minigo/oneoffs/inspect_game.py at v199 · caddliulei/minigo

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# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

# http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

import sys; sys.path.insert(0, '.')

import dual_net

import strategies

import sgf_wrapper

import numpy as np

import pdb

import random

import features

import symmetries

def initialize_game(sgf_file, load_file, move=1):

with open(sgf_file) as f:

sgf_contents = f.read()

iterator = sgf_wrapper.replay_sgf(sgf_contents)

for i in range(move):

position_w_context = next(iterator)

player = strategies.MCTSPlayerMixin(dual_net.DualNetwork(load_file))

player.initialize_game(position_w_context.position)

return player

def analyze_symmetries(sgf_file, load_file):

with open(sgf_file) as f:

sgf_contents = f.read()

iterator = sgf_wrapper.replay_sgf(sgf_contents)

net = dual_net.DualNetwork(load_file)

for i, pwc in enumerate(iterator):

if i < 200:

continue

feats = features.extract_features(pwc.position)

variants = [symmetries.apply_symmetry_feat(s, feats) for s in symmetries.SYMMETRIES]

values = net.sess.run(

net.inference_output['value_output'],

feed_dict={net.inference_input['pos_tensor']: variants})

mean = np.mean(values)

stdev = np.std(values)

all_vals = sorted(zip(values, symmetries.SYMMETRIES))

print("{:3d} {:.3f} +/- {:.3f} min {:.3f} {} max {:.3f} {}".format(

i, mean, stdev, *all_vals[0], *all_vals[-1]))

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