本文整理汇总了Python中baselines.common.tf_util.switch方法的典型用法代码示例。如果您正苦于以下问题：Python tf_util.switch方法的具体用法？Python tf_util.switch怎么用？Python tf_util.switch使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类baselines.common.tf_util的用法示例。
在下文中一共展示了tf_util.switch方法的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _init
# 需要导入模块: from baselines.common import tf_util [as 别名]
# 或者: from baselines.common.tf_util import switch [as 别名]
def _init(self, ob_space, ac_space, hid_size, num_hid_layers, gaussian_fixed_var=True):
assert isinstance(ob_space, gym.spaces.Box)
self.pdtype = pdtype = make_pdtype(ac_space)
sequence_length = None
ob = U.get_placeholder(name="ob", dtype=tf.float32, shape=[sequence_length] + list(ob_space.shape))
with tf.variable_scope("obfilter"):
self.ob_rms = RunningMeanStd(shape=ob_space.shape)
obz = tf.clip_by_value((ob - self.ob_rms.mean) / self.ob_rms.std, -5.0, 5.0)
last_out = obz
for i in range(num_hid_layers):
last_out = tf.nn.tanh(dense(last_out, hid_size, "vffc%i" % (i+1), weight_init=U.normc_initializer(1.0)))
self.vpred = dense(last_out, 1, "vffinal", weight_init=U.normc_initializer(1.0))[:, 0]
last_out = obz
for i in range(num_hid_layers):
last_out = tf.nn.tanh(dense(last_out, hid_size, "polfc%i" % (i+1), weight_init=U.normc_initializer(1.0)))
if gaussian_fixed_var and isinstance(ac_space, gym.spaces.Box):
mean = dense(last_out, pdtype.param_shape()[0]//2, "polfinal", U.normc_initializer(0.01))
logstd = tf.get_variable(name="logstd", shape=[1, pdtype.param_shape()[0]//2], initializer=tf.zeros_initializer())
pdparam = tf.concat([mean, mean * 0.0 + logstd], axis=1)
else:
pdparam = dense(last_out, pdtype.param_shape()[0], "polfinal", U.normc_initializer(0.01))
self.pd = pdtype.pdfromflat(pdparam)
self.state_in = []
self.state_out = []
# change for BC
stochastic = U.get_placeholder(name="stochastic", dtype=tf.bool, shape=())
ac = U.switch(stochastic, self.pd.sample(), self.pd.mode())
self.ac = ac
self._act = U.function([stochastic, ob], [ac, self.vpred]) 示例2: _init
# 需要导入模块: from baselines.common import tf_util [as 别名]
# 或者: from baselines.common.tf_util import switch [as 别名]
def _init(self, ob_space, ac_space, hid_size, num_hid_layers, gaussian_fixed_var=True):
assert isinstance(ob_space, gym.spaces.Box)
self.pdtype = pdtype = make_pdtype(ac_space)
sequence_length = None
ob = U.get_placeholder(name="ob", dtype=tf.float32, shape=[sequence_length] + list(ob_space.shape))
with tf.variable_scope("obfilter"):
self.ob_rms = RunningMeanStd(shape=ob_space.shape)
with tf.variable_scope('vf'):
obz = tf.clip_by_value((ob - self.ob_rms.mean) / self.ob_rms.std, -5.0, 5.0)
last_out = obz
for i in range(num_hid_layers):
last_out = tf.nn.tanh(tf.layers.dense(last_out, hid_size, name="fc%i"%(i+1), kernel_initializer=U.normc_initializer(1.0)))
self.vpred = tf.layers.dense(last_out, 1, name='final', kernel_initializer=U.normc_initializer(1.0))[:,0]
with tf.variable_scope('pol'):
last_out = obz
for i in range(num_hid_layers):
last_out = tf.nn.tanh(tf.layers.dense(last_out, hid_size, name='fc%i'%(i+1), kernel_initializer=U.normc_initializer(1.0)))
if gaussian_fixed_var and isinstance(ac_space, gym.spaces.Box):
mean = tf.layers.dense(last_out, pdtype.param_shape()[0]//2, name='final', kernel_initializer=U.normc_initializer(0.01))
logstd = tf.get_variable(name="logstd", shape=[1, pdtype.param_shape()[0]//2], initializer=tf.zeros_initializer())
pdparam = tf.concat([mean, mean * 0.0 + logstd], axis=1)
else:
pdparam = tf.layers.dense(last_out, pdtype.param_shape()[0], name='final', kernel_initializer=U.normc_initializer(0.01))
self.pd = pdtype.pdfromflat(pdparam)
self.state_in = []
self.state_out = []
stochastic = tf.placeholder(dtype=tf.bool, shape=())
ac = U.switch(stochastic, self.pd.sample(), self.pd.mode())
self._act = U.function([stochastic, ob], [ac, self.vpred]) 示例3: _init
# 需要导入模块: from baselines.common import tf_util [as 别名]
# 或者: from baselines.common.tf_util import switch [as 别名]
def _init(self, ob_space, ac_space, hid_size, num_hid_layers, gaussian_fixed_var=True):
assert isinstance(ob_space, gym.spaces.Box)
self.pdtype = pdtype = make_pdtype(ac_space)
sequence_length = None
ob = U.get_placeholder(name="ob", dtype=tf.float32, shape=[sequence_length] + list(ob_space.shape))
with tf.variable_scope("obfilter"):
self.ob_rms = RunningMeanStd(shape=ob_space.shape)
obz = tf.clip_by_value((ob - self.ob_rms.mean) / self.ob_rms.std, -5.0, 5.0)
last_out = obz
for i in range(num_hid_layers):
last_out = tf.nn.tanh(tf.layers.dense(last_out, hid_size, name="vffc%i"%(i+1), kernel_initializer=U.normc_initializer(1.0)))
self.vpred = tf.layers.dense(last_out, 1, name="vffinal", kernel_initializer=U.normc_initializer(1.0))[:,0]
last_out = obz
for i in range(num_hid_layers):
last_out = tf.nn.tanh(tf.layers.dense(last_out, hid_size, name="polfc%i"%(i+1), kernel_initializer=U.normc_initializer(1.0)))
if gaussian_fixed_var and isinstance(ac_space, gym.spaces.Box):
mean = tf.layers.dense(last_out, pdtype.param_shape()[0]//2, name="polfinal", kernel_initializer=U.normc_initializer(0.01))
logstd = tf.get_variable(name="logstd", shape=[1, pdtype.param_shape()[0]//2], initializer=tf.zeros_initializer())
pdparam = tf.concat([mean, mean * 0.0 + logstd], axis=1)
else:
pdparam = tf.layers.dense(last_out, pdtype.param_shape()[0], name="polfinal", kernel_initializer=U.normc_initializer(0.01))
self.pd = pdtype.pdfromflat(pdparam)
self.state_in = []
self.state_out = []
stochastic = tf.placeholder(dtype=tf.bool, shape=())
ac = U.switch(stochastic, self.pd.sample(), self.pd.mode())
self._act = U.function([stochastic, ob], [ac, self.vpred]) 示例4: _init
# 需要导入模块: from baselines.common import tf_util [as 别名]
# 或者: from baselines.common.tf_util import switch [as 别名]
def _init(self, ob_space, ac_space, hid_size, num_hid_layers, gaussian_fixed_var=True):
assert isinstance(ob_space, gym.spaces.Box)
self.pdtype = pdtype = make_pdtype(ac_space)
sequence_length = None
ob = U.get_placeholder(name="ob", dtype=tf.float32, shape=[sequence_length] + list(ob_space.shape))
with tf.variable_scope("obfilter"):
self.ob_rms = RunningMeanStd(shape=ob_space.shape)
obz = tf.clip_by_value((ob - self.ob_rms.mean) / self.ob_rms.std, -5.0, 5.0)
last_out = obz
for i in range(num_hid_layers):
last_out = tf.nn.tanh(U.dense(last_out, hid_size, "vffc%i"%(i+1), weight_init=U.normc_initializer(1.0)))
self.vpred = U.dense(last_out, 1, "vffinal", weight_init=U.normc_initializer(1.0))[:,0]
last_out = obz
for i in range(num_hid_layers):
last_out = tf.nn.tanh(U.dense(last_out, hid_size, "polfc%i"%(i+1), weight_init=U.normc_initializer(1.0)))
if gaussian_fixed_var and isinstance(ac_space, gym.spaces.Box):
mean = U.dense(last_out, pdtype.param_shape()[0]//2, "polfinal", U.normc_initializer(0.01))
logstd = tf.get_variable(name="logstd", shape=[1, pdtype.param_shape()[0]//2], initializer=tf.zeros_initializer())
pdparam = U.concatenate([mean, mean * 0.0 + logstd], axis=1)
else:
pdparam = U.dense(last_out, pdtype.param_shape()[0], "polfinal", U.normc_initializer(0.01))
self.pd = pdtype.pdfromflat(pdparam)
self.state_in = []
self.state_out = []
stochastic = tf.placeholder(dtype=tf.bool, shape=())
ac = U.switch(stochastic, self.pd.sample(), self.pd.mode())
self._act = U.function([stochastic, ob], [ac, self.vpred]) 示例5: _init
# 需要导入模块: from baselines.common import tf_util [as 别名]
# 或者: from baselines.common.tf_util import switch [as 别名]
def _init(self, ob_space, ac_space, hid_size, num_hid_layers, activation='tanh', gaussian_fixed_var=True, keep=1.0):
assert isinstance(ob_space, gym.spaces.Box)
self.pdtype = pdtype = make_pdtype(ac_space)
sequence_length = None
ob_shape = OBSERVATION_DIM if PREPROCESS else ob_space.shape[0]
ob = U.get_placeholder(name="ob", dtype=tf.float32, shape=[sequence_length, ob_shape])
if activation == 'tanh':
activ = tf.nn.tanh
elif activation == 'elu':
activ = tf.nn.elu
elif activation == 'lrelu':
activ = lambda x: tf.maximum(x, 0.01 * x)
else:
raise NotImplementedError("Not available activation: " + activation)
if PREPROCESS:
last_out = ob
else:
with tf.variable_scope("obfilter"):
self.ob_rms = RunningMeanStd(shape=ob_space.shape)
obz = tf.clip_by_value((ob - self.ob_rms.mean) / self.ob_rms.std, -5.0, 5.0)
last_out = obz
for i in range(num_hid_layers):
last_out = activ(U.dense(last_out, hid_size, "vffc%i" % (i + 1), weight_init=U.normc_initializer(1.0)))
last_out = tf.nn.dropout(last_out, keep_prob=keep, name="vdrop%i" % (i + 1))
self.vpred = U.dense(last_out, 1, "vffinal", weight_init=U.normc_initializer(1.0))[:, 0]
last_out = ob
for i in range(num_hid_layers):
last_out = activ(U.dense(last_out, hid_size, "polfc%i" % (i + 1), weight_init=U.normc_initializer(1.0)))
last_out = tf.nn.dropout(last_out, keep_prob=keep, name="pdrop%i" % (i + 1))
if gaussian_fixed_var and isinstance(ac_space, gym.spaces.Box):
mean = U.dense(last_out, pdtype.param_shape()[0] // 2, "polfinal", U.normc_initializer(0.01))
logstd = tf.get_variable(name="logstd", shape=[1, pdtype.param_shape()[0] // 2],
initializer=tf.zeros_initializer())
pdparam = U.concatenate([mean, mean * 0.0 + logstd], axis=1)
else:
pdparam = U.dense(last_out, pdtype.param_shape()[0], "polfinal", U.normc_initializer(0.01))
self.pd = pdtype.pdfromflat(pdparam)
self.state_in = []
self.state_out = []
stochastic = tf.placeholder(dtype=tf.bool, shape=())
ac = U.switch(stochastic, self.pd.sample(), self.pd.mode())
self._act = U.function([stochastic, ob], [ac, self.vpred])