我正在尝试使用序列对单词进行序列化模型，但是在训练过程中，我不断收到无效的参数错误。我不知道我在做什么错。请帮帮我。
下面是一个示例代码，可重现我得到的错误。
我正在使用， tensorflow 2.0.0，cudatoolkit 10.0.130，cudnn 7.6.4
import numpy as np
from tensorflow.keras.utils import Sequence
from tensorflow.keras.layers import Input, LSTM, Embedding
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Model
x = ['this is really good',
'i am feeling better',
'yesterday was a bad day',
'today is better']
y = ['<sos> Ceci est vraiment bon <eos>',
'<sos> je me sens mieux <eos>',
'<sos> hier était une mauvaise journée <eos>',
"<sos> aujourd'hui c`est mieux <eos>"]
x_dict = dict(zip(np.unique(np.hstack([i.split() for i in x])), range(1, 16)))
y_dict = dict(zip(np.unique(np.hstack([i.split() for i in y])), range(1, 18)))
MAX_LEN_X = 5
MAX_LEN_Y = 7
encoder_input = np.zeros((4, MAX_LEN_X), dtype='float32')
for i, j in enumerate(x):
for k, l in enumerate(j.split()):
encoder_input[i, k] = x_dict[l]
decoder_input = np.zeros((4, MAX_LEN_Y), dtype='float32')
decoder_output = np.zeros((4, MAX_LEN_Y, len(y_dict)+1), dtype='float32')
for i, j in enumerate(y):
for k, l in enumerate(j.split()):
decoder_input[i, k] = y_dict[l]
if k > 0:
decoder_output[i, k - 1, y_dict[l]] = 1.
latent_dim = 30
INPUT_VOCAB = len(x_dict)
TARGET_VOCAB = len(y_dict) + 1
# Encoder
encoder_inputs = Input(shape=(None,))
encoder_emb = Embedding(INPUT_VOCAB, latent_dim, mask_zero=True)(encoder_inputs)
encoder_lstm = LSTM(latent_dim, return_state=True)
encoder_outputs, state_h, state_c = encoder_lstm(encoder_emb)
encoder_states = [state_h, state_c]
# Decoder
decoder_inputs = Input(shape=(None,))
dec_emb_layer = Embedding(TARGET_VOCAB, latent_dim, mask_zero=True)
decoder_emb = dec_emb_layer(decoder_inputs)
decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True)
decoder_outputs, _, _ = decoder_lstm(decoder_emb, initial_state=encoder_states)
decoder_dense = Dense(TARGET_VOCAB, activation='softmax')
decoder_outputs = decoder_dense(decoder_outputs)
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
model.summary()
这是我得到的错误的完整回溯：
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-2-f704c3b2a0b8> in <module>
2 epochs=10,
3 verbose=1,
----> 4 shuffle=True)
~/miniconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
726 max_queue_size=max_queue_size,
727 workers=workers,
--> 728 use_multiprocessing=use_multiprocessing)
729
730 def evaluate(self,
~/miniconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, **kwargs)
322 mode=ModeKeys.TRAIN,
323 training_context=training_context,
--> 324 total_epochs=epochs)
325 cbks.make_logs(model, epoch_logs, training_result, ModeKeys.TRAIN)
326
~/miniconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py in run_one_epoch(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)
121 step=step, mode=mode, size=current_batch_size) as batch_logs:
122 try:
--> 123 batch_outs = execution_function(iterator)
124 except (StopIteration, errors.OutOfRangeError):
125 # TODO(kaftan): File bug about tf function and errors.OutOfRangeError?
~/miniconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py in execution_function(input_fn)
84 # `numpy` translates Tensors to values in Eager mode.
85 return nest.map_structure(_non_none_constant_value,
---> 86 distributed_function(input_fn))
87
88 return execution_function
~/miniconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in __call__(self, *args, **kwds)
455
456 tracing_count = self._get_tracing_count()
--> 457 result = self._call(*args, **kwds)
458 if tracing_count == self._get_tracing_count():
459 self._call_counter.called_without_tracing()
~/miniconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in _call(self, *args, **kwds)
518 # Lifting succeeded, so variables are initialized and we can run the
519 # stateless function.
--> 520 return self._stateless_fn(*args, **kwds)
521 else:
522 canon_args, canon_kwds = \
~/miniconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in __call__(self, *args, **kwargs)
1821 """Calls a graph function specialized to the inputs."""
1822 graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
-> 1823 return graph_function._filtered_call(args, kwargs) # pylint: disable=protected-access
1824
1825 @property
~/miniconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _filtered_call(self, args, kwargs)
1139 if isinstance(t, (ops.Tensor,
1140 resource_variable_ops.BaseResourceVariable))),
-> 1141 self.captured_inputs)
1142
1143 def _call_flat(self, args, captured_inputs, cancellation_manager=None):
~/miniconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
1222 if executing_eagerly:
1223 flat_outputs = forward_function.call(
-> 1224 ctx, args, cancellation_manager=cancellation_manager)
1225 else:
1226 gradient_name = self._delayed_rewrite_functions.register()
~/miniconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in call(self, ctx, args, cancellation_manager)
509 inputs=args,
510 attrs=("executor_type", executor_type, "config_proto", config),
--> 511 ctx=ctx)
512 else:
513 outputs = execute.execute_with_cancellation(
~/miniconda3/lib/python3.7/site-packages/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 = [
~/miniconda3/lib/python3.7/site-packages/six.py in raise_from(value, from_value)
InvalidArgumentError: 2 root error(s) found.
(0) Invalid argument: indices[3,0] = 14 is not in [0, 14)
[[node model/embedding/embedding_lookup (defined at /home/user83/miniconda3/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py:1751) ]]
[[loss/dense_loss/weighted_loss/broadcast_weights/assert_broadcastable/AssertGuard/else/_13/Assert/data_2/_92]]
(1) Invalid argument: indices[3,0] = 14 is not in [0, 14)
[[node model/embedding/embedding_lookup (defined at /home/user83/miniconda3/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py:1751) ]]
0 successful operations.
0 derived errors ignored. [Op:__inference_distributed_function_16237]
Function call stack:
distributed_function -> distributed_function
答案 0 :(得分：2)
您的代码中的错误很少。
如果您进行print(x_dict)操作，将会发现您的词汇量从1开始并上升到某个值（例如n）。现在，将INPUT_VOCAB设置为len(x_dict)。这使您的Embedding层缺少词汇表中最后一个单词的行。因此，只要您的模型遇到最后一个单词，您都会遇到embedding_look_up类型错误。因此，您需要设置INPUT_SIZE=len(x_dict)+1
None 我个人总是要避免这种情况。可以保留批次尺寸None。但是输出形状中有多个None是危险的。例如TensorFlow / Keras有时会进行分层重塑。如果您有多个None，您将无法恢复张量的原始形状（甚至可能不允许其进行重塑）。无论哪种方式，都不是最佳实践。因此，我在您的Input形状中设置了序列长度。
因此，更改后，您的代码将如下所示。
import numpy as np
from tensorflow.keras.utils import Sequence
from tensorflow.keras.layers import Input, LSTM, Embedding
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Model
x = ['this is really good',
'i am feeling better',
'yesterday was a bad day',
'today is better']
y = ['<sos> Ceci est vraiment bon <eos>',
'<sos> je me sens mieux <eos>',
'<sos> hier était une mauvaise journée <eos>',
"<sos> aujourd'hui c`est mieux <eos>"]
x_dict = dict(zip(np.unique(np.hstack([i.split() for i in x])), range(1, 16)))
y_dict = dict(zip(np.unique(np.hstack([i.split() for i in y])), range(1, 18)))
MAX_LEN_X = 5
MAX_LEN_Y = 7
encoder_input = np.zeros((4, MAX_LEN_X), dtype='float32')
for i, j in enumerate(x):
for k, l in enumerate(j.split()):
encoder_input[i, k] = x_dict[l]
decoder_input = np.zeros((4, MAX_LEN_Y), dtype='float32')
decoder_output = np.zeros((4, MAX_LEN_Y, len(y_dict)+1), dtype='float32')
for i, j in enumerate(y):
for k, l in enumerate(j.split()):
decoder_input[i, k] = y_dict[l]
if k > 0:
decoder_output[i, k - 1, y_dict[l]] = 1.
latent_dim = 30
INPUT_VOCAB = len(x_dict) + 1
TARGET_VOCAB = len(y_dict) + 1
print(MAX_LEN_X, MAX_LEN_Y)
# Encoder
encoder_inputs = Input(shape=(MAX_LEN_X,))
encoder_emb = Embedding(INPUT_VOCAB, latent_dim, mask_zero=True)(encoder_inputs)
encoder_lstm = LSTM(latent_dim, return_state=True)
encoder_outputs, state_h, state_c = encoder_lstm(encoder_emb)
encoder_states = [state_h, state_c]
# Decoder
decoder_inputs = Input(shape=(MAX_LEN_Y,))
dec_emb_layer = Embedding(TARGET_VOCAB, latent_dim, mask_zero=True)
decoder_emb = dec_emb_layer(decoder_inputs)
decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True)
decoder_outputs, _, _ = decoder_lstm(decoder_emb, initial_state=encoder_states)
decoder_dense = Dense(TARGET_VOCAB, activation='softmax')
decoder_outputs = decoder_dense(decoder_outputs)
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
model.summary()