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model_test_vp_3c.py
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import tensorflow as tf
from core.model import Model
import numpy as np
def configure_optimizer(learning_rate, conf):
if conf["optimizer"] == 'adadelta':
optimizer = tf.train.AdadeltaOptimizer(
learning_rate,
rho=conf["adadelta_rho"],
epsilon=conf["opt_epsilon"])
elif conf["optimizer"] == 'adagrad':
optimizer = tf.train.AdagradOptimizer(
learning_rate,
initial_accumulator_value=conf["adagrad_initial_accumulator_value"])
elif conf["optimizer"] == 'adam':
optimizer = tf.train.AdamOptimizer(
learning_rate,
beta1=conf["adam_beta1"],
beta2=conf["adam_beta2"],
epsilon=conf["opt_epsilon"])
elif conf["optimizer"] == 'ftrl':
optimizer = tf.train.FtrlOptimizer(
learning_rate,
learning_rate_power=conf["ftrl_learning_rate_power"],
initial_accumulator_value=conf["ftrl_initial_accumulator_value"],
l1_regularization_strength=conf["ftrl_l1"],
l2_regularization_strength=conf["ftrl_l2"])
elif conf["optimizer"] == 'momentum':
optimizer = tf.train.MomentumOptimizer(
learning_rate,
momentum=conf["momentum"],
name='Momentum')
elif conf["optimizer"] == 'rmsprop':
optimizer = tf.train.RMSPropOptimizer(
learning_rate,
decay=conf["rmsprop_decay"],
momentum=conf["rmsprop_momentum"],
epsilon=conf["opt_epsilon"])
elif conf["optimizer"] == 'sgd':
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
else:
raise ValueError('Optimizer [%s] was not recognized', conf["optimizer"])
return optimizer
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
input_shape = [10, 9, 3]
num_layers = 1
num_classes = 10 * 9
weight_decay = 0.01
momentum = 0.9
use_bias = True
learning_rate = 0.01
inputs = tf.placeholder(tf.float32, [None, input_shape[0], input_shape[1], input_shape[2]],
"inputs")
policy_label = tf.placeholder(tf.float32, [None, num_classes], "policy_label")
value_label = tf.placeholder(tf.float32, [None], "value_label")
# policy_label = tf.placeholder(tf.float32, [None, input_shape[0], input_shape[1], input_shape[2]], "policy_label")
net = tf.layers.conv2d(
inputs=inputs, filters=256, kernel_size=3, strides=1,
padding='SAME', use_bias=use_bias,
kernel_initializer=tf.variance_scaling_initializer())
# _BATCH_NORM_DECAY = 0.997
# _BATCH_NORM_EPSILON = 1e-5
# net = tf.layers.batch_normalization(
# inputs=net, axis=3, momentum=_BATCH_NORM_DECAY, epsilon=_BATCH_NORM_EPSILON, center=True,
# scale=True, training=True, fused=True)
net = tf.nn.relu(net)
# net = tf.layers.conv2d(
# inputs=net, filters=1, kernel_size=1, strides=1,
# padding='SAME', use_bias=use_bias,
# kernel_initializer=tf.variance_scaling_initializer())
# net = tf.nn.relu(net)
value_network = tf.layers.conv2d(
inputs=net, filters=1, kernel_size=1, strides=1,
padding='SAME', use_bias=use_bias,
kernel_initializer=tf.variance_scaling_initializer())
value_network = tf.reshape(value_network, [-1, num_classes], name="value_reshape")
value_network = tf.layers.dense(inputs=value_network, units=64, name="value_dense1")
value_network = tf.nn.relu(value_network, name="value_relu")
value_network = tf.layers.dense(inputs=value_network, units=1, name="value_dense2")
value_network = tf.nn.tanh(value_network, name="value_tanh")
# value_net_inputs = tf.reshape(value_net_inputs, [-1], name="value_scalar_reshape")
policy_network = tf.layers.conv2d(
inputs=net, filters=2, kernel_size=1, strides=1,
padding='SAME', use_bias=use_bias,
kernel_initializer=tf.variance_scaling_initializer())
policy_network = tf.reshape(policy_network, [-1, num_classes * 2], name="policy_reshape")
policy_network = tf.layers.dense(inputs=policy_network, units=num_classes, name="policy_dense")
policy_network = tf.nn.softmax(policy_network)
weights = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
regularizer = 0
for weight in weights:
regularizer += tf.nn.l2_loss(weight)
regularizer *= weight_decay
value_loss = tf.reduce_mean(tf.square(value_label - tf.reshape(value_network, [-1])))
# value_loss = tf.reduce_mean(tf.square(value_label - value_network))
# policy_loss = -tf.reduce_mean(tf.nn.softmax(self.policy_label) * tf.log(self.policy_network))
# policy_loss = -tf.reduce_mean(tf.transpose(self.policy_label) * tf.log(self.policy_network))
policy_loss = -tf.reduce_mean(tf.reduce_sum(policy_label * tf.log(policy_network), axis=1))
cost = value_loss + policy_loss + regularizer
# cost = value_loss + policy_loss
conf = {"optimizer": "sgd"}
conf['adadelta_rho'] = 0.95
conf['adagrad_initial_accumulator_value'] = 0.1
conf['adam_beta1'] = 0.9
conf['adam_beta2'] = 0.999
conf['opt_epsilon'] = 1.0
conf['ftrl_learning_rate_power'] = -0.5
conf['ftrl_initial_accumulator_value'] = 0.1
conf['ftrl_l1'] = 0.0
conf['ftrl_l2'] = 0.0
conf['rmsprop_momentum'] = 0.9
conf['rmsprop_decay'] = 0.9
optimizer = configure_optimizer(learning_rate, conf)
train_op = optimizer.minimize(cost)
sess.run(tf.global_variables_initializer())
epoch = 400
KING = 1.0
# CAR = 0.9
# PHO = 0.8
# MA = 0.7
# SA = 0.6
# SANG = 0.5
# JJOL = 0.4
CAR = 0.66
PHO = 0.33
MA = 0.
SA = -0.33
SANG = -0.66
JJOL = -1.
state = np.array([[[
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[JJOL, 0, JJOL, 0, JJOL, 0, JJOL, 0, JJOL],
[0, PHO, 0, 0, 0, 0, 0, PHO, 0],
[0, 0, 0, 0, KING, 0, 0, 0, 0],
[CAR, SANG, MA, SA, 0, SA, SANG, MA, CAR],
],[
[CAR, MA, SANG, SA, 0, SA, MA, SANG, CAR],
[0, 0, 0, 0, KING, 0, 0, 0, 0],
[0, PHO, 0, 0, 0, 0, 0, PHO, 0],
[JJOL, 0, JJOL, 0, JJOL, 0, JJOL, 0, JJOL],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
],[
[1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1],
]],
[[
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[JJOL, 0, JJOL, 0, JJOL, 0, JJOL, 0, JJOL],
[0, PHO, 0, 0, 0, 0, 0, PHO, 0],
[0, 0, 0, 0, KING, 0, 0, 0, 0],
[CAR, SANG, MA, SA, 0, SA, SANG, MA, CAR],
], [
[CAR, MA, SANG, SA, 0, SA, MA, SANG, CAR],
[0, 0, 0, 0, KING, 0, 0, 0, 0],
[0, PHO, 0, 0, 0, 0, 0, PHO, 0],
[JJOL, 0, JJOL, 0, JJOL, 0, JJOL, 0, JJOL],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
], [
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
]],
])
policy = np.array([0.] * 90)
policy[35] = 0.5
policy[34] = 0.5
policy2 = np.array([0.] * 90)
policy2[62] = 0.5
policy2[61] = 0.5
# state = np.array([np.transpose(state, [1, 2, 0])])
state = np.array(np.transpose(state, [0, 2, 3, 1]))
policy = np.array([policy, policy2])
value = np.array([1., -1.])
# policy = np.array([np.transpose(np.reshape(policy, [1,10,9]), [1,2,0])])
for i in range(epoch):
print(i)
_, cost_result, policy_result, value_result, value_loss_result, policy_loss_result = sess.run(
[train_op, cost, policy_network, value_network, value_loss, policy_loss],
feed_dict={inputs: state, policy_label: policy,
value_label: value})
print(policy_result)
# policy_result = np.transpose(policy_result[0], [2, 0, 1])
# policy_result = np.reshape(policy_result, [90])
print(policy_result.shape)
print(policy_result[0][35], policy_result[0][34])
print(policy_result[1][62], policy_result[1][61])
print(value_result)
print(cost_result)
print(value_loss_result)
print(policy_loss_result)