建议的解决方案对我有用,但是Layer.call
方法规范更通用,因此以下Wrapper
应该对 API 更改更加稳健。你这个:
class Wrapper(tf.nn.rnn_cell.RNNCell):
def __init__(self, inner_cell):
super(Wrapper, self).__init__()
self._inner_cell = inner_cell
@property
def state_size(self):
return self._inner_cell.state_size
@property
def output_size(self):
return (self._inner_cell.state_size, self._inner_cell.output_size)
def call(self, input, *args, **kwargs):
output, next_state = self._inner_cell(input, *args, **kwargs)
emit_output = (next_state, output)
return emit_output, next_state
这是测试:
n_steps = 2
n_inputs = 3
n_neurons = 5
X = tf.placeholder(dtype=tf.float32, shape=[None, n_steps, n_inputs])
basic_cell = Wrapper(tf.nn.rnn_cell.LSTMCell(num_units=n_neurons, state_is_tuple=False))
outputs, states = tf.nn.dynamic_rnn(basic_cell, X, dtype=tf.float32)
print(outputs, states)
X_batch = np.array([
# t = 0 t = 1
[[0, 1, 2], [9, 8, 7]], # instance 0
[[3, 4, 5], [0, 0, 0]], # instance 1
[[6, 7, 8], [6, 5, 4]], # instance 2
[[9, 0, 1], [3, 2, 1]], # instance 3
])
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
outputs_val = outputs[0].eval(feed_dict={X: X_batch})
print(outputs_val)
回outputs
是元组(?, 2, 10)
and (?, 2, 5)
张量,都是 LSTM 状态和输出。请注意,我使用的是“毕业”版本LSTMCell
, from tf.nn.rnn_cell
包,不tf.contrib.rnn
。另请注意state_is_tuple=True
以避免处理LSTMStateTuple
.