AlphaZero version 3¶
This model is based on AlphaZero version 2 and trained further on 1.000.000 training examples from the StageOne dataset on a 5x4 board.
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import sys
sys.path.append('..')
import numpy as np
import tensorflow as tf
from tensorflow.python import debug as tf_debug
from keras.callbacks import *
from keras.models import *
from keras.layers import *
from keras.optimizers import *
from keras.utils.np_utils import to_categorical
from keras.utils import plot_model
import keras.backend as K
from keras.regularizers import l2
from keras.engine.topology import Layer
from PIL import Image
from matplotlib.pyplot import imshow
%matplotlib inline
import random
import LineFilterLayer
baseModelPath = 'model/alphaZeroV2.h5'
modelPath = 'model/alphaZeroV3.h5'
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print(K.image_data_format())
# expected output: channels_last
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def dotsAndBoxesToCategorical(inputData):
inp = np.copy(inputData)
inp[inp == 255] = 1 # Line - comes first so that target data only has two categories
inp[inp == 65] = 2 # Box A
inp[inp == 150] = 3 # Box B
inp[inp == 215] = 4 # Dot
cat = to_categorical(inp)
newShape = inp.shape + (cat.shape[-1],)
return cat.reshape(newShape)
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rawDataset = np.load('stageOne5x4.npz')
x_train = rawDataset['x_train']
y_train = rawDataset['y_train']
x_train_cat = dotsAndBoxesToCategorical(x_train)
y_train_cat = dotsAndBoxesToCategorical(y_train)
np.set_printoptions(precision=2)
print("original data:")
print(x_train[0])
print(y_train[0])
print(x_train.shape)
print(y_train.shape)
print("\nnormalized data:")
print(np.transpose(x_train_cat[0]))
print(np.transpose(y_train_cat[0]))
print(x_train_cat.shape)
print(y_train_cat.shape)
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LineFilterLayer.LineFilterLayer.imgWidth = x_train.shape[-1]
LineFilterLayer.LineFilterLayer.imgHeight = x_train.shape[-2]
model = load_model(baseModelPath, custom_objects={'LineFilterLayer':LineFilterLayer.LineFilterLayer})
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def imgSizeToBoxes(x):
return (x-3)/2
def lineFilterMatrixNP(imgWidth,imgHeight):
boxWidth = imgSizeToBoxes(imgWidth)
boxHeight = imgSizeToBoxes(imgHeight)
linesCnt = 2*boxWidth*boxHeight+boxWidth+boxHeight
mat = np.zeros((imgHeight, imgWidth), dtype=np.bool)
for idx in range(linesCnt):
y1 = idx / ((2*boxWidth) + 1)
if idx % ((2*boxWidth) + 1) < boxWidth:
# horizontal line
x1 = idx % ((2*boxWidth) + 1)
x2 = x1 + 1
y2 = y1
else:
# vertical line
x1 = idx % ((2*boxWidth) + 1) - boxWidth
x2 = x1
y2 = y1 + 1
px = x2 * 2 + y2 - y1
py = y2 * 2 + x2 - x1
mat[py,px] = 1
return mat
lineFilterMatrixNP(x_train.shape[-1],x_train.shape[-2])
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y_train_lines = y_train[:,lineFilterMatrixNP(y_train.shape[-1], y_train.shape[-2])]
print(y_train_lines.shape)
print(y_train_lines[0])
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model.summary()
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#sess = K.get_session()
#sess = tf_debug.LocalCLIDebugWrapperSession(sess)
#K.set_session(sess)
# Training
callbacks = []
checkpoint = ModelCheckpoint(filepath=modelPath+".checkpoint", save_weights_only=False)
callbacks.append(checkpoint)
progbar = ProgbarLogger()
callbacks.append(progbar)
tensorboard = TensorBoard(log_dir='model/log3', write_grads=True, write_graph=True, write_images=True, histogram_freq=1)
#callbacks.append(tensorboard)
model.fit(x_train_cat, y_train_lines, epochs=16, batch_size=64, callbacks=callbacks, validation_split=0.001)
model.save(modelPath)
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model.save(modelPath)
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def linesToDotsAndBoxesImage(lines, imgWidth, imgHeight):
boxWidth = imgSizeToBoxes(imgWidth)
boxHeight = imgSizeToBoxes(imgHeight)
linesCnt = 2*boxWidth*boxHeight+boxWidth+boxHeight
mat = np.zeros((imgHeight, imgWidth), dtype=lines.dtype)
for idx in range(linesCnt):
y1 = idx / ((2*boxWidth) + 1)
if idx % ((2*boxWidth) + 1) < boxWidth:
# horizontal line
x1 = idx % ((2*boxWidth) + 1)
x2 = x1 + 1
y2 = y1
else:
# vertical line
x1 = idx % ((2*boxWidth) + 1) - boxWidth
x2 = x1
y2 = y1 + 1
px = x2 * 2 + y2 - y1
py = y2 * 2 + x2 - x1
mat[py,px] = lines[idx]
return mat
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example = random.randrange(x_train.shape[0])
print("example: "+str(example))
input_data = x_train[example:example+1]
input_data_cat = x_train_cat[example:example+1]
prediction_lines = model.predict(input_data_cat)
prediction_lines_print = prediction_lines * 100
print(prediction_lines_print.astype(np.uint8))
print(np.sum(prediction_lines))
prediction = linesToDotsAndBoxesImage(prediction_lines[0], x_train.shape[2], x_train.shape[1])
# print input data
input_data_print = x_train[example,:,:]
input_data_print = input_data_print.astype(np.uint8)
print("input "+str(input_data_print.shape)+": ")
print(input_data_print)
# generate greyscale image data from input data
target_imgdata = x_train[example,:,:]
target_imgdata = target_imgdata.astype(np.uint8)
# print prediction
prediction_data_print = prediction * 100
prediction_data_print = prediction_data_print.astype(np.uint8)
print("prediction: ")
print(prediction_data_print)
# generate greyscale image data from prediction data
prediction_imgdata = prediction * 255
prediction_imgdata = prediction_imgdata.astype(np.uint8)
# merge image data in color channels
tmp = np.zeros((prediction.shape[0], prediction.shape[1]), dtype=np.uint8)
merged_imgdata = np.stack([target_imgdata, prediction_imgdata, tmp], axis=2)
#create image
img = Image.fromarray(merged_imgdata, 'RGB')
img = img.resize(size=(img.size[0]*10, img.size[1]*10))
img
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