I have read plenty of posts for this point. They are inconsistent with each other and every answer seems to have a different explanation so I thought to ask based on my analyzing of all of them.
As Keras RNN documentation states, the input shape is always in this form (batch_size, timesteps, input_dim). I am a bit confused about that but I guess, not sure though, that input_dim is always 1 while timesteps depends on your problem (could be the data dimension as well). Is that roughly correct?
The reason for this question is that I always get an error when trying to change the value of input_dim to be my dataset dimension (as input_dim sounds like that!!), so I made an assumption that input_dim represent the shape of the input vector to LSTM at a time. Am I wrong again?
C = C.reshape((C.shape[0], C.shape[1], 1))
tr_C, ts_C, tr_r, ts_r = train_test_split(C, r, train_size=.8)
batch_size = 1000
print('Build model...')
model = Sequential()
model.add(LSTM(8, batch_input_shape=(batch_size, C.shape[1], 1), stateful=True, activation='relu'))
model.add(Dense(1, activation='relu'))
print('Training...')
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(tr_C, tr_r,
batch_size=batch_size, epochs=1,
shuffle=True, validation_data=(ts_C, ts_r))
Thanks!
Indeed, input_dim is the shape of the input vector at a time. In other words, input_dim is the number of the input features.
It's not necessarily 1, though. If you're working with more than one var, it can be any number.
Suppose you have 10 sequences, each sequence has 200 time steps, and you're measuring just a temperature. Then you have one feature:
input_shape = (200,1) -- notice that the batch size (number of sequences) is ignored herebatch_input_shape = (10,200,1) -- only in specific cases, like stateful = True, you will need a batch input shape.Now suppose you're measuring not only temperature, but also pressure and volume. Now you've got three input features:
input_shape = (200,3)batch_input_shape = (10,200,3)In other words, the first dimension is the number of different sequences. The second is the length of the sequence (how many measures along time). And the last is how many vars at each time.
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