您好我有关于Tensorflow的问题 . 我训练了一些LSTM模型，我可以访问突触连接的权重和偏差，但是我似乎无法访问LSTM单元的输入，新输入，输出和忘记门权重 . 我可以获得门限张量，但当我在会话中尝试.eval（）时，我会得到错误 . 我正在使用tensorflow / python / ops / rnn_cell.py中的BasicLSTMCell类来为我的网络
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class BasicLSTMCell(RNNCell):
"""Basic LSTM recurrent network cell.
The implementation is based on: http://arxiv.org/abs/1409.2329.
We add forget_bias (default: 1) to the biases of the forget gate in order to
reduce the scale of forgetting in the beginning of the training.
It does not allow cell clipping, a projection layer, and does not
use peep-hole connections: it is the basic baseline.
For advanced models, please use the full LSTMCell that follows.
"""
def __init__(self, num_units, forget_bias=1.0, input_size=None,
state_is_tuple=True, activation=tanh):
"""Initialize the basic LSTM cell.
Args:
num_units: int, The number of units in the LSTM cell.
forget_bias: float, The bias added to forget gates (see above).
input_size: Deprecated and unused.
state_is_tuple: If True, accepted and returned states are 2-tuples of
the `c_state` and `m_state`. If False, they are concatenated
along the column axis. The latter behavior will soon be deprecated.
activation: Activation function of the inner states.
"""
if not state_is_tuple:
logging.warn("%s: Using a concatenated state is slower and will soon be "
"deprecated. Use state_is_tuple=True.", self)
if input_size is not None:
logging.warn("%s: The input_size parameter is deprecated.", self)
self._num_units = num_units
self._forget_bias = forget_bias
self._state_is_tuple = state_is_tuple
self._activation = activation
@property
def state_size(self):
return (LSTMStateTuple(self._num_units, self._num_units)
if self._state_is_tuple else 2 * self._num_units)
@property
def output_size(self):
return self._num_units
def __call__(self, inputs, state, scope=None):
"""Long short-term memory cell (LSTM)."""
with vs.variable_scope(scope or type(self).__name__): # "BasicLSTMCell"
# Parameters of gates are concatenated into one multiply for efficiency.
if self._state_is_tuple:
c, h = state
else:
c, h = array_ops.split(1, 2, state)
concat = _linear([inputs, h], 4 * self._num_units, True)
# i = input_gate, j = new_input, f = forget_gate, o = output_gate
i, j, f, o = array_ops.split(1, 4, concat)
new_c = (c * sigmoid(f + self._forget_bias) + sigmoid(i) *
self._activation(j))
new_h = self._activation(new_c) * sigmoid(o)
if self._state_is_tuple:
new_state = LSTMStateTuple(new_c, new_h)
else:
new_state = array_ops.concat(1, [new_c, new_h])
return new_h, new_state
def _get_concat_variable(name, shape, dtype, num_shards):
"""Get a sharded variable concatenated into one tensor."""
sharded_variable = _get_sharded_variable(name, shape, dtype, num_shards)
if len(sharded_variable) == 1:
return sharded_variable[0]
concat_name = name + "/concat"
concat_full_name = vs.get_variable_scope().name + "/" + concat_name + ":0"
for value in ops.get_collection(ops.GraphKeys.CONCATENATED_VARIABLES):
if value.name == concat_full_name:
return value
concat_variable = array_ops.concat(0, sharded_variable, name=concat_name)
ops.add_to_collection(ops.GraphKeys.CONCATENATED_VARIABLES,
concat_variable)
return concat_variable
def _get_sharded_variable(name, shape, dtype, num_shards):
"""Get a list of sharded variables with the given dtype."""
if num_shards > shape[0]:
raise ValueError("Too many shards: shape=%s, num_shards=%d" %
(shape, num_shards))
unit_shard_size = int(math.floor(shape[0] / num_shards))
remaining_rows = shape[0] - unit_shard_size * num_shards
shards = []
for i in range(num_shards):
current_size = unit_shard_size
if i < remaining_rows:
current_size += 1
shards.append(vs.get_variable(name + "_%d" % i, [current_size] + shape[1:],
dtype=dtype))
return shards
`
我可以看到在def _1477380中使用的i，j，f，o门但是当我tf.print它们时我得到张量，当我在会话中尝试.eval（）时我得到错误 . 我也试过tf.getVariable但是无法提取权重矩阵 . 我的问题：有没有办法评估i，j，f和o门权重/矩阵？