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 ?

## Rohit Patel

Setting

`timesteps = 1`

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