Question

我正在使用生成器训练模型，并且从Tensorflow收到此警告，尽管我可以正确地训练模型，但我想解决这个问题，或者至少了解它为什么会发生。

我来自生成器的数据具有以下形状：

for x, y in model_generator(): # x[0] and x[1] are the inputs, y is the output
    print(x[0].shape, x[1].shape, y.shape)

# (20,)(20,)(20,17772) 
# 17772 --> Number of unique words in my datatset
# 20 --> Number of words per example (per sentence)

这是我的模特

Model: "functional_1"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            [(None, 20)]         0                                            
__________________________________________________________________________________________________
input_2 (InputLayer)            [(None, 20)]         0                                            
__________________________________________________________________________________________________
embedding (Embedding)           (None, 20, 50)       890850      input_1[0][0]                    
__________________________________________________________________________________________________
embedding_1 (Embedding)         (None, 20, 50)       890850      input_2[0][0]                    
__________________________________________________________________________________________________
lstm (LSTM)                     [(None, 64), (None,  29440       embedding[0][0]                  
__________________________________________________________________________________________________
lstm_1 (LSTM)                   (None, 20, 64)       29440       embedding_1[0][0]                
                                                                 lstm[0][1]                       
                                                                 lstm[0][2]                       
__________________________________________________________________________________________________
time_distributed (TimeDistribut (None, 20, 17772)    1155180     lstm_1[0][0]                     
==================================================================================================
Total params: 2,995,760
Trainable params: 1,214,060
Non-trainable params: 1,781,700
__________________________________________________________________________________________________
None

这是我在运行模型时收到的警告：

WARNING:tensorflow:Model was constructed with shape (None, 20) for input Tensor("input_1:0", shape=(None, 20), dtype=float32), but it was called on an input with incompatible shape (None, 1).
WARNING:tensorflow:Model was constructed with shape (None, 20) for input Tensor("input_2:0", shape=(None, 20), dtype=float32), but it was called on an input with incompatible shape (None, 1).
WARNING:tensorflow:Model was constructed with shape (None, 20) for input Tensor("input_1:0", shape=(None, 20), dtype=float32), but it was called on an input with incompatible shape (None, 1).
WARNING:tensorflow:Model was constructed with shape (None, 20) for input Tensor("input_2:0", shape=(None, 20), dtype=float32), but it was called on an input with incompatible shape (None, 1).

我不明白为什么会这样，输入的形状是（20，），所以应该正确，有任何建议吗？

编辑

生成器：

def model_generator():
    for index, output in enumerate(training_decoder_output):
        for i in range(size):
            yield ([training_encoder_input[size*index+i], training_decoder_input[size*index+i]], output[i])

# Generator, returns inputs and ouput one by one when calling 
# (I saved the outputs in chunks on disk so that's why I iterate over it in that way)

致电model.fit()：

model.fit(model_generator(), epochs=5)

training_encoder_input的示例：

print(training_encoder_input[:5])

[[   3 1516   10 3355 2798    1 9105    1 9106    4  162    1  411    1
  9107 3356  612    1 9108    1]
 [   0    0    0    0    0    0    0    0    0    0    0    2 9109 2799
  5632   29 1187    2  157  275]
 [   0   54 5633 5634    1  412 4199   12 9110 5633 5634   27  443  134
  1516    7    6 4200 1280    1]
 [  23 9112  816   11 9113   33  184 9114  816    1 9115   42    3    2
    57    5 2120    3  185    1]
 [   0    0    0    0    0    0   15  301 9116    3 3357    1 9117    1
    67 5635    4  110 5635    1]]

Answer 1

您输入的形状应为：

x[0].shape => (1,20,) # where 1 is batch size.

在模型None中为批次大小，因此此特定尺寸也应出现在您的x数据中。因此，您需要将生成的更新为：

def model_generator():
for index, output in enumerate(training_decoder_output):
    for i in range(size):
        yield ([np.expand_dims(training_encoder_input[size*index+i], axis=0), np.expand_dims(training_decoder_input[size*index+i]], axis=0), np.expand_dims(output[i], axis=0))

如果批处理大小超过一个，则创建一个元素列表/数组，形式为(bs,20,)，其中bs是批处理大小。

警告：tensorflow：使用输入Tensor（）的形状构造模型。但它在形状不兼容的输入上被调用

1 个答案: