class ConvRNNCell(object):
def __call__(self, inputs, state, scope=None):
raise NotImplementedError("Abstract method")
@property
def state_size(self):
raise NotImplementedError("Abstract method")
@property
def output_size(self):
raise NotImplementedError("Abstract method")
def zero_state(self, batch_size, dtype):
shape = self.shape
num_features = self.num_features
zeros = tf.zeros([batch_size, shape[0], shape[1], num_features * 2])
return zeros
class BasicConvLSTMCell(ConvRNNCell):
def __init__(self, shape, filter_size, num_features, forget_bias=1.0, input_size=None,
state_is_tuple=False, activation=tf.nn.tanh):
if input_size is not None:
logging.warn("%s: The input_size parameter is deprecated.", self)
self.shape = shape
self.filter_size = filter_size
self.num_features = num_features
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='convLSTM'):
"""Long short-term memory cell (LSTM)."""
with tf.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 = tf.split(state, 2, 3)
concat = _conv_linear([inputs, h], self.filter_size, self.num_features * 4, True)
# i = input_gate, j = new_input, f = forget_gate, o = output_gate
i, j, f, o = tf.split(concat, 4, 3)
new_c = (c * tf.nn.sigmoid(f + self._forget_bias) + tf.nn.sigmoid(i) *
self._activation(j))
new_h = self._activation(new_c) * tf.nn.sigmoid(o)
if self._state_is_tuple:
new_state = LSTMStateTuple(new_c, new_h)
else:
new_state = tf.concat([new_c, new_h], 3)
return new_h, new_state
filter_size是怎么定义的
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