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.

## Rohit Patel

I solved the problem by making

and changed the

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