Question

我正在尝试建立用于文本分类的lstm模型，但收到错误。这是我尝试过的全部代码。

请让我知道错误背后的原因以及如何解决该错误。

input1.shape # text data integer coded
(37788, 130)
input2.shape # multiple category columns(one hot encoded) concatenated together
(37788, 104)

train_data = [input1, input2] # this is the train data.

i1 = Input(shape=(130,), name='input')
embeddings = Embedding(input_dim=20000, output_dim=100, input_length=130)(i1)
lstm = LSTM(100)(embeddings)
flatten = Flatten()(lstm)

i2  = Input(shape=(None, 104))
c1 = Conv1D(64, 2, padding='same', activation='relu', kernel_initializer='he_uniform')(i2)
c2 = Conv1D(32, kernel_size=3, activation='relu', kernel_initializer='he_uniform')(c1)
flatten1 = Flatten()(c2)

concat = concatenate([flatten, flatten1])
dense1 = Dense(32, 'relu', kernel_initializer='he_uniform')(concat)

我尝试打印conv1d层的形状，但对于扁平层却没有显示。我认为这可能是错误的原因。

Tensor("conv1d_81/Identity:0", shape=(None, None, 64), dtype=float32)
Tensor("conv1d_82/Identity:0", shape=(None, None, 32), dtype=float32)
Tensor("flatten_106/Identity:0", shape=(None, None), dtype=float32)

这是我遇到的错误。如何解决？

---------------------------------------------------------------------------
    ValueError                                Traceback (most recent call last)
    <ipython-input-531-31a53fbf3d37> in <module>
         14 concat = concatenate([flatten, flatten1])
    ---> 15 dense1 = Dense(32, 'relu', kernel_initializer='he_uniform')(concat)
         16 drop = Dropout(0.5)(dense1)

    ~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in __call__(self, inputs, *args, **kwargs)
        614           # Build layer if applicable (if the `build` method has been
        615           # overridden).
    --> 616           self._maybe_build(inputs)
        617 
        618           # Wrapping `call` function in autograph to allow for dynamic control

    ~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in _maybe_build(self, inputs)
       1964         # operations.
       1965         with tf_utils.maybe_init_scope(self):
    -> 1966           self.build(input_shapes)
       1967       # We must set self.built since user defined build functions are not
       1968       # constrained to set self.built.

    ~\Anaconda3\lib\site-packages\tensorflow\python\keras\layers\core.py in build(self, input_shape)
       1003     input_shape = tensor_shape.TensorShape(input_shape)
       1004     if tensor_shape.dimension_value(input_shape[-1]) is None:
    -> 1005       raise ValueError('The last dimension of the inputs to `Dense` '
       1006                        'should be defined. Found `None`.')
       1007     last_dim = tensor_shape.dimension_value(input_shape[-1])

    ValueError: The last dimension of the inputs to `Dense` should be defined. Found `None`.

Answer 1

第二个模型中序列的长度为None。

i2  = Input(shape=(None, 104))

您不能展平可变长度并具有已知大小。
您需要Dense的已知大小。

或者使用固定长度而不是None，或者使用GlobalMaxPooling1D或GlobalAveragePooling1D而不是Flatten。

Answer 2

对我来说，问题在于在输入函数中使用Tensor之前我没有对其进行重塑

image = tf.reshape(image, [400,400,3])

错误：ValueError：应定义“密集”输入的最后维度。找不到`

2 个答案: