How to Build an LSTM Classifier Model in Keras - keras

Question

I am new to Keras and RNN
I need to build a Classifier Model using LSTM RNN in Keras for a Dataset that contain a train set of shape (1795575, 6) and labels array of shape (1795575, 1).The labels is 11 class (from 0 to 10)
The test set of shape (575643, 6) and Labels array of shape (575643, 1.Again, the labels is 11 (from 0 to 10)
How can I shape the following Keras Model to satisfy my Dataset.What Values should I put for ?
from keras.models import Sequential
from keras.layers import LSTM, Dense
from keras.optimizers import SGD
import numpy as np
data_dim = ?
timesteps = ?
num_classes = ?
batch_size = ?
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model = Sequential()
model.add(LSTM(32, return_sequences=True, stateful=True,batch_input_shape=
(batch_size, timesteps, data_dim)))
model.add(LSTM(32, return_sequences=True, stateful=True))
model.add(LSTM(32, stateful=True))
model.add(Dense(?, activation='softmax'))
model.compile(loss='sparse_categorical_crossentropy',optimizer='sgd',
metrics=['accuracy'])
model.fit(train_X_arr, train_y_arr,batch_size=batch_size, epochs=epochs,
shuffle=False,validation_data=(test_X_arr, test_y_arr))
I appreciate your help and Thanks in advance

Answer 1

What you would like to do is this:
from keras.models import Sequential
from keras.layers import LSTM, Dense
from keras.optimizers import SGD
import numpy as np
data_dim = 1 # EACH TIMESTAMP IS SCALAR SO SHAPE=1
timesteps = 6 # EACH EXAMPLE CONTAINS 6 TIMESTAMPS
num_classes = 1 # EACH LABEL IS ONE NUMBER SO SHAPE=1
batch_size = 1 # TAKE SIZE THAT CAN DIVIDE THE NUMBER OF EXAMPLES IN THE TRAIN DATA. THE HIGHER THE BATCH SIZE THE BETTER!
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model = Sequential()
model.add(LSTM(32, return_sequences=True, stateful=True,batch_input_shape=
(batch_size, timesteps, data_dim)))
model.add(LSTM(32, return_sequences=True, stateful=True))
model.add(LSTM(32, stateful=True))
model.add(Dense(1, activation='softmax')) # AT THE END YOU WANT ONE VALUE (LIKE THE LABELS) -> SO DENSE SHOULD OUTPUT 1 NODE
model.compile(loss='sparse_categorical_crossentropy',optimizer='sgd',
metrics=['accuracy'])
model.fit(train_X_arr, train_y_arr,batch_size=batch_size, epochs=epochs,
shuffle=False,validation_data=(test_X_arr, test_y_arr))
and that's it.
EDIT: In addition, make sure that you reshape your train data to be: (1795575, 6,1) -> 1795575 examples, each has 6 timestamps, each timestamps is scalar.
You can achieve that easily by using np.expand_dims(train_data,-1).