本文整理匯總了Python中tensorflow.asinh方法的典型用法代碼示例。如果您正苦於以下問題：Python tensorflow.asinh方法的具體用法？Python tensorflow.asinh怎麽用？Python tensorflow.asinh使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類tensorflow的用法示例。
在下文中一共展示了tensorflow.asinh方法的6個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: __init__
# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import asinh [as 別名]
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
# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import asinh [as 別名]
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: arcsinh
# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import asinh [as 別名]
def arcsinh(x, alpha=1):
"""
asignh
See Also: logtanh
"""
return tf.asinh(x * alpha) / alpha 示例4: reashu
# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import asinh [as 別名]
def reashu(t):
return nn.where(t < 0, 0, asinh(t)) 示例5: ashlu
# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import asinh [as 別名]
def ashlu(t):
return nn.where(t < 0, asinh(t), t) 示例6: self_normalizing_asinh
# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import asinh [as 別名]
def self_normalizing_asinh(x):
return 1.256734802399369*tf.asinh(x)