I'm trying to use the example described in the Keras documentation named "Stacked LSTM for sequence classification" (see code below) and can't figure out the `input_shape`

parameter in the context of my data.

I have as input a matrix of sequences of 25 possible characters encoded in integers to a padded sequence of maximum length 31. As a result, my `x_train`

has the shape `(1085420, 31)`

meaning `(n_observations, sequence_length)`

.

```
from keras.models import Sequential
from keras.layers import LSTM, Dense
import numpy as np
data_dim = 16
timesteps = 8
num_classes = 10
# expected input data shape: (batch_size, timesteps, data_dim)
model = Sequential()
model.add(LSTM(32, return_sequences=True,
input_shape=(timesteps, data_dim))) # returns a sequence of vectors of dimension 32
model.add(LSTM(32, return_sequences=True)) # returns a sequence of vectors of dimension 32
model.add(LSTM(32)) # return a single vector of dimension 32
model.add(Dense(10, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
# Generate dummy training data
x_train = np.random.random((1000, timesteps, data_dim))
y_train = np.random.random((1000, num_classes))
# Generate dummy validation data
x_val = np.random.random((100, timesteps, data_dim))
y_val = np.random.random((100, num_classes))
model.fit(x_train, y_train,
batch_size=64, epochs=5,
validation_data=(x_val, y_val))
```

In this code `x_train`

has the shape `(1000, 8, 16)`

, as for an array of 1000 arrays of 8 arrays of 16 elements. There I get completely lost on what is what and how my data can reach this shape.

Looking at Keras doc and various tutorials and Q&A, it seems I'm missing something obvious. Can someone give me a hint of what to look for ?

Thanks for your help !

## Best Answer

LSTM shapes are tough so don't feel bad, I had to spend a couple days battling them myself:

If you will be feeding data 1 character at a time your input shape should be (31,1) since your input has 31 timesteps, 1 character each. You will need to reshape your x_train from (1085420, 31) to (1085420, 31,1) which is easily done with this command :