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

## Question:

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.

## Answers:

input_shape is supposed to be (timesteps, n_features). Remove the first dimension.

```
input_shape = (95000,360)
```

Same for the output.

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)
```

Well, I think the main problem out there is with the `return_sequences`

parameter in the network.This hyper parameter should be set to `False`

for the *last layer* and `true`

for the other *previous layers*.

Adding up to the answers above,

`return_sequences`

should not be set True in all layers, just do not set it in the last layer, and omit `return_sequences=True`

In Artifical Neural Networks (ANN), input is of shape (N,D), where N is the number of samples and D is the number of features.

IN RNN, GRU and LSTM, input is of shape (N,T,D), where N is the number of samples, T is length of time sequence and D is the number of features.

So, while adding layers

Input(shape = (D,)) for ANN and

Input(shape = (T,D)) for RNN, GRU and LSTMs