float or scalar float Tensor with value in [0, 1]. Leak applied
during training.
ratio_on
float or scalar float Tensor with value in [0, 1]. Ratio of the
period during which the gates are open.
trainable_ratio_on
bool, weather ratio_on is trainable.
period_init_min
float or scalar float Tensor. With value > 0.
Minimum value of the initialized period.
The period values are initialized by drawing from the distribution:
e^U(log(period_init_min), log(period_init_max))
Where U(.,.) is the uniform distribution.
period_init_max
float or scalar float Tensor.
With value > period_init_min. Maximum value of the initialized period.
reuse
(optional) Python boolean describing whether to reuse variables
in an existing scope. If not True, and the existing scope already has
the given variables, an error is raised.
Attributes
graph
DEPRECATED FUNCTION
output_size
Integer or TensorShape: size of outputs produced by this cell.
scope_name
state_size
size(s) of state(s) used by this cell.
It can be represented by an Integer, a TensorShape or a tuple of Integers
or TensorShapes.
int, float, or unit Tensor representing the batch size.
dtype
the data type to use for the state.
Returns
If state_size is an int or TensorShape, then the return value is a
N-D tensor of shape [batch_size, state_size] filled with zeros.
If state_size is a nested list or tuple, then the return value is
a nested list or tuple (of the same structure) of 2-D tensors with
the shapes [batch_size, s] for each s in state_size.
[null,null,["Last updated 2020-10-01 UTC."],[],[],null,["# tf.contrib.rnn.PhasedLSTMCell\n\n\u003cbr /\u003e\n\n|------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/rnn/python/ops/rnn_cell.py#L1915-L2064) |\n\nPhased LSTM recurrent network cell.\n\nInherits From: [`RNNCell`](../../../tf/nn/rnn_cell/RNNCell) \n\n tf.contrib.rnn.PhasedLSTMCell(\n num_units, use_peepholes=False, leak=0.001, ratio_on=0.1,\n trainable_ratio_on=True, period_init_min=1.0, period_init_max=1000.0, reuse=None\n )\n\n\u003chttps://arxiv.org/pdf/1610.09513v1.pdf\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|----------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `num_units` | int, The number of units in the Phased LSTM cell. |\n| `use_peepholes` | bool, set True to enable peephole connections. |\n| `leak` | float or scalar float Tensor with value in \\[0, 1\\]. Leak applied during training. |\n| `ratio_on` | float or scalar float Tensor with value in \\[0, 1\\]. Ratio of the period during which the gates are open. |\n| `trainable_ratio_on` | bool, weather ratio_on is trainable. |\n| `period_init_min` | float or scalar float Tensor. With value \\\u003e 0. Minimum value of the initialized period. The period values are initialized by drawing from the distribution: e\\^U(log(period_init_min), log(period_init_max)) Where U(.,.) is the uniform distribution. |\n| `period_init_max` | float or scalar float Tensor. With value \\\u003e period_init_min. Maximum value of the initialized period. |\n| `reuse` | (optional) Python boolean describing whether to reuse variables in an existing scope. If not `True`, and the existing scope already has the given variables, an error is raised. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Attributes ---------- ||\n|---------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `graph` | DEPRECATED FUNCTION \u003cbr /\u003e | **Warning:** THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Stop using this property because tf.layers layers no longer track their graph. |\n| `output_size` | Integer or TensorShape: size of outputs produced by this cell. |\n| `scope_name` | \u003cbr /\u003e |\n| `state_size` | size(s) of state(s) used by this cell. \u003cbr /\u003e It can be represented by an Integer, a TensorShape or a tuple of Integers or TensorShapes. |\n\n\u003cbr /\u003e\n\nMethods\n-------\n\n### `get_initial_state`\n\n[View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/ops/rnn_cell_impl.py#L281-L309) \n\n get_initial_state(\n inputs=None, batch_size=None, dtype=None\n )\n\n### `zero_state`\n\n[View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/ops/rnn_cell_impl.py#L311-L340) \n\n zero_state(\n batch_size, dtype\n )\n\nReturn zero-filled state tensor(s).\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ||\n|--------------|---------------------------------------------------------|\n| `batch_size` | int, float, or unit Tensor representing the batch size. |\n| `dtype` | the data type to use for the state. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ||\n|---|---|\n| If `state_size` is an int or TensorShape, then the return value is a `N-D` tensor of shape `[batch_size, state_size]` filled with zeros. \u003cbr /\u003e If `state_size` is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of `2-D` tensors with the shapes `[batch_size, s]` for each s in `state_size`. ||\n\n\u003cbr /\u003e"]]
View source on GitHub