AlphaZero version 11¶
This AlphaZero version was trained from scratch on 1.000.000 training examples from the StageFourNoMCTS dataset on various board sizes.
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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.initializers 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 gc
from LineFilterLayer import LineFilterLayer
from ValueLayer import ValueLayer
modelPath = 'model/alphaZeroV11.h5'
datasetPath = 'StageFour-AlphaZeroV7-noMCTS-1000000-4x3-23:37-31_05_2018.npz'
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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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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
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def loadPVDataset(datasetPath):
rawDataset = np.load(datasetPath)
x_input = rawDataset['input']
y_policy = rawDataset['policy']
y_value = rawDataset['value']
x_input = dotsAndBoxesToCategorical(x_input)
y_policy = y_policy[:,lineFilterMatrixNP(y_policy.shape[-1], y_policy.shape[-2])]
y_policy /= 255
return (x_input, y_policy, y_value)
np.set_printoptions(precision=2)
(x_input, y_policy, y_value) = loadPVDataset(datasetPath)
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print(x_input.shape)
print(y_policy.shape)
print(y_value.shape)
print("input:")
print(x_input[0,::,::,1])
print("policy:")
print(y_policy[0])
print('value:')
print(y_value[0])
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kernelSize = (5,5)
filterCnt = 128
l2reg = 1e-4
resBlockCnt = 8
imgWidth = x_input.shape[-2]
imgHeight = x_input.shape[-3]
def build_residual_block(x, index):
in_x = x
res_name = "res"+str(index)
x = Conv2D(filters=filterCnt, kernel_size=kernelSize, padding="same",
data_format="channels_last", kernel_regularizer=l2(l2reg),
name=res_name+"_conv1_"+str(filterCnt))(x)
x = BatchNormalization(name=res_name+"_batchnorm1")(x)
x = Activation("relu",name=res_name+"_relu1")(x)
x = Conv2D(filters=filterCnt, kernel_size=kernelSize, padding="same",
data_format="channels_last", kernel_regularizer=l2(l2reg),
name=res_name+"_conv2-"+str(filterCnt))(x)
x = BatchNormalization(name="res"+str(index)+"_batchnorm2")(x)
x = Add(name=res_name+"_add")([in_x, x])
x = Activation("relu", name=res_name+"_relu2")(x)
return x
img_input = Input(shape=(None,None,5,))
x = Conv2D(filterCnt, kernelSize, padding='same', kernel_regularizer=l2(l2reg), name="input_conv")(img_input)
x = Activation("relu", name="input_relu")(x)
x = BatchNormalization()(x)
for i in range(resBlockCnt):
x = build_residual_block(x, i+1)
res_out = x
# policy output
x = Conv2D(1, kernelSize, padding='same', kernel_regularizer=l2(l2reg), name="policy_conv")(x)
x = LineFilterLayer(imgWidth, imgHeight)(x)
x = Activation("softmax", name="policy")(x)
policy_output = x
# value output
x = Conv2D(1, kernelSize, padding='same', kernel_regularizer=l2(l2reg), name="value_conv")(res_out)
#x = Flatten()(x)
#x = Dense(1, trainable=False, kernel_initializer=Constant(1.0/(imgWidth*imgHeight)), use_bias=False, name="value_dense")(x)
x = ValueLayer(imgWidth, imgHeight)(x)
x = Activation("tanh", name="value")(x)
value_output = x
model = Model(inputs=img_input, outputs=[policy_output, value_output])
model.compile(optimizer='adam', loss=['categorical_crossentropy', 'mean_squared_error'])
#for layer in model.layers:
# print("{:30}: {}".format(layer.name, layer.output_shape))
# if layer.name is 'value_dense':
# print(layer.kernel)
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/log2', write_grads=True, write_graph=True, write_images=True, histogram_freq=1)
#callbacks.append(tensorboard)
model.fit(x_input, [y_policy, y_value], epochs=2, 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_input.shape[0])
print("example: "+str(example))
input_data = x_input[example:example+1]
(prediction_lines, prediction_value) = model.predict(input_data)
prediction_lines_print = prediction_lines * 100
print(prediction_lines_print.astype(np.uint8))
print(np.sum(prediction_lines))
prediction = linesToDotsAndBoxesImage(prediction_lines[0], imgWidth, imgHeight)
# print input data
input_data_print = x_input[example,:,:,1]
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
planes = [1,2,3,4]
input_imgdata = np.sum(x_input[example,:,:,1:], axis=-1) * 255
input_imgdata = input_imgdata.astype(np.uint8)
# print prediction
prediction_data_print = prediction * 100
prediction_data_print = prediction_data_print.astype(np.uint8)
print("prediction policy: ")
print(prediction_data_print)
print("prediction value: ")
print(prediction_value)
print("target value: ")
print(y_value[example])
# generate greyscale image data from prediction data
prediction_imgdata = prediction * 255
prediction_imgdata = prediction_imgdata.astype(np.uint8)
# generate greyscale image of target data
target_imgdata = linesToDotsAndBoxesImage(y_policy[example], imgWidth, imgHeight) * 255
# merge image data in color channels
merged_imgdata = np.stack([input_imgdata, prediction_imgdata, target_imgdata], 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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