Let me explain it via an example: So let’s say you have the following series: 1,2,3,4,5,6,…,100. You have to decide how many timesteps your lstm will learn, and reshape your data as so. Like below: if you decide time_steps = 5, you have to reshape your time series as a matrix of samples in this … Read more
I solved the problem by making input size: (95000,360,1) and output size: (95000,22) and changed the input shape to (360,1) in the code where model is defined: model = Sequential() model.add(LSTM(22, input_shape=(360,1))) model.add(Dense(22, activation=’softmax’)) model.compile(loss=”categorical_crossentropy”, optimizer=”adam”, metrics=[‘accuracy’]) print(model.summary()) model.fit(ml2_train_input, ml2_train_output_enc, epochs=2, batch_size=500)
In general, when you shuffle the training data (a set of sequences), you shuffle the order in which sequences are fed to the RNN, you don’t shuffle the ordering within individual sequences. This is fine to do when your network is stateless: Stateless Case: The network’s memory only persists for the duration of a sequence. … Read more
The problem is from my training loop: it doesn’t detach or repackage the hidden state in between batches? If so, then loss.backward() is trying to back-propagate all the way through to the start of time, which works for the first batch but not for the second because the graph for the first batch has been … Read more
You need to install tqdm module, you can do it by using python pip. pip install tqdm for more info tqdm
From this brilliant article num_units can be interpreted as the analogy of hidden layer from the feed forward neural network. The number of nodes in hidden layer of a feed forward neural network is equivalent to num_units number of LSTM units in a LSTM cell at every time step of the network. See the image … Read more
I am not clear about the embedding procedure. But still here is a way to implement a variable-length input LSTM. Just do not specify the timespan dimension when building LSTM. import keras.backend as K from keras.layers import LSTM, Input I = Input(shape=(None, 200)) # unknown timespan, fixed feature size lstm = LSTM(20) f = K.function(inputs=[I], … Read more
The LSTM layer and the TimeDistributed wrapper are two different ways to get the “many to many” relationship that you want. LSTM will eat the words of your sentence one by one, you can chose via “return_sequence” to outuput something (the state) at each step (after each word processed) or only output something after the … Read more
Difference between CNN and RNN are as follows: CNN: CNN takes a fixed size inputs and generates fixed-size outputs. CNN is a type of feed-forward artificial neural network – are variations of multilayer perceptrons which are designed to use minimal amounts of preprocessing. CNNs use connectivity pattern between its neurons and is inspired by the … Read more
LSTM in its core, preserves information from inputs that has already passed through it using the hidden state. Unidirectional LSTM only preserves information of the past because the only inputs it has seen are from the past. Using bidirectional will run your inputs in two ways, one from past to future and one from future … Read more