tf.keras.layers.ConvLSTM2D     View source on GitHub    Convolutional LSTM. Inherits From: RNN, Layer, Module  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.keras.layers.ConvLSTM2D  
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
)
 It is similar to an LSTM layer, but the input transformations and recurrent transformations are both convolutional.
 


 Arguments
  filters   Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).  
  kernel_size   An integer or tuple/list of n integers, specifying the dimensions of the convolution window.  
  strides   An integer or tuple/list of n integers, specifying the strides of the convolution. Specifying any stride value != 1 is incompatible with specifying any dilation_rate value != 1.  
  padding   One of "valid" or "same" (case-insensitive). "valid" means no padding. "same" results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input.  
  data_format   A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch, time, ..., channels) while channels_first corresponds to inputs with shape (batch, time, channels, ...). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. If you never set it, then it will be "channels_last".  
  dilation_rate   An integer or tuple/list of n integers, specifying the dilation rate to use for dilated convolution. Currently, specifying any dilation_rate value != 1 is incompatible with specifying any strides value != 1.  
  activation   Activation function to use. By default hyperbolic tangent activation function is applied (tanh(x)).  
  recurrent_activation   Activation function to use for the recurrent step.  
  use_bias   Boolean, whether the layer uses a bias vector.  
  kernel_initializer   Initializer for the kernel weights matrix, used for the linear transformation of the inputs.  
  recurrent_initializer   Initializer for the recurrent_kernel weights matrix, used for the linear transformation of the recurrent state.  
  bias_initializer   Initializer for the bias vector.  
  unit_forget_bias   Boolean. If True, add 1 to the bias of the forget gate at initialization. Use in combination with bias_initializer="zeros". This is recommended in Jozefowicz et al., 2015  
  kernel_regularizer   Regularizer function applied to the kernel weights matrix.  
  recurrent_regularizer   Regularizer function applied to the recurrent_kernel weights matrix.  
  bias_regularizer   Regularizer function applied to the bias vector.  
  activity_regularizer   Regularizer function applied to.  
  kernel_constraint   Constraint function applied to the kernel weights matrix.  
  recurrent_constraint   Constraint function applied to the recurrent_kernel weights matrix.  
  bias_constraint   Constraint function applied to the bias vector.  
  return_sequences   Boolean. Whether to return the last output in the output sequence, or the full sequence. (default False)  
  return_state   Boolean Whether to return the last state in addition to the output. (default False)  
  go_backwards   Boolean (default False). If True, process the input sequence backwards.  
  stateful   Boolean (default False). If True, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch.  
  dropout   Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs.  
  recurrent_dropout   Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state.    Call arguments:  
inputs: A 5D tensor. 
mask: Binary tensor of shape (samples, timesteps) indicating whether a given timestep should be masked. 
training: Python boolean indicating whether the layer should behave in training mode or in inference mode. This argument is passed to the cell when calling it. This is only relevant if dropout or recurrent_dropout are set. 
initial_state: List of initial state tensors to be passed to the first call of the cell.  Input shape:  If data_format='channels_first' 5D tensor with shape: (samples, time, channels, rows, cols)
 If data_format='channels_last' 5D tensor with shape: (samples, time, rows, cols, channels)
  Output shape:  If return_state: a list of tensors. The first tensor is the output. The remaining tensors are the last states, each 4D tensor with shape: (samples, filters, new_rows, new_cols) if data_format='channels_first' or 4D tensor with shape: (samples, new_rows, new_cols, filters) if data_format='channels_last'. rows and cols values might have changed due to padding. If return_sequences: 5D tensor with shape: (samples, timesteps, filters, new_rows, new_cols) if data_format='channels_first' or 5D tensor with shape: (samples, timesteps, new_rows, new_cols, filters) if data_format='channels_last'. Else, 4D tensor with shape: (samples, filters, new_rows, new_cols) if data_format='channels_first' or 4D tensor with shape: (samples, new_rows, new_cols, filters) if data_format='channels_last'. 
 


 Raises
  ValueError   in case of invalid constructor arguments.    References:  
Shi et al., 2015 (the current implementation does not include the feedback loop on the cells output). 
 


 Attributes
  activation  
 
  bias_constraint  
 
  bias_initializer  
 
  bias_regularizer  
 
  data_format  
 
  dilation_rate  
 
  dropout  
 
  filters  
 
  kernel_constraint  
 
  kernel_initializer  
 
  kernel_regularizer  
 
  kernel_size  
 
  padding  
 
  recurrent_activation  
 
  recurrent_constraint  
 
  recurrent_dropout  
 
  recurrent_initializer  
 
  recurrent_regularizer  
 
  states  
 
  strides  
 
  unit_forget_bias  
 
  use_bias  
   Methods reset_states View source 
reset_states(
    states=None
)
 Reset the recorded states for the stateful RNN layer. Can only be used when RNN layer is constructed with stateful = True. Args: states: Numpy arrays that contains the value for the initial state, which will be feed to cell at the first time step. When the value is None, zero filled numpy array will be created based on the cell state size.
 


 Raises
  AttributeError   When the RNN layer is not stateful.  
  ValueError   When the batch size of the RNN layer is unknown.  
  ValueError   When the input numpy array is not compatible with the RNN layer state, either size wise or dtype wise.