ValueError:输入 0 与层 lstm_13 不兼容:预期 ndim=3，发现 ndim=4

问题描述

我正在尝试多类分类，这里是我的训练输入和输出的详细信息:

I am trying for multi-class classification and here are the details of my training input and output:

train_input.shape= (1, 95000, 360) (95000 长度的输入数组，每个元素是一个长度为 360 的数组)

train_output.shape = (1, 95000, 22)(有 22 个类)

train_output.shape = (1, 95000, 22) (22 Classes are there)

model = Sequential()

model.add(LSTM(22, input_shape=(1, 95000,360)))
model.add(Dense(22, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
print(model.summary())
model.fit(train_input, train_output, epochs=2, batch_size=500)

错误是:

ValueError: Input 0 is incompatible with layer lstm_13: expected ndim=3, found ndim=4排队:model.add(LSTM(22, input_shape=(1, 95000,360)))

请帮帮我，我无法通过其他答案解决.

Please help me out, I am not able to solve it through other answers.

推荐答案

我通过制作

输入大小:(95000,360,1) 和输出大小:(95000,22)

并在定义模型的代码中将输入形状更改为 (360,1):

and changed the input shape to (360,1) in the code where model is defined:

model = Sequential()
model.add(LSTM(22, input_shape=(360,1)))
model.add(Dense(22, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
print(model.summary())
model.fit(ml2_train_input, ml2_train_output_enc, epochs=2, batch_size=500)

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