我试图设置stateful = True来训练我的LSTM模型，并且它起作用了。
但是我必须将输入的形状调整为我为第一层设置的相同batch_size，这对于有状态RNN是必须的，否则我将收到错误消息：InvalidArgumentError：Invalid input_h shape。
我将batch_size设置为64，但是我只想输入一个开始句子来生成文本。如果我必须提供batch_size = 64的输入，则需要准备64个句子，这很荒谬。
如果我没有设置stateful = True，它会很好地工作，但是我需要提高性能。 在这种情况下，如何在不匹配我设置的batch_size的情况下使用有状态的LSTM模型？
我定义的模型
seq_length = 100
batch_size = 64
epochs = 3
vocab_size = len(vocab) # 65
embedding_dim = 256
rnn_units = 1024
def bi_lstm(vocab_size, embedding_dim, batch_size, rnn_units):
model = keras.models.Sequential([
keras.layers.Embedding(vocab_size, embedding_dim,
batch_input_shape = (batch_size, None)),
keras.layers.Bidirectional(
keras.layers.LSTM(units = rnn_units,
return_sequences = True,
stateful = True,
recurrent_initializer = "glorot_uniform"
)),
keras.layers.Dense(vocab_size),
])
return model
我做了一个简单的测试，它向我显示了错误。
for x, y in seq_dataset.take(1):
x = x[:-10,:] # change the batch size from 64 to 54, it worked well if I del this line
print(x.shape)
pred = model(x)
print(pred.shape)
InvalidArgumentError Traceback (most recent call last)
<ipython-input-98-99323ee3e09d> in <module>()
2 x = x[:-10,:]
3 print(x.shape)
----> 4 pred = model(x)
5 print(pred.shape)
14 frames
/tensorflow-2.0.0/python3.6/tensorflow_core/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
889 with base_layer_utils.autocast_context_manager(
890 self._compute_dtype):
--> 891 outputs = self.call(cast_inputs, *args, **kwargs)
892 self._handle_activity_regularization(inputs, outputs)
893 self._set_mask_metadata(inputs, outputs, input_masks)
/tensorflow-2.0.0/python3.6/tensorflow_core/python/keras/engine/sequential.py in call(self, inputs, training, mask)
254 if not self.built:
255 self._init_graph_network(self.inputs, self.outputs, name=self.name)
--> 256 return super(Sequential, self).call(inputs, training=training, mask=mask)
257
258 outputs = inputs # handle the corner case where self.layers is empty
/tensorflow-2.0.0/python3.6/tensorflow_core/python/keras/engine/network.py in call(self, inputs, training, mask)
706 return self._run_internal_graph(
707 inputs, training=training, mask=mask,
--> 708 convert_kwargs_to_constants=base_layer_utils.call_context().saving)
709
710 def compute_output_shape(self, input_shape):
/tensorflow-2.0.0/python3.6/tensorflow_core/python/keras/engine/network.py in _run_internal_graph(self, inputs, training, mask, convert_kwargs_to_constants)
858
859 # Compute outputs.
--> 860 output_tensors = layer(computed_tensors, **kwargs)
861
862 # Update tensor_dict.
/tensorflow-2.0.0/python3.6/tensorflow_core/python/keras/layers/wrappers.py in __call__(self, inputs, initial_state, constants, **kwargs)
526
527 if initial_state is None and constants is None:
--> 528 return super(Bidirectional, self).__call__(inputs, **kwargs)
529
530 # Applies the same workaround as in `RNN.__call__`
/tensorflow-2.0.0/python3.6/tensorflow_core/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
889 with base_layer_utils.autocast_context_manager(
890 self._compute_dtype):
--> 891 outputs = self.call(cast_inputs, *args, **kwargs)
892 self._handle_activity_regularization(inputs, outputs)
893 self._set_mask_metadata(inputs, outputs, input_masks)
/tensorflow-2.0.0/python3.6/tensorflow_core/python/keras/layers/wrappers.py in call(self, inputs, training, mask, initial_state, constants)
640
641 y = self.forward_layer(forward_inputs,
--> 642 initial_state=forward_state, **kwargs)
643 y_rev = self.backward_layer(backward_inputs,
644 initial_state=backward_state, **kwargs)
/tensorflow-2.0.0/python3.6/tensorflow_core/python/keras/layers/recurrent.py in __call__(self, inputs, initial_state, constants, **kwargs)
621
622 if initial_state is None and constants is None:
--> 623 return super(RNN, self).__call__(inputs, **kwargs)
624
625 # If any of `initial_state` or `constants` are specified and are Keras
/tensorflow-2.0.0/python3.6/tensorflow_core/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
889 with base_layer_utils.autocast_context_manager(
890 self._compute_dtype):
--> 891 outputs = self.call(cast_inputs, *args, **kwargs)
892 self._handle_activity_regularization(inputs, outputs)
893 self._set_mask_metadata(inputs, outputs, input_masks)
/tensorflow-2.0.0/python3.6/tensorflow_core/python/keras/layers/recurrent_v2.py in call(self, inputs, mask, training, initial_state)
959 if can_use_gpu:
960 last_output, outputs, new_h, new_c, runtime = cudnn_lstm(
--> 961 **cudnn_lstm_kwargs)
962 else:
963 last_output, outputs, new_h, new_c, runtime = standard_lstm(
/tensorflow-2.0.0/python3.6/tensorflow_core/python/keras/layers/recurrent_v2.py in cudnn_lstm(inputs, init_h, init_c, kernel, recurrent_kernel, bias, mask, time_major, go_backwards)
1172 outputs, h, c, _ = gen_cudnn_rnn_ops.cudnn_rnn(
1173 inputs, input_h=init_h, input_c=init_c, params=params, is_training=True,
-> 1174 rnn_mode='lstm')
1175
1176 last_output = outputs[-1]
/tensorflow-2.0.0/python3.6/tensorflow_core/python/ops/gen_cudnn_rnn_ops.py in cudnn_rnn(input, input_h, input_c, params, rnn_mode, input_mode, direction, dropout, seed, seed2, is_training, name)
107 input_mode=input_mode, direction=direction, dropout=dropout,
108 seed=seed, seed2=seed2, is_training=is_training, name=name,
--> 109 ctx=_ctx)
110 except _core._SymbolicException:
111 pass # Add nodes to the TensorFlow graph.
/tensorflow-2.0.0/python3.6/tensorflow_core/python/ops/gen_cudnn_rnn_ops.py in cudnn_rnn_eager_fallback(input, input_h, input_c, params, rnn_mode, input_mode, direction, dropout, seed, seed2, is_training, name, ctx)
196 "is_training", is_training)
197 _result = _execute.execute(b"CudnnRNN", 4, inputs=_inputs_flat,
--> 198 attrs=_attrs, ctx=_ctx, name=name)
199 _execute.record_gradient(
200 "CudnnRNN", _inputs_flat, _attrs, _result, name)
/tensorflow-2.0.0/python3.6/tensorflow_core/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
65 else:
66 message = e.message
---> 67 six.raise_from(core._status_to_exception(e.code, message), None)
68 except TypeError as e:
69 keras_symbolic_tensors = [
/usr/local/lib/python3.6/dist-packages/six.py in raise_from(value, from_value)
InvalidArgumentError: Invalid input_h shape: [1,64,1024] [1,54,1024] [Op:CudnnRNN]
答案 0 :(得分：1)
当stateful=True时，确实需要batch_size才能使模型的逻辑正常工作。
但是，模型的权重根本不需要知道batch_size。因此，如果有一些set_batch_size()方法，甚至更好，如果fit()和predict()可以从输入中派生它，那将是更好的选择。但不幸的是，情况并非如此。
但是有一种解决方法：只需定义该模型的另一个实例并指定batch_size=1（或您想要的任何数字）即可。然后，只需将训练后的模型的权重分配给具有不同批次大小的新模型：
model64 = bi_lstm(vocab_size, embedding_dim, batch_size=64, rnn_units=rnn_units)
model64.fit(...)
# optional: model64.save_weights('model64_weights.hdf5')
model1 = bi_lstm(vocab_size, embedding_dim, batch_size=1, rnn_units=rnn_units)
model1.set_weights(model64.get_weights()) # or: model1.load_weights('model64_weights.hdf5')
model1.predict(...)
之所以可行，是因为batch_size根本不参与权重的形状，因此它们是可以互换的。