You can get the output shape of a layer by layer.output_shape. for layer in model.layers: print(layer.output_shape) Gives you: (None, None, 64, 64, 40) (None, None, 64, 64, 40) (None, None, 64, 64, 40) (None, None, 64, 64, 40) (None, None, 64, 64, 40) (None, None, 64, 64, 40) (None, None, 64, 64, 40) (None, None, … Read more
I think this warning can be safely ignored as you can find the same warning even in a tutorial given by tensorflow. I often see this warning when saving custom models such as graph NNs. You should be good to go as long as you don’t want to access those non-callable functions. However, if you’re … Read more
Yes, you can emulate one by another, the reason for having them separate is efficiency. LSTMCell is a cell that takes arguments: Input of shape batch × input dimension; A tuple of LSTM hidden states of shape batch x hidden dimensions. It is a straightforward implementation of the equations. LSTM is a layer applying an … Read more
The output for the LSTM is the output for all the hidden nodes on the final layer. hidden_size – the number of LSTM blocks per layer. input_size – the number of input features per time-step. num_layers – the number of hidden layers. In total there are hidden_size * num_layers LSTM blocks. The input dimensions are … Read more
Setting timesteps = 1 (since, I want one timestep for each instance) and reshaping the X_train and X_test as: import numpy as np X_train = np.reshape(X_train, (X_train.shape[0], 1, X_train.shape[1])) X_test = np.reshape(X_test, (X_test.shape[0], 1, X_test.shape[1])) This worked!
As suggested by François Chollet (creator of Keras) in his book, one way to handle missing values is to replace them with zero: In general, with neural networks, it’s safe to input missing values as 0, with the condition that 0 isn’t already a meaningful value. The network will learn from exposure to the data … Read more
Unfortunately what you want to do is impossible with Keras … I’ve also struggle a lot of time on this problems and the only way is to dive into the rabbit hole and work with Tensorflow directly to do LSTM rolling prediction. First, to be clear on terminology, batch_size usually means number of sequences that … Read more
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
If there’s a mask in your model, it’ll be propagated layer-by-layer and eventually applied to the loss. So if you’re padding and masking the sequences in a correct way, the loss on the padding placeholders would be ignored. Some Details: It’s a bit involved to explain the whole process, so I’ll just break it down … 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)