2D Convolutional Long-Short Term Memory (LSTM) layer. Similar to a normal LSTM, but the input and recurrent transformations are both convolutional. Corresponds to the ConvLSTM2D Keras layer .
ConvLSTM2D
layer.
The shape of the tensor must be [time, height, width, channel] or [time, channel, height, width] for data format channels_last and channels_first respectively.ConvLSTM2D
layer.
Note that if this port is connected, you also have to connect the second hidden state port.
The shape must be [height, width, channel] or [channel, height, width] depending on data format and the dimensionality of the channel dimension must match the number of filters of this layer.ConvLSTM2D
layer.
Note that if this port is connected, you also have to connect the first hidden state port.
The shape must be [height, width, channel] or [channel, height, width] depending on data format and the dimensionality of the channel dimension must match the number of filters of this layer.You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.
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