How can I set up a keras model such that the final LSTM layer outputs a prediction for each time step while having variable sequence lengths as input?
I'd then like to provide labels for each of the timesteps after a dense layer with linear activation.
When I try to add a reshape or a dense layer to the LSTM model that is returning the full sequence and has a masking layer to take care of variable sequence lengths, it says:
The reshape and the dense layers do not support masking.
Would this be possible to do?
You can use the TimeDistributed layer wrapper for this. This applies the layer you want to each timestep. In your case, you could also just use TimeDistributedDense.
Related
How is dense layer changing the output coming from LSTM layer? How come that from 50 shaped output from previous layer i get output of size 1 from dense layer that is used for prediction?
Lets say i have this basic model:
model = Sequential()
model.add(LSTM(50,input_shape=(60,1)))
model.add(Dense(1, activation="softmax"))
Is the Dense layer taking the values coming from previous layer and assigning the probablity(using softmax function) of each of the 50 inputs and then taking it out as an output?
No, Dense layers do not work like that, the input has 50-dimensions, and the output will have dimensions equal to the number of neurons, one in this case. The output is a weighted linear combination of the input plus a bias.
Note that with the softmax activation, it makes no sense to use it with a one neuron layer, as the softmax is normalized, the only possible output will be constant 1.0. That's probably now what you want.
I'm new at keras lstm could you please explain to me this model.summary()
in rasa core training
![model after training][1]
Also, what is the Masking layer doing and what does the value -1 in it mean?
A Masking layer is meant to "ignore steps" in sequences.
Your LSTM is working with sequences of 5 steps and 42 features per step.
If all features in a step have the same value defined in Masking (-1 in the example), that step will be ignored during training.
The idea is to simulate variable length sequences.
Not sure exactly, what exactly you don't understand but model.summary()
prints a summary representation of your model. (keras.io)
It lists all layers used in the given model with its respective size.
This particular model obviously starts with a masking layer for input sequences (I guess because of padding) and is followed by the simplest LSTM model possible.
My question is does the this code make sense? And if this makes sense what should be the purpose?
model.add(LSTM(18, return_sequences=True,batch_input_shape=(batch_size,look_back,dim_x), stateful=True))
model.add(Dropout(0.3))
model.add(LSTM(50,return_sequences=False,stateful=False))
model.add(Dropout(0.3))
model.add(Dense(1, activation='linear'))
Because if my first LSTM layer returns my state from one batch to the next, why shouldn't do my second LSTM layer the same?
I'm having a hard time to understand the LSTM mechanics in Keras so I'm very thankful for any kind of help :)
And if you down vote this post could you tell me why in the commands? thanks.
Your program is a regression problem where your model consists of 2 lstm layers with 18 and 50 layers each and finally a dense layer to show the regression value.
LSTM requires a 3D input.Since the output of your first LSTM layer is going to the input for the second LSTM layer.The input of the Second LSTM layer should also be in 3D. so we set the retrun sequence as true in 1st as it will return a 3D output which can then be used as an input for the second LSTM.
Your second LSTMs value does not return a sequence because after the second LSTM you have a dense layer which does not need a 3D value as input.
[update]
In keras by default LSTM states are reset after each batch of training data,so if you don't want the states to be reset after each batch you can set the stateful=True. If LSTM is made stateful final state of a batch will be used as an initial state for the next batch.
You can later reset the states by calling reset_states()
Using RapidMiner I want to implement an LSTM to classify patternes in a time series. Input data is a flat table. My first layer in the Keras operator is a core reshape from exampleset_length x nr_of_attributes to batch x time-steps x features. In the reshape parameter I specifically enter three figures because I want a specific amount of features and time-steps. The only way to achieve this is to specify also batch size, so in total three figures. But when I add a RNN LSTM layer an error is returned: Input is incompatible with layer lstm expected ndim=n found ndim=n+1. What’s wrong?
When specifying 'input_shape' for the LSTM layer, you do not include the batch size.
So your 'input_shape' value should be (timesteps, input_dim).
Source: Keras RNN Layer, the parent layer for LSTM
How do you write a simple sequence copy task in keras using the LSTM architecture without an Embedding layer? I already have the word vectors.
If you say that you have word vectors, I guess you have a dictionary to map a word to its vector representation (calculated from word2vec, GloVe...).
Using this dictionary, you replace all words in your sequence by their corresponding vectors. You will also need to make all your sequences the same length, since LSTMs need all the input sequences to be of constant length. So you will need to determine a max_length value and trim all sequences that are longer and pad all sequences that are shorter with zeros (see Keras pad_sequences function).
Then you can pass the vectorized sequences directly to the LSTM layer of your neural network. Since the LSTM layer is the first layer of the network, you will need to define the input shape, which in your case is (max_length, embedding_dim, ).
This ways you skip the Embedding layer and use your own precomputed word vectors instead.
I had same problem after searching in Keras at "Stacked LSTM for sequence classification" part , I found following code might be useful:
model = Sequential()
model.add(LSTM(3NumberOfLSTM, return_sequences=True,
input_shape=(YourSequenceLenght, YourWord2VecLenght)))