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Kenil Vasani
Kenil Vasani

Kenil Vasani

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Asked: 2020-12-11T20:38:40+00:00 In:

ValueError: Input 0 is incompatible with layer lstm_13: expected ndim=3, found ndim=4

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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 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)

The error is:

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)))

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

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    1 Answer

    1. Rohit Patel

      Rohit Patel

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      Best Answer

      I solved the problem by making

      input size: (95000,360,1) and
      output size: (95000,22)

      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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