值：LSTM_14层的输入0与图层不兼容：预期ndim = 3，找到ndim = 4。收到完整的形状：[NONE，12，12，64]
我正在使用CNN-LSTM网络进行图像分类。我的图像大小为（224、224、3），批处理大小为90。当我将输入传递到LSTM层时，我会遇到此错误。以下是我的代码片段：
input1 = Input(shape=(224, 224,3))
x = Conv2D(8, (3,3), activation ="relu")(input1)
x = MaxPooling2D(2,2)(x)
x = Conv2D(16, (3,3), activation ="relu")(x)
x = MaxPooling2D(2,2)(x)
x = Conv2D(32, (3,3), activation ="relu")(x)
x = MaxPooling2D(2,2)(x)
x = Conv2D(64, (3,3), activation ="relu")(x)
x = Dropout(0.2)(x)
x = MaxPooling2D(2,2)(x)
x = LSTM(units= 64, activation= 'tanh', input_shape= [None, 144], return_sequences = True)(x)
error:
---> 10 x = LSTM(units= 64, activation= 'tanh', input_shape= [None, 144], return_sequences = True)(x)
ValueError: Input 0 of layer lstm_14 is incompatible with the layer: expected ndim=3, found ndim=4. Full shape received: [None, 12, 12, 64]
感谢有人可以对我的问题进行整理。
I am using CNN-LSTM network for image classification. My image size is (224, 224, 3) and batch size is 90. I m getting this error when i passing input to LSTM layer. Following is my code snippet:
input1 = Input(shape=(224, 224,3))
x = Conv2D(8, (3,3), activation ="relu")(input1)
x = MaxPooling2D(2,2)(x)
x = Conv2D(16, (3,3), activation ="relu")(x)
x = MaxPooling2D(2,2)(x)
x = Conv2D(32, (3,3), activation ="relu")(x)
x = MaxPooling2D(2,2)(x)
x = Conv2D(64, (3,3), activation ="relu")(x)
x = Dropout(0.2)(x)
x = MaxPooling2D(2,2)(x)
x = LSTM(units= 64, activation= 'tanh', input_shape= [None, 144], return_sequences = True)(x)
error:
---> 10 x = LSTM(units= 64, activation= 'tanh', input_shape= [None, 144], return_sequences = True)(x)
ValueError: Input 0 of layer lstm_14 is incompatible with the layer: expected ndim=3, found ndim=4. Full shape received: [None, 12, 12, 64]
Thanks if someone can sort my issue.
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根据 documentation>文档页面以下形状：
[批处理，时间段，功能]。您的LSTM层接收到形状的输入[NONE，12，12，64]，这就是为什么您获得有关3D/4D形状的错误的原因。您需要重塑张量：[NONE，12，12，64] - ＆GT; [无，144，64]。为此，您可以插入reshapelayer 您的最后一个maxpooling2d和lstm层。According to the documentation page, LSTM layer input should have the following shape:
[batch, timesteps, feature]. Your LSTM layer receives input of shape[None, 12, 12, 64], this is why you obtain an error about 3D/4D shapes. You need to reshape your tensor:[None, 12, 12, 64] -> [None, 144, 64]. To do this, you can insertReshapelayer between your lastMaxPooling2DandLSTMlayers.