本文整理汇总了Python中tensorflow.sinh方法的典型用法代码示例。如果您正苦于以下问题：Python tensorflow.sinh方法的具体用法？Python tensorflow.sinh怎么用？Python tensorflow.sinh使用的例子？那么恭喜您, 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.sinh方法的7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: import tensorflow [as 别名]
# 或者: from tensorflow import sinh [as 别名]
def __init__(self, config):
self.config = config
self.n_steps = 10
self.n_input, self.n_hidden = 4, 2
self.state = tf.Variable(tf.random_normal(shape=[1, 4]))
self.lstm = tf.contrib.rnn.BasicLSTMCell(self.n_hidden, forget_bias=1.0, state_is_tuple=False)
self.Wc, self.bc = self.init_controller_vars()
self.Wv, self.bv = self.init_value_vars()
# Other functions used in the paper
# self.full_list_unary = {1:lambda x:x ,2:lambda x: -x, 3: tf.abs, 4:lambda x : tf.pow(x,2),5:lambda x : tf.pow(x,3),
# 6:tf.sqrt,7:lambda x: tf.Variable(tf.truncated_normal([1], stddev=0.08))*x,
# 8:lambda x : x + tf.Variable(tf.truncated_normal([1], stddev=0.08)),9:lambda x: tf.log(tf.abs(x)+10e-8),
# 10:tf.exp,11:tf.sin,12:tf.sinh,13:tf.cosh,14:tf.tanh,15:tf.asinh,16:tf.atan,17:lambda x: tf.sin(x)/x,
# 18:lambda x : tf.maximum(x,0),19:lambda x : tf.minimum(x,0),20:tf.sigmoid,21:lambda x:tf.log(1+tf.exp(x)),
# 22:lambda x:tf.exp(-tf.pow(x,2)),23:tf.erf,24:lambda x: tf.Variable(tf.truncated_normal([1], stddev=0.08))}
#
# self.full_list_binary = {1:lambda x,y: x+y,2:lambda x,y:x*y,3:lambda x,y:x-y,4:lambda x,y:x/(y+10e-8),
# 5:lambda x,y:tf.maximum(x,y),6:lambda x,y: tf.sigmoid(x)*y,7:lambda x,y:tf.exp(-tf.Variable(tf.truncated_normal([1], stddev=0.08))*tf.pow(x-y,2)),
# 8:lambda x,y:tf.exp(-tf.Variable(tf.truncated_normal([1], stddev=0.08))*tf.abs(x-y)),
# 9:lambda x,y: tf.Variable(tf.truncated_normal([1], stddev=0.08))*x + (1-tf.Variable(tf.truncated_normal([1], stddev=0.08)))*y}
#
# self.unary = {1:lambda x:x ,2:lambda x: -x, 3: lambda x: tf.maximum(x,0), 4:lambda x : tf.pow(x,2),5:tf.tanh}
# binary = {1:lambda x,y: x+y,2:lambda x,y:x*y,3:lambda x,y:x-y,4:lambda x,y:tf.maximum(x,y),5:lambda x,y: tf.sigmoid(x)*y}
# inputs = {1:lambda x:x , 2:lambda x:0, 3: lambda x:3.14159265,4: lambda x : 1, 5: lambda x: 1.61803399} 示例2: test_forward_unary
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import sinh [as 别名]
def test_forward_unary():
def _test_forward_unary(op, a_min=1, a_max=5, dtype=np.float32):
"""test unary operators"""
np_data = np.random.uniform(a_min, a_max, size=(2, 3, 5)).astype(dtype)
tf.reset_default_graph()
with tf.Graph().as_default():
in_data = tf.placeholder(dtype, (2, 3, 5), name="in_data")
out = op(in_data)
compare_tf_with_tvm([np_data], ['in_data:0'], out.name)
_test_forward_unary(tf.acos, -1, 1)
_test_forward_unary(tf.asin, -1, 1)
_test_forward_unary(tf.atanh, -1, 1)
_test_forward_unary(tf.sinh)
_test_forward_unary(tf.cosh)
_test_forward_unary(tf.acosh)
_test_forward_unary(tf.asinh)
_test_forward_unary(tf.atan)
_test_forward_unary(tf.sin)
_test_forward_unary(tf.cos)
_test_forward_unary(tf.tan)
_test_forward_unary(tf.tanh)
_test_forward_unary(tf.erf)
_test_forward_unary(tf.log)
_test_forward_unary(tf.log1p) 示例3: numpy_sinh
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import sinh [as 别名]
def numpy_sinh(a):
return np.sinh(a) 示例4: tfe_sinh
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import sinh [as 别名]
def tfe_sinh(t):
return tf.sinh(t) 示例5: dtfcosh
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import sinh [as 别名]
def dtfcosh(y, x):
d[x] = d[y] * tf.sinh(x) 示例6: ttfcosh
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import sinh [as 别名]
def ttfcosh(y, x):
d[y] = d[x] * tf.sinh(x) 示例7: displaced_squeezed
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import sinh [as 别名]
def displaced_squeezed(r_d, phi_d, r_s, phi_s, cutoff, pure=True, batched=False, eps=1e-12):
"""creates a single mode input displaced squeezed state"""
alpha = tf.cast(r_d, def_type) * tf.exp(1j * tf.cast(phi_d, def_type))
r_s = (
tf.cast(r_s, def_type) + eps
) # to prevent nans if r==0, we add an epsilon (default is miniscule)
phi_s = tf.cast(phi_s, def_type)
phase = tf.exp(1j * phi_s)
sinh = tf.sinh(r_s)
cosh = tf.cosh(r_s)
tanh = tf.tanh(r_s)
# create Hermite polynomials
gamma = alpha * cosh + tf.math.conj(alpha) * phase * sinh
hermite_arg = gamma / tf.sqrt(phase * tf.sinh(2 * r_s))
prefactor = tf.expand_dims(
tf.exp(-0.5 * alpha * tf.math.conj(alpha) - 0.5 * tf.math.conj(alpha) ** 2 * phase * tanh),
-1,
)
coeff = tf.stack(
[
_numer_safe_power(0.5 * phase * tanh, n / 2.0) / tf.sqrt(factorial(n) * cosh)
for n in range(cutoff)
],
axis=-1,
)
hermite_terms = tf.stack([tf.cast(H(n, hermite_arg), def_type) for n in range(cutoff)], axis=-1)
squeezed_coh = prefactor * coeff * hermite_terms
if not pure:
squeezed_coh = mixed(squeezed_coh, batched)
return squeezed_coh