regularizer": null, "W_constraint": null, "activation": "sigmoid", "input_dim": null, "trainable": true, "W_regularizer": null, "output_dim": 81}}]}
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diff --git a/models/model_10_12800.h5 b/models/model_10_12800.h5
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diff --git a/models/model_10_12800.json b/models/model_10_12800.json
new file mode 100644
index 0000000..dab506e
--- /dev/null
+++ b/models/model_10_12800.json
@@ -0,0 +1 @@
+{"config": [{"class_name": "Dense", "config": {"name": "dense_43", "b_regularizer": null, "input_dtype": "float32", "init": "normal", "b_constraint": null, "W_regularizer": null, "input_dim": 100, "activation": "sigmoid", "W_constraint": null, "batch_input_shape": [null, 100], "bias": true, "activity_regularizer": null, "output_dim": 400, "trainable": true}}, {"class_name": "Dense", "config": {"name": "dense_44", "b_regularizer": null, "init": "normal", "b_constraint": null, "W_regularizer": null, "input_dim": null, "trainable": true, "W_constraint": null, "activation": "sigmoid", "bias": true, "activity_regularizer": null, "output_dim": 400}}, {"class_name": "Dense", "config": {"name": "dense_45", "b_regularizer": null, "init": "normal", "b_constraint": null, "W_regularizer": null, "input_dim": null, "trainable": true, "W_constraint": null, "activation": "sigmoid", "bias": true, "activity_regularizer": null, "output_dim": 100}}], "sample_weight_mode": null, "keras_version": "1.0.5", "optimizer": {"name": "Nadam", "epsilon": 1e-08, "beta_2": 0.9990000128746033, "schedule_decay": 0.004, "beta_1": 0.8999999761581421, "lr": 0.0020000000949949026}, "class_name": "Sequential", "loss": "MSE"}
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diff --git a/modelsCyclo/model_8_800.h5 b/modelsCyclo/model_8_800.h5
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+++ b/modelsCyclo/model_9_12800.json
@@ -0,0 +1 @@
+{"class_name": "Sequential", "sample_weight_mode": null, "keras_version": "1.0.5", "config": [{"class_name": "Dense", "config": {"output_dim": 405, "bias": true, "b_constraint": null, "activity_regularizer": null, "W_regularizer": null, "trainable": true, "init": "normal", "name": "dense_25", "batch_input_shape": [null, 81], "activation": "sigmoid", "b_regularizer": null, "input_dim": 81, "input_dtype": "float32", "W_constraint": null}}, {"class_name": "Dense", "config": {"output_dim": 405, "bias": true, "b_constraint": null, "activity_regularizer": null, "W_regularizer": null, "name": "dense_26", "init": "normal", "trainable": true, "activation": "sigmoid", "b_regularizer": null, "input_dim": null, "W_constraint": null}}, {"class_name": "Dense", "config": {"output_dim": 81, "bias": true, "b_constraint": null, "activity_regularizer": null, "W_regularizer": null, "name": "dense_27", "init": "normal", "trainable": true, "activation": "sigmoid", "b_regularizer": null, "input_dim": null, "W_constraint": null}}], "optimizer": {"epsilon": 1e-08, "name": "Nadam", "beta_2": 0.9990000128746033, "lr": 0.0020000000949949026, "beta_1": 0.8999999761581421, "schedule_decay": 0.004}, "loss": "MSE"}
\ No newline at end of file
diff --git a/modelsCyclo/model_9_204800.h5 b/modelsCyclo/model_9_204800.h5
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index 0000000..5c6d7d7
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diff --git a/modelsCyclo/model_9_204800.json b/modelsCyclo/model_9_204800.json
new file mode 100644
index 0000000..ac1431a
--- /dev/null
+++ b/modelsCyclo/model_9_204800.json
@@ -0,0 +1 @@
+{"class_name": "Sequential", "sample_weight_mode": null, "keras_version": "1.0.5", "config": [{"class_name": "Dense", "config": {"output_dim": 405, "bias": true, "b_constraint": null, "activity_regularizer": null, "W_regularizer": null, "trainable": true, "init": "normal", "name": "dense_31", "batch_input_shape": [null, 81], "activation": "sigmoid", "b_regularizer": null, "input_dim": 81, "input_dtype": "float32", "W_constraint": null}}, {"class_name": "Dense", "config": {"output_dim": 405, "bias": true, "b_constraint": null, "activity_regularizer": null, "W_regularizer": null, "name": "dense_32", "init": "normal", "trainable": true, "activation": "sigmoid", "b_regularizer": null, "input_dim": null, "W_constraint": null}}, {"class_name": "Dense", "config": {"output_dim": 81, "bias": true, "b_constraint": null, "activity_regularizer": null, "W_regularizer": null, "name": "dense_33", "init": "normal", "trainable": true, "activation": "sigmoid", "b_regularizer": null, "input_dim": null, "W_constraint": null}}], "optimizer": {"epsilon": 1e-08, "name": "Nadam", "beta_2": 0.9990000128746033, "lr": 0.0020000000949949026, "beta_1": 0.8999999761581421, "schedule_decay": 0.004}, "loss": "MSE"}
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diff --git a/modelsCyclo/model_9_3200.h5 b/modelsCyclo/model_9_3200.h5
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diff --git a/modelsCyclo/model_9_3200.json b/modelsCyclo/model_9_3200.json
new file mode 100644
index 0000000..615db0b
--- /dev/null
+++ b/modelsCyclo/model_9_3200.json
@@ -0,0 +1 @@
+{"class_name": "Sequential", "sample_weight_mode": null, "keras_version": "1.0.5", "config": [{"class_name": "Dense", "config": {"output_dim": 405, "bias": true, "b_constraint": null, "activity_regularizer": null, "W_regularizer": null, "trainable": true, "init": "normal", "name": "dense_22", "batch_input_shape": [null, 81], "activation": "sigmoid", "b_regularizer": null, "input_dim": 81, "input_dtype": "float32", "W_constraint": null}}, {"class_name": "Dense", "config": {"output_dim": 405, "bias": true, "b_constraint": null, "activity_regularizer": null, "W_regularizer": null, "name": "dense_23", "init": "normal", "trainable": true, "activation": "sigmoid", "b_regularizer": null, "input_dim": null, "W_constraint": null}}, {"class_name": "Dense", "config": {"output_dim": 81, "bias": true, "b_constraint": null, "activity_regularizer": null, "W_regularizer": null, "name": "dense_24", "init": "normal", "trainable": true, "activation": "sigmoid", "b_regularizer": null, "input_dim": null, "W_constraint": null}}], "optimizer": {"epsilon": 1e-08, "name": "Nadam", "beta_2": 0.9990000128746033, "lr": 0.0020000000949949026, "beta_1": 0.8999999761581421, "schedule_decay": 0.004}, "loss": "MSE"}
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diff --git a/modelsCyclo/model_9_51200.h5 b/modelsCyclo/model_9_51200.h5
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diff --git a/modelsCyclo/model_9_51200.json b/modelsCyclo/model_9_51200.json
new file mode 100644
index 0000000..6f87967
--- /dev/null
+++ b/modelsCyclo/model_9_51200.json
@@ -0,0 +1 @@
+{"class_name": "Sequential", "sample_weight_mode": null, "keras_version": "1.0.5", "config": [{"class_name": "Dense", "config": {"output_dim": 405, "bias": true, "b_constraint": null, "activity_regularizer": null, "W_regularizer": null, "trainable": true, "init": "normal", "name": "dense_28", "batch_input_shape": [null, 81], "activation": "sigmoid", "b_regularizer": null, "input_dim": 81, "input_dtype": "float32", "W_constraint": null}}, {"class_name": "Dense", "config": {"output_dim": 405, "bias": true, "b_constraint": null, "activity_regularizer": null, "W_regularizer": null, "name": "dense_29", "init": "normal", "trainable": true, "activation": "sigmoid", "b_regularizer": null, "input_dim": null, "W_constraint": null}}, {"class_name": "Dense", "config": {"output_dim": 81, "bias": true, "b_constraint": null, "activity_regularizer": null, "W_regularizer": null, "name": "dense_30", "init": "normal", "trainable": true, "activation": "sigmoid", "b_regularizer": null, "input_dim": null, "W_constraint": null}}], "optimizer": {"epsilon": 1e-08, "name": "Nadam", "beta_2": 0.9990000128746033, "lr": 0.0020000000949949026, "beta_1": 0.8999999761581421, "schedule_decay": 0.004}, "loss": "MSE"}
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diff --git a/modelsCyclo/model_9_800.h5 b/modelsCyclo/model_9_800.h5
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diff --git a/modelsCyclo/model_9_800.json b/modelsCyclo/model_9_800.json
new file mode 100644
index 0000000..fe8fa44
--- /dev/null
+++ b/modelsCyclo/model_9_800.json
@@ -0,0 +1 @@
+{"class_name": "Sequential", "sample_weight_mode": null, "keras_version": "1.0.5", "config": [{"class_name": "Dense", "config": {"output_dim": 405, "bias": true, "b_constraint": null, "activity_regularizer": null, "W_regularizer": null, "trainable": true, "init": "normal", "name": "dense_19", "batch_input_shape": [null, 81], "activation": "sigmoid", "b_regularizer": null, "input_dim": 81, "input_dtype": "float32", "W_constraint": null}}, {"class_name": "Dense", "config": {"output_dim": 405, "bias": true, "b_constraint": null, "activity_regularizer": null, "W_regularizer": null, "name": "dense_20", "init": "normal", "trainable": true, "activation": "sigmoid", "b_regularizer": null, "input_dim": null, "W_constraint": null}}, {"class_name": "Dense", "config": {"output_dim": 81, "bias": true, "b_constraint": null, "activity_regularizer": null, "W_regularizer": null, "name": "dense_21", "init": "normal", "trainable": true, "activation": "sigmoid", "b_regularizer": null, "input_dim": null, "W_constraint": null}}], "optimizer": {"epsilon": 1e-08, "name": "Nadam", "beta_2": 0.9990000128746033, "lr": 0.0020000000949949026, "beta_1": 0.8999999761581421, "schedule_decay": 0.004}, "loss": "MSE"}
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diff --git a/modelsCyclo/model_9_819200.h5 b/modelsCyclo/model_9_819200.h5
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diff --git a/modelsCyclo/model_9_819200.json b/modelsCyclo/model_9_819200.json
new file mode 100644
index 0000000..0bc0d5f
--- /dev/null
+++ b/modelsCyclo/model_9_819200.json
@@ -0,0 +1 @@
+{"class_name": "Sequential", "sample_weight_mode": null, "keras_version": "1.0.5", "config": [{"class_name": "Dense", "config": {"output_dim": 405, "bias": true, "b_constraint": null, "activity_regularizer": null, "W_regularizer": null, "trainable": true, "init": "normal", "name": "dense_34", "batch_input_shape": [null, 81], "activation": "sigmoid", "b_regularizer": null, "input_dim": 81, "input_dtype": "float32", "W_constraint": null}}, {"class_name": "Dense", "config": {"output_dim": 405, "bias": true, "b_constraint": null, "activity_regularizer": null, "W_regularizer": null, "name": "dense_35", "init": "normal", "trainable": true, "activation": "sigmoid", "b_regularizer": null, "input_dim": null, "W_constraint": null}}, {"class_name": "Dense", "config": {"output_dim": 81, "bias": true, "b_constraint": null, "activity_regularizer": null, "W_regularizer": null, "name": "dense_36", "init": "normal", "trainable": true, "activation": "sigmoid", "b_regularizer": null, "input_dim": null, "W_constraint": null}}], "optimizer": {"epsilon": 1e-08, "name": "Nadam", "beta_2": 0.9990000128746033, "lr": 0.0020000000949949026, "beta_1": 0.8999999761581421, "schedule_decay": 0.004}, "loss": "MSE"}
\ No newline at end of file
diff --git a/neurallife.py b/neurallife.py
new file mode 100644
index 0000000..df345a7
--- /dev/null
+++ b/neurallife.py
@@ -0,0 +1,194 @@
+#!/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"
+
+import sys
+from copy import deepcopy
+from datetime import datetime
+import logging
+
+import numpy as np
+
+from sklearn.cross_validation import train_test_split
+
+import keras
+from keras.models import Sequential
+from keras.layers import Dense, Dropout
+
+import matplotlib.pyplot as plt
+import matplotlib.cm as cm
+import matplotlib.patches as mpatches
+
+
+def initLogging():
+ logger = logging.getLogger()
+ logger.setLevel(logging.DEBUG)
+
+ formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
+ fh = logging.FileHandler('neurallife.log')
+ fh.setLevel(logging.DEBUG)
+ fh.setFormatter(formatter)
+ logger.addHandler(fh)
+ ch = logging.StreamHandler()
+ ch.setLevel(logging.DEBUG)
+ ch.setFormatter(formatter)
+ logger.addHandler(ch)
+
+
+def neighbors(field, i, j, fsize):
+ nsum = 0
+ for l in range(1, 10):
+ x = i - 1 + (l - 1) // 3
+ y = j - 1 + (l + 2) % 3
+ if -1 < x < fsize and -1 < y < fsize and field[x][y] == 1:
+ nsum += 1
+ nsum -= field[i][j]
+ return nsum
+
+
+def neighbors_cyclo(field, i, j, fsize):
+ nsum = 0
+ for l in range(9):
+ x = (i - 1 + l // 3) % fsize
+ y = (j - 1 + l % 3) % fsize
+ nsum += field[x][y]
+ nsum -= field[i][j]
+ return nsum
+
+
+def nextGen(field, fsize):
+ tmp_field = deepcopy(field)
+ for i in range(fsize):
+ for j in range(fsize):
+ neighb = neighbors(tmp_field, i, j, fsize)
+ if field[i][j] == 1 and not (2 <= neighb <= 3):
+ field[i][j] = 0
+ elif field[i][j] == 0 and neighb == 3:
+ field[i][j] = 1
+
+
+def uniqueRows(data):
+ uniq = np.unique(data.view(data.dtype.descr * data.shape[1]))
+ return uniq.view(data.dtype).reshape(-1, data.shape[1])
+
+
+def generateData(board_size, count=10**5):
+ assert(2**(board_size**2) >= count)
+ X = np.random.randint(2, size=(int(count*1.2),board_size*board_size))
+ X = uniqueRows(X)[:count]
+ Y = []
+ for row in X:
+ tmp_list = row.reshape((board_size,board_size)).tolist()
+ nextGen(tmp_list, board_size)
+ Y.append(tmp_list)
+ return (X, np.asarray(Y).reshape(X.shape))
+
+
+def loadKeras(path):
+ model = keras.models.model_from_json(open(path + '.json').read())
+ model.load_weights(path + '.h5')
+ model.compile(loss='MSE', optimizer='nadam', metrics=[])
+
+ logging.debug("Keras model loaded from {}".format(path))
+ return model
+
+
+def saveKeras(model, path):
+ json_architecture = model.to_json()
+ json_path = path + '.json'
+ with open(json_path, 'w') as f:
+ f.write(json_architecture)
+ weights_path = path + '.h5'
+ model.save_weights(weights_path, overwrite=True)
+
+
+def getModel(n):
+ nn = Sequential()
+ nn.add(Dense(5*n**2, input_dim=n**2, init="normal", activation="sigmoid"))
+ #nn.add(Dropout(0.2)) # model without dropout because need simple
+ 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
+
+
+def getAccuracies(model,x_test,y_test):
+ preds = model.predict(x_test)
+ preds = np.rint(preds).astype("int")
+ acc_square = 1.0 * (preds == y_test).sum() / y_test.size
+ acc_boards = 0
+ for pred, real in zip(preds, y_test):
+ if (pred != real).sum() == 0:
+ acc_boards += 1
+ acc_boards = 1.0 * acc_boards / y_test.shape[0]
+ return (acc_square, acc_boards)
+
+
+
+META_PARAMETERS = {
+ 5:[1600*4**i for i in range(6)],
+ 6:[1600*4**i for i in range(6)],
+ 8:[1600*4**i for i in range(6)],
+ 9:[1600*4**i for i in range(6)],
+ 10:[1600*4**i for i in range(6)],
+}
+
+
+if __name__ == "__main__":
+ initLogging()
+
+ plt.title("Neural Life")
+
+ plt.xscale("log")
+
+ plt.xlabel("Train size")
+ plt.ylabel("Cell accuracy")
+
+ plt_patches = []
+ for meta_ind,N in enumerate(sorted(META_PARAMETERS.keys())):
+ points_x = []
+ points_y = []
+ for data_size in META_PARAMETERS[N]:
+ cur_time = datetime.now()
+
+ X_train, X_test, Y_train, Y_test = train_test_split(
+ *generateData(N, data_size), # X and Y
+ test_size=0.6,
+ random_state=23
+ )
+
+ train_size = X_train.shape[0]
+
+ nn = getModel(N)
+
+ nn.fit(X_train, Y_train, nb_epoch=40, shuffle=False, verbose=0)
+
+ cellAcc, boardAcc = getAccuracies(nn, X_test, Y_test)
+
+ points_x.append(train_size)
+ points_y.append(cellAcc)
+
+ logging.info(("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,
+ train_size,
+ 100 * cellAcc,
+ 100 * boardAcc,
+ 100 * abs(boardAcc - cellAcc**(N**2)),
+ datetime.now() - cur_time
+ ))
+ saveKeras(nn, "models/model_{}_{}".format(N,train_size))
+ plt.plot(points_x, points_y, "o", linestyle="-", color=cm.ocean(meta_ind/len(META_PARAMETERS)))
+ plt_patches.append(mpatches.Patch(color=cm.ocean(meta_ind/len(META_PARAMETERS)), label="N={}".format(N)))
+
+ plt.legend(handles=plt_patches)
+ plt.savefig("graphics.svg")
diff --git a/plotboardacc.py b/plotboardacc.py
new file mode 100644
index 0000000..caacee8
--- /dev/null
+++ b/plotboardacc.py
@@ -0,0 +1,46 @@
+#!/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"
+
+import matplotlib.pyplot as plt
+import matplotlib.cm as cm
+import matplotlib.patches as mpatches
+
+from neurallife import loadKeras, generateData, getAccuracies, META_PARAMETERS
+
+
+if __name__ == "__main__":
+ plt.title("Neural Life")
+
+ plt.xscale("log")
+ plt.yscale("log")
+
+ plt.xlabel("Train size")
+ plt.ylabel("Board accuracy")
+
+ plt_patches = []
+ for meta_ind,N in enumerate(sorted(META_PARAMETERS.keys())):
+ points_x = []
+ points_y = []
+
+ X_test, Y_test = generateData(N, 100000)
+
+ for data_size in META_PARAMETERS[N]:
+ nn = loadKeras("models/model_{}_{}".format(N, int(data_size * 0.5)))
+
+ cellAcc, boardAcc = getAccuracies(nn, X_test, Y_test)
+
+ points_x.append(data_size * 0.5)
+ points_y.append(boardAcc)
+
+ plt.plot(points_x, points_y, "o", linestyle="-", color=cm.ocean(meta_ind/len(META_PARAMETERS)))
+ plt_patches.append(mpatches.Patch(color=cm.ocean(meta_ind/len(META_PARAMETERS)), label="N={}".format(N)))
+
+ plt.legend(handles=plt_patches, loc=2)
+ plt.savefig("graphics-board.svg")