|TensorFlow 1 version||View source on GitHub|
2D Convolutional LSTM layer.
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tf.keras.layers.ConvLSTM2D( filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, dilation_rate=(1, 1), activation='tanh', recurrent_activation='hard_sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', unit_forget_bias=True, kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, return_sequences=False, return_state=False, go_backwards=False, stateful=False, dropout=0.0, recurrent_dropout=0.0, **kwargs )
A convolutional LSTM is similar to an LSTM, but the input transformations and recurrent transformations are both convolutional. This layer is typically used to process timeseries of images (i.e. video-like data).
It is known to perform well for weather data forecasting, using inputs that are timeseries of 2D grids of sensor values. It isn't usually applied to regular video data, due to its high computational cost.
||Integer, the dimensionality of the output space (i.e. the number of output fi|