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# Copyright 2018 Google LLC
#
# 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.
"""
The policy and value networks share a majority of their architecture.
This helps the intermediate layers extract concepts that are relevant to both
move prediction and score estimation.
"""
import collections
import functools
import math
import numpy as np
import os.path
import itertools
import sys
import tensorflow as tf
from tqdm import tqdm
from typing import Dict
import features
import preprocessing
import symmetries
import go
# How many positions to look at per generation.
# Per AGZ, 2048 minibatch * 1k = 2M positions/generation
EXAMPLES_PER_GENERATION = 2000000
# How many positions can fit on a graphics card. 256 for 9s, 16 or 32 for 19s.
TRAIN_BATCH_SIZE = 256
class DualNetworkTrainer():
def __init__(self, save_file=None, logdir=None, **hparams):
self.hparams = get_default_hyperparams(**hparams)
self.save_file = save_file
self.logdir = logdir
self.sess = tf.Session(graph=tf.Graph())
def initialize_weights(self, init_from=None):
"""Initialize weights from model checkpoint.
If model checkpoint does not exist, fall back to init_from.
If that doesn't exist either, bootstrap with random weights.
This priority order prevents the mistake where the latest saved
model exists, but you accidentally init from an older model checkpoint
and then overwrite the newer model weights.
"""
tf.logging.set_verbosity(tf.logging.WARN) # Hide startup spam
if self.save_file is not None and os.path.exists(self.save_file + '.meta'):
tf.train.Saver().restore(self.sess, self.save_file)
return
if init_from is not None:
print("Initializing from {}".format(init_from), file=sys.stderr)
tf.train.Saver().restore(self.sess, init_from)
else:
print("Bootstrapping with random weights", file=sys.stderr)
self.sess.run(tf.global_variables_initializer())
tf.logging.set_verbosity(tf.logging.INFO)
def save_weights(self):
with self.sess.graph.as_default():
tf.train.Saver().save(self.sess, self.save_file)
def bootstrap(self):
'Create a save file with random initial weights.'
sess = tf.Session(graph=tf.Graph())
with sess.graph.as_default():
input_tensors = get_inference_input()
output_tensors = dual_net(input_tensors, TRAIN_BATCH_SIZE,
train_mode=True, **self.hparams)
train_tensors = train_ops(
input_tensors, output_tensors, **self.hparams)
sess.run(tf.global_variables_initializer())
tf.train.Saver().save(sess, self.save_file)
def train(self, tf_records, init_from=None, num_steps=None,
logging_freq=100, verbosity=1):
logdir = os.path.join(
self.logdir, 'train') if self.logdir is not None else None
def should_log(i):
return logdir is not None and i % logging_freq == 0
if num_steps is None:
num_steps = EXAMPLES_PER_GENERATION // TRAIN_BATCH_SIZE
with self.sess.graph.as_default():
input_tensors = preprocessing.get_input_tensors(
TRAIN_BATCH_SIZE, tf_records)
output_tensors = dual_net(input_tensors, TRAIN_BATCH_SIZE,
train_mode=True, **self.hparams)
train_tensors = train_ops(
input_tensors, output_tensors, **self.hparams)
weight_summary_op = logging_ops()
weight_tensors = tf.trainable_variables()
self.initialize_weights(init_from)
if logdir is not None:
training_stats = StatisticsCollector()
logger = tf.summary.FileWriter(logdir, self.sess.graph)
for i in tqdm(range(num_steps)):
if should_log(i):
before_weights = self.sess.run(weight_tensors)
try:
tensor_values = self.sess.run(train_tensors)
except tf.errors.OutOfRangeError:
break
if verbosity > 1 and i % logging_freq == 0:
print(tensor_values)
if logdir is not None:
training_stats.report(
{k: tensor_values[k] for k in (
'policy_cost', 'value_cost', 'l2_cost',
'combined_cost', 'policy_entropy')})
if should_log(i):
after_weights = self.sess.run(weight_tensors)
weight_update_summaries = compute_update_ratio(
weight_tensors, before_weights, after_weights)
accuracy_summaries = training_stats.collect()
weight_summaries = self.sess.run(weight_summary_op)
global_step = tensor_values['global_step']
logger.add_summary(weight_update_summaries, global_step)
logger.add_summary(accuracy_summaries, global_step)
logger.add_summary(weight_summaries, global_step)
self.save_weights()
def validate(self, tf_records, batch_size=128, init_from=None, num_steps=1000):
"""Compute only the error terms for a set of tf_records, ideally a
holdout set, and report them to an 'test' subdirectory of the logs.
"""
cost_tensor_names = ['policy_cost', 'value_cost', 'l2_cost',
'combined_cost']
if self.logdir is None:
print("Error, trainer not initialized with a logdir.", file=sys.stderr)
return
logdir = os.path.join(self.logdir, 'test')
with self.sess.graph.as_default():
input_tensors = preprocessing.get_input_tensors(
batch_size, tf_records, shuffle_buffer_size=1000, filter_amount=0.05)
output_tensors = dual_net(input_tensors, TRAIN_BATCH_SIZE,
train_mode=False, **self.hparams)
train_tensors = train_ops(
input_tensors, output_tensors, **self.hparams)
# just run our cost tensors
validate_tensors = {k: train_tensors[k]
for k in cost_tensor_names}
8000
self.initialize_weights(init_from)
training_stats = StatisticsCollector()
logger = tf.summary.FileWriter(logdir, None) # No graph needed.
for i in tqdm(range(num_steps)):
try:
tensor_values = self.sess.run(validate_tensors)
except tf.errors.OutOfRangeError:
break
training_stats.report(tensor_values)
accuracy_summaries = training_stats.collect()
global_step = self.sess.run(train_tensors['global_step'])
logger.add_summary(accuracy_summaries, global_step)
logger.flush()
print(accuracy_summaries)
class DualNetwork():
def __init__(self, save_file, **hparams):
self.save_file = save_file
self.hparams = get_default_hyperparams(**hparams)
self.inference_input = None
self.inference_output = None
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(graph=tf.Graph(), config=config)
self.initialize_graph()
def initialize_graph(self):
with self.sess.graph.as_default():
input_tensors = get_inference_input()
output_tensors = dual_net(input_tensors, batch_size=-1,
train_mode=False, **self.hparams)
self.inference_input = input_tensors
self.inference_output = output_tensors
if self.save_file is not None:
self.initialize_weights(self.save_file)
else:
self.sess.run(tf.global_variables_initializer())
def initialize_weights(self, save_file):
"""Initialize the weights from the given save_file.
Assumes that the graph has been constructed, and the
save_file contains weights that match the graph. Used
to set the weights to a different version of the player
without redifining the entire graph."""
tf.train.Saver().restore(self.sess, save_file)
def run(self, position, use_random_symmetry=True):
probs, values = self.run_many([position],
use_random_symmetry=use_random_symmetry)
return probs[0], values[0]
def run_many(self, positions, use_random_symmetry=True):
processed = list(map(features.extract_features, positions))
if use_random_symmetry:
syms_used, processed = symmetries.randomize_symmetries_feat(
processed)
outputs = self.sess.run(self.inference_output,
feed_dict={self.inference_input['pos_tensor']: processed})
probabilities, value = outputs['policy_output'], outputs['value_output']
if use_random_symmetry:
probabilities = symmetries.invert_symmetries_pi(
syms_used, probabilities)
return probabilities, value
def get_inference_input():
return {
'pos_tensor': tf.placeholder(tf.float32,
[None, go.N, go.N, features.NEW_FEATURES_PLANES],
name='pos_tensor'),
'pi_tensor': tf.placeholder(tf.float32,
[None, go.N * go.N + 1]),
'value_tensor': tf.placeholder(tf.float32, [None]),
}
def _round_power_of_two(n):
"""Finds the nearest power of 2 to a number.
Thus 84 -> 64, 120 -> 128, etc.
"""
return 2 ** int(round(math.log(n, 2)))
def get_default_hyperparams(**overrides):
"""Returns the hyperparams for the neural net.
In other words, returns a dict whose parameters come from the AGZ
paper:
k: number of filters (AlphaGoZero used 256). We use 128 by
default for a 19x19 go board.
fc_width: Dimensionality of the fully connected linear layer
num_shared_layers: number of shared residual blocks. AGZ used both 19
and 39. Here we use 19 because it's faster to train.
l2_strength: The L2 regularization parameter.
momentum: The momentum parameter for training
"""
k = _round_power_of_two(go.N ** 2 / 3) # width of each layer
hparams = {
'k': k, # Width of each conv layer
'fc_width': 2 * k, # Width of each fully connected layer
'num_shared_layers': go.N, # Number of shared trunk layers
'l2_strength': 1e-4, # Regularization strength
'momentum': 0.9, # Momentum used in SGD
}
hparams.update(**overrides)
return hparams
def dual_net(input_tensors, batch_size, train_mode, **hparams):
'''
Given dict of batched tensors
pos_tensor: [BATCH_SIZE, go.N, go.N, features.NEW_FEATURES_PLANES]
pi_tensor: [BATCH_SIZE, go.N * go.N + 1]
value_tensor: [BATCH_SIZE]
return dict of tensors
logits: [BATCH_SIZE, go.N * go.N + 1]
policy: [BATCH_SIZE, go.N * go.N + 1]
value: [BATCH_SIZE]
'''
my_batchn = functools.partial(tf.layers.batch_normalization,
momentum=.997, epsilon=1e-5,
fused=True, center=True, scale=True,
training=train_mode)
my_conv2d = functools.partial(tf.layers.conv2d,
filters=hparams['k'],
kernel_size=[3, 3], padding="same")
def my_res_layer(inputs, train_mode):
int_layer1 = my_batchn(my_conv2d(inputs))
initial_output = tf.nn.relu(int_layer1)
int_layer2 = my_batchn(my_conv2d(initial_output))
output = tf.nn.relu(inputs + int_layer2)
return output
initial_output = tf.nn.relu(my_batchn(
my_conv2d(input_tensors['pos_tensor'])))
# the shared stack
shared_output = initial_output
for i in range(hparams['num_shared_layers']):
shared_output = my_res_layer(shared_output, train_mode)
# policy head
policy_conv = tf.nn.relu(my_batchn(
my_conv2d(shared_output, filters=2, kernel_size=[1, 1]),
center=False, scale=False))
logits = tf.layers.dense(
tf.reshape(policy_conv, [batch_size, go.N * go.N * 2]),
go.N * go.N + 1)
policy_output = tf.nn.softmax(logits, name='policy_output')
# value head
value_conv = tf.nn.relu(my_batchn(
my_conv2d(shared_output, filters=1, kernel_size=[1, 1]),
center=False, scale=False))
value_fc_hidden = tf.nn.relu(tf.layers.dense(
tf.reshape(value_conv, [batch_size, go.N * go.N]),
hparams['fc_width']))
value_output = tf.nn.tanh(
tf.reshape(tf.layers.dense(value_fc_hidden, 1), [batch_size]),
name='value_output')
return {
'logits': logits,
'policy_output': policy_output,
'value_output': value_output,
}
def train_ops(input_tensors, output_tensors, **hparams):
global_step = tf.Variable(0, name="global_step", trainable=False)
policy_cost = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(
logits=output_tensors['logits'],
labels=input_tensors['pi_tensor']))
value_cost = tf.reduce_mean(tf.square(
output_tensors['value_output'] - input_tensors['value_tensor']))
l2_cost = hparams['l2_strength'] * tf.add_n([tf.nn.l2_loss(v)
for v in tf.trainable_variables() if not 'bias' in v.name])
combined_cost = policy_cost + value_cost + l2_cost
policy_output = output_tensors['policy_output']
policy_entropy = -tf.reduce_mean(tf.reduce_sum(
policy_output * tf.log(policy_output), axis=1))
boundaries = list(map(int, [1e6, 2 * 1e6]))
values = [1e-2, 1e-3, 1e-4]
learning_rate = tf.train.piecewise_constant(
global_step, boundaries, values)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = tf.train.MomentumOptimizer(
learning_rate, hparams['momentum']).minimize(
combined_cost, global_step=global_step)
return {
'policy_cost': policy_cost,
'value_cost': value_cost,
'l2_cost': l2_cost,
'combined_cost': combined_cost,
'policy_entropy': policy_entropy,
'global_step': global_step,
'train_op': train_op,
}
def logging_ops():
return tf.summary.merge([
tf.summary.histogram(weight_var.name, weight_var)
for weight_var in tf.trainable_variables()],
name="weight_summaries")
def compute_update_ratio(weight_tensors, before_weights, after_weights):
"""Compute the ratio of gradient norm to weight norm."""
deltas = [after - before for after,
before in zip(after_weights, before_weights)]
delta_norms = [np.linalg.norm(d.ravel()) for d in deltas]
weight_norms = [np.linalg.norm(w.ravel()) for w in before_weights]
ratios = [d / w for d, w in zip(delta_norms, weight_norms)]
all_summaries = [
tf.Summary.Value(tag='update_ratios/' +
tensor.name, simple_value=ratio)
for tensor, ratio in zip(weight_tensors, ratios)]
return tf.Summary(value=all_summaries)
class StatisticsCollector(object):
"""Collect statistics on the runs and create graphs.
Accuracy and cost cannot be calculated with the full test dataset
in one pass, so they must be computed in batches. Unfortunately,
the built-in TF summary nodes cannot be told to aggregate multiple
executions. Therefore, we aggregate the accuracy/cost ourselves at
the python level, and then generate the summary protobufs for writing.
"""
def __init__(self):
self.accums = collections.defaultdict(list)
def report(self, values):
"""Take a dict of scalar names to scalars, and aggregate by name."""
for key, val in values.items():
self.accums[key].append(val)
def collect(self):
all_summaries = []
for summary_name, summary_values in self.accums.items():
avg_value = sum(summary_values) / len(summary_values)
self.accums[summary_name] = []
all_summaries.append(tf.Summary.Value(
tag=summary_name, simple_value=avg_value))
return tf.Summary(value=all_summaries)