本文整理汇总了Python中tensorflow.constant_initializer方法的典型用法代码示例。如果您正苦于以下问题:Python tensorflow.constant_initializer方法的具体用法?Python tensorflow.constant_initializer怎么用?Python tensorflow.constant_initializer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tensorflow
的用法示例。
在下文中一共展示了tensorflow.constant_initializer方法的15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: test_adam
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import constant_initializer [as 别名]
def test_adam(self):
with self.test_session() as sess:
w = tf.get_variable(
"w",
shape=[3],
initializer=tf.constant_initializer([0.1, -0.2, -0.1]))
x = tf.constant([0.4, 0.2, -0.5])
loss = tf.reduce_mean(tf.square(x - w))
tvars = tf.trainable_variables()
grads = tf.gradients(loss, tvars)
global_step = tf.train.get_or_create_global_step()
optimizer = optimization.AdamWeightDecayOptimizer(learning_rate=0.2)
train_op = optimizer.apply_gradients(zip(grads, tvars), global_step)
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
sess.run(init_op)
for _ in range(100):
sess.run(train_op)
w_np = sess.run(w)
self.assertAllClose(w_np.flat, [0.4, 0.2, -0.5], rtol=1e-2, atol=1e-2)
示例2: wrap_variable
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import constant_initializer [as 别名]
def wrap_variable(self, var):
"""wrap layer.w into variables"""
val = self.lay.w.get(var, None)
if val is None:
shape = self.lay.wshape[var]
args = [0., 1e-2, shape]
if 'moving_mean' in var:
val = np.zeros(shape)
elif 'moving_variance' in var:
val = np.ones(shape)
else:
val = np.random.normal(*args)
self.lay.w[var] = val.astype(np.float32)
self.act = 'Init '
if not self.var: return
val = self.lay.w[var]
self.lay.w[var] = tf.constant_initializer(val)
if var in self._SLIM: return
with tf.variable_scope(self.scope):
self.lay.w[var] = tf.get_variable(var,
shape = self.lay.wshape[var],
dtype = tf.float32,
initializer = self.lay.w[var])
示例3: highwaynet
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import constant_initializer [as 别名]
def highwaynet(inputs, num_units=None, scope="highwaynet", reuse=None):
'''Highway networks, see https://arxiv.org/abs/1505.00387
Args:
inputs: A 3D tensor of shape [N, T, W].
num_units: An int or `None`. Specifies the number of units in the highway layer
or uses the input size if `None`.
scope: Optional scope for `variable_scope`.
reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Returns:
A 3D tensor of shape [N, T, W].
'''
if not num_units:
num_units = inputs.get_shape()[-1]
with tf.variable_scope(scope, reuse=reuse):
H = tf.layers.dense(inputs, units=num_units, activation=tf.nn.relu, name="dense1")
T = tf.layers.dense(inputs, units=num_units, activation=tf.nn.sigmoid,
bias_initializer=tf.constant_initializer(-1.0), name="dense2")
outputs = H * T + inputs * (1. - T)
return outputs
示例4: cifarnet_arg_scope
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import constant_initializer [as 别名]
def cifarnet_arg_scope(weight_decay=0.004):
"""Defines the default cifarnet argument scope.
Args:
weight_decay: The weight decay to use for regularizing the model.
Returns:
An `arg_scope` to use for the inception v3 model.
"""
with slim.arg_scope(
[slim.conv2d],
weights_initializer=tf.truncated_normal_initializer(stddev=5e-2),
activation_fn=tf.nn.relu):
with slim.arg_scope(
[slim.fully_connected],
biases_initializer=tf.constant_initializer(0.1),
weights_initializer=trunc_normal(0.04),
weights_regularizer=slim.l2_regularizer(weight_decay),
activation_fn=tf.nn.relu) as sc:
return sc
示例5: _initialize_gru_cell
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import constant_initializer [as 别名]
def _initialize_gru_cell(self, num_units):
"""Initializes a GRU cell.
The Variables of the GRU cell are initialized in a way that exactly matches
the skip-thoughts paper: recurrent weights are initialized from random
orthonormal matrices and non-recurrent weights are initialized from random
uniform matrices.
Args:
num_units: Number of output units.
Returns:
cell: An instance of RNNCell with variable initializers that match the
skip-thoughts paper.
"""
return gru_cell.LayerNormGRUCell(
num_units,
w_initializer=self.uniform_initializer,
u_initializer=random_orthonormal_initializer,
b_initializer=tf.constant_initializer(0.0))
示例6: conv_linear
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import constant_initializer [as 别名]
def conv_linear(args, kw, kh, nin, nout, rate, do_bias, bias_start, prefix):
"""Convolutional linear map."""
if not isinstance(args, (list, tuple)):
args = [args]
with tf.variable_scope(prefix):
with tf.device("/cpu:0"):
k = tf.get_variable("CvK", [kw, kh, nin, nout])
if len(args) == 1:
arg = args[0]
else:
arg = tf.concat(axis=3, values=args)
res = tf.nn.convolution(arg, k, dilation_rate=(rate, 1), padding="SAME")
if not do_bias: return res
with tf.device("/cpu:0"):
bias_term = tf.get_variable(
"CvB", [nout], initializer=tf.constant_initializer(bias_start))
bias_term = tf.reshape(bias_term, [1, 1, 1, nout])
return res + bias_term
示例7: __init__
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import constant_initializer [as 别名]
def __init__(self, initializer=Bias(0), name=None):
"""Initializes Bias block.
|initializer| parameter have two special cases.
1. If initializer is None, then this block works as a PassThrough.
2. If initializer is a Bias class object, then tf.constant_initializer is
used with the stored value.
Args:
initializer: An initializer for the bias variable.
name: Name of this block.
"""
super(BiasAdd, self).__init__(name)
with self._BlockScope():
if isinstance(initializer, Bias):
self._initializer = tf.constant_initializer(value=initializer.value)
else:
self._initializer = initializer
self._bias = None
示例8: __init__
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import constant_initializer [as 别名]
def __init__(self, learning_rate, clip_norm=5,
policy_weight=1.0, critic_weight=0.1,
tau=0.1, gamma=1.0, rollout=10,
eps_lambda=0.0, clip_adv=None):
super(ActorCritic, self).__init__(learning_rate, clip_norm=clip_norm)
self.policy_weight = policy_weight
self.critic_weight = critic_weight
self.tau = tau
self.gamma = gamma
self.rollout = rollout
self.clip_adv = clip_adv
self.eps_lambda = tf.get_variable( # TODO: need a better way
'eps_lambda', [], initializer=tf.constant_initializer(eps_lambda))
self.new_eps_lambda = tf.placeholder(tf.float32, [])
self.assign_eps_lambda = self.eps_lambda.assign(
0.95 * self.eps_lambda + 0.05 * self.new_eps_lambda)
示例9: __init__
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import constant_initializer [as 别名]
def __init__(self):
self.session = tf.Session(config=tf.ConfigProto(allow_soft_placement=True,log_device_placement=False))
self.actor = networks.Actor_MLP(scope="actor1",units=[settings.S_DIM,100,settings.A_DIM],activations=[None,'relu','tanh'],trainable=True)
self.old_actor = networks.Actor_MLP(scope="actor0",units=[settings.S_DIM,100,settings.A_DIM],activations=[None,'relu','tanh'],trainable=False)
self.critic = networks.Critic_MLP(scope="critic1",units=[settings.S_DIM,100,1],activations=[None,'relu',None],trainable=True)
self.state_tf = tf.placeholder(dtype=tf.float32,shape=[None,settings.S_DIM])
self.action_tf = tf.placeholder(dtype=tf.float32,shape=[None,settings.A_DIM])
self.return_tf = tf.placeholder(dtype=tf.float32,shape=[None,1])
self.adv_tf = tf.placeholder(dtype=tf.float32,shape=[None,1])
# global steps to keep track of training
self.actor_step = tf.get_variable('actor_global_step', [], initializer=tf.constant_initializer(0), trainable=False)
self.critic_step = tf.get_variable('critic_global_step', [], initializer=tf.constant_initializer(0), trainable=False)
# build computation graphs
self.actor.build_graph(self.state_tf,self.actor_step)
self.old_actor.build_graph(self.state_tf,0)
self.critic.build_graph(self.state_tf,self.critic_step)
self.build_graph()
示例10: make_encoder
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import constant_initializer [as 别名]
def make_encoder(self, state, z_size, scope, n_layers, hid_size):
"""
### PROBLEM 3
### YOUR CODE HERE
args:
state: tf variable
z_size: output dimension of the encoder network
scope: scope name
n_layers: number of layers of the encoder network
hid_size: hidden dimension of encoder network
TODO:
1. z_mean: the output of a neural network that takes the state as input,
has output dimension z_size, n_layers layers, and hidden
dimension hid_size
2. z_logstd: a trainable variable, initialized to 0
shape (z_size,)
Hint: use build_mlp
"""
z_mean = build_mlp(state, z_size, scope, n_layers, hid_size)
z_logstd = tf.get_variable('z_logstd', shape=z_size, trainable=True,
initializer=tf.constant_initializer(value=0.))
return tfp.distributions.MultivariateNormalDiag(loc=z_mean, scale_diag=tf.exp(z_logstd))
示例11: init_vq_bottleneck
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import constant_initializer [as 别名]
def init_vq_bottleneck(bottleneck_size, hidden_size):
"""Get lookup table for VQ bottleneck."""
means = tf.get_variable(
name="means",
shape=[bottleneck_size, hidden_size],
initializer=tf.uniform_unit_scaling_initializer())
ema_count = tf.get_variable(
name="ema_count",
shape=[bottleneck_size],
initializer=tf.constant_initializer(0),
trainable=False)
with tf.colocate_with(means):
ema_means = tf.get_variable(
name="ema_means",
initializer=means.initialized_value(),
trainable=False)
return means, ema_means, ema_count
示例12: conv
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import constant_initializer [as 别名]
def conv(x, scope, *, nf, rf, stride, pad='VALID', init_scale=1.0, data_format='NHWC'):
if data_format == 'NHWC':
channel_ax = 3
strides = [1, stride, stride, 1]
bshape = [1, 1, 1, nf]
elif data_format == 'NCHW':
channel_ax = 1
strides = [1, 1, stride, stride]
bshape = [1, nf, 1, 1]
else:
raise NotImplementedError
nin = x.get_shape()[channel_ax].value
wshape = [rf, rf, nin, nf]
with tf.variable_scope(scope):
w = tf.get_variable("w", wshape, initializer=ortho_init(init_scale))
b = tf.get_variable("b",bshape, initializer=tf.constant_initializer(0.0))
return b + tf.nn.conv2d(x, w, strides=strides, padding=pad, data_format=data_format)
示例13: lstm
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import constant_initializer [as 别名]
def lstm(xs, ms, s, scope, nh, init_scale=1.0):
nbatch, nin = [v.value for v in xs[0].get_shape()]
nsteps = len(xs)
with tf.variable_scope(scope):
wx = tf.get_variable("wx", [nin, nh*4], initializer=ortho_init(init_scale))
wh = tf.get_variable("wh", [nh, nh*4], initializer=ortho_init(init_scale))
b = tf.get_variable("b", [nh*4], initializer=tf.constant_initializer(0.0))
c, h = tf.split(axis=1, num_or_size_splits=2, value=s)
for idx, (x, m) in enumerate(zip(xs, ms)):
c = c*(1-m)
h = h*(1-m)
z = tf.matmul(x, wx) + tf.matmul(h, wh) + b
i, f, o, u = tf.split(axis=1, num_or_size_splits=4, value=z)
i = tf.nn.sigmoid(i)
f = tf.nn.sigmoid(f)
o = tf.nn.sigmoid(o)
u = tf.tanh(u)
c = f*c + i*u
h = o*tf.tanh(c)
xs[idx] = h
s = tf.concat(axis=1, values=[c, h])
return xs, s
示例14: conv2d
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import constant_initializer [as 别名]
def conv2d(self, input_, n_filters, k_size, padding='same'):
if not self.cfg.weight_scale:
return tf.layers.conv2d(input_, n_filters, k_size, padding=padding)
n_feats_in = input_.get_shape().as_list()[-1]
fan_in = k_size * k_size * n_feats_in
c = tf.constant(np.sqrt(2. / fan_in), dtype=tf.float32)
kernel_init = tf.random_normal_initializer(stddev=1.)
w_shape = [k_size, k_size, n_feats_in, n_filters]
w = tf.get_variable('kernel', shape=w_shape, initializer=kernel_init)
w = c * w
strides = [1, 1, 1, 1]
net = tf.nn.conv2d(input_, w, strides, padding=padding.upper())
b = tf.get_variable('bias', [n_filters],
initializer=tf.constant_initializer(0.))
net = tf.nn.bias_add(net, b)
return net
示例15: conv
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import constant_initializer [as 别名]
def conv(x, scope, *, nf, rf, stride, pad='VALID', init_scale=1.0, data_format='NHWC', one_dim_bias=False):
if data_format == 'NHWC':
channel_ax = 3
strides = [1, stride, stride, 1]
bshape = [1, 1, 1, nf]
elif data_format == 'NCHW':
channel_ax = 1
strides = [1, 1, stride, stride]
bshape = [1, nf, 1, 1]
else:
raise NotImplementedError
bias_var_shape = [nf] if one_dim_bias else [1, nf, 1, 1]
nin = x.get_shape()[channel_ax].value
wshape = [rf, rf, nin, nf]
with tf.variable_scope(scope):
w = tf.get_variable("w", wshape, initializer=ortho_init(init_scale))
b = tf.get_variable("b", bias_var_shape, initializer=tf.constant_initializer(0.0))
if not one_dim_bias and data_format == 'NHWC':
b = tf.reshape(b, bshape)
return tf.nn.conv2d(x, w, strides=strides, padding=pad, data_format=data_format) + b