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:
train_input.shape= (1, 95000, 360) (95000 length input array with each element being an array of 360 length)
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 in line: model.add(LSTM(22, input_shape=(1, 95000,360)))
请帮帮我，我无法通过其他答案解决。
最佳答案
我通过制作解决了这个问题
input size: (95000,360,1) and output size: (95000,22)
并在定义模型的代码中将输入形状更改为 (360,1):
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)
关于python - ValueError : Input 0 is incompatible with layer lstm_13: expected ndim=3, 发现 ndim=4，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/44583254/