# Error when checking model input: expected lstm_1_input to have 3 dimensions, but got array with shape (339732, 29)

## Question:

My input is simply a csv file with 339732 rows and two columns :

- the first being 29 feature values, i.e. X
- the second being a binary label value, i.e. Y

I am trying to train my data on a stacked LSTM model:

```
data_dim = 29
timesteps = 8
num_classes = 2
model = Sequential()
model.add(LSTM(30, return_sequences=True,
input_shape=(timesteps, data_dim))) # returns a sequence of vectors of dimension 30
model.add(LSTM(30, return_sequences=True)) # returns a sequence of vectors of dimension 30
model.add(LSTM(30)) # return a single vector of dimension 30
model.add(Dense(1, activation='softmax'))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
model.summary()
model.fit(X_train, y_train, batch_size = 400, epochs = 20, verbose = 1)
```

This throws the error:

Traceback (most recent call last):

File “first_approach.py”, line 80, in

model.fit(X_train, y_train, batch_size = 400, epochs = 20, verbose = 1)ValueError: Error when checking model input: expected lstm_1_input to

have 3 dimensions, but got array with shape (339732, 29)

I tried reshaping my input using `X_train.reshape((1,339732, 29))`

but it did not work showing error:

ValueError: Error when checking model input: expected lstm_1_input to

have shape (None, 8, 29) but got array with shape (1, 339732, 29)

How can I feed in my input to the LSTM ?

## Answers:

Setting `timesteps = 1`

(since, I want one timestep for each instance) and reshaping the X_train and X_test as:

```
import numpy as np
X_train = np.reshape(X_train, (X_train.shape[0], 1, X_train.shape[1]))
X_test = np.reshape(X_test, (X_test.shape[0], 1, X_test.shape[1]))
```

This worked!

For `timesteps != 1`

, you can use the below function (adapted from here)

```
import numpy as np
def create_dataset(dataset, look_back=1):
dataX, dataY = [], []
for i in range(len(dataset)-look_back+1):
a = dataset[i:(i+look_back), :]
dataX.append(a)
dataY.append(dataset[i + look_back - 1, :])
return np.array(dataX), np.array(dataY)
```

Examples

```
X = np.reshape(range(30),(3,10)).transpose()
array([[ 0, 10, 20],
[ 1, 11, 21],
[ 2, 12, 22],
[ 3, 13, 23],
[ 4, 14, 24],
[ 5, 15, 25],
[ 6, 16, 26],
[ 7, 17, 27],
[ 8, 18, 28],
[ 9, 19, 29]])
create_dataset(X, look_back=1 )
(array([[[ 0, 10, 20]],
[[ 1, 11, 21]],
[[ 2, 12, 22]],
[[ 3, 13, 23]],
[[ 4, 14, 24]],
[[ 5, 15, 25]],
[[ 6, 16, 26]],
[[ 7, 17, 27]],
[[ 8, 18, 28]],
[[ 9, 19, 29]]]),
array([[ 0, 10, 20],
[ 1, 11, 21],
[ 2, 12, 22],
[ 3, 13, 23],
[ 4, 14, 24],
[ 5, 15, 25],
[ 6, 16, 26],
[ 7, 17, 27],
[ 8, 18, 28],
[ 9, 19, 29]]))
create_dataset(X, look_back=3)
(array([[[ 0, 10, 20],
[ 1, 11, 21],
[ 2, 12, 22]],
[[ 1, 11, 21],
[ 2, 12, 22],
[ 3, 13, 23]],
[[ 2, 12, 22],
[ 3, 13, 23],
[ 4, 14, 24]],
[[ 3, 13, 23],
[ 4, 14, 24],
[ 5, 15, 25]],
[[ 4, 14, 24],
[ 5, 15, 25],
[ 6, 16, 26]],
[[ 5, 15, 25],
[ 6, 16, 26],
[ 7, 17, 27]],
[[ 6, 16, 26],
[ 7, 17, 27],
[ 8, 18, 28]],
[[ 7, 17, 27],
[ 8, 18, 28],
[ 9, 19, 29]]]),
array([[ 2, 12, 22],
[ 3, 13, 23],
[ 4, 14, 24],
[ 5, 15, 25],
[ 6, 16, 26],
[ 7, 17, 27],
[ 8, 18, 28],
[ 9, 19, 29]]))
```

Reshape input for LSTM:

```
X = array([[10, 20, 30], [40, 50, 60], [70, 80, 90]])
X_train = X.reshape(1, 3, 3) # X.reshape(samples, timesteps, features)
```