# Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== # pylint: disable=protected-access # pylint: disable=g-classes-have-attributes """Convolutional-recurrent layers.""" import numpy as np from tensorflow.python.keras import activations from tensorflow.python.keras import backend from tensorflow.python.keras import constraints from tensorflow.python.keras import initializers from tensorflow.python.keras import regularizers from tensorflow.python.keras.engine.base_layer import Layer from tensorflow.python.keras.engine.input_spec import InputSpec from tensorflow.python.keras.layers.recurrent import DropoutRNNCellMixin from tensorflow.python.keras.layers.recurrent import RNN from tensorflow.python.keras.utils import conv_utils from tensorflow.python.keras.utils import generic_utils from tensorflow.python.keras.utils import tf_utils from tensorflow.python.ops import array_ops from tensorflow.python.util.tf_export import keras_export class ConvRNN2D(RNN): """Base class for convolutional-recurrent layers. Args: cell: A RNN cell instance. A RNN cell is a class that has: - a `call(input_at_t, states_at_t)` method, returning `(output_at_t, states_at_t_plus_1)`. The call method of the cell can also take the optional argument `constants`, see section "Note on passing external constants" below. - a `state_size` attribute. This can be a single integer (single state) in which case it is the number of channels of the recurrent state (which should be the same as the number of channels of the cell output). This can also be a list/tuple of integers (one size per state). In this case, the first entry (`state_size[0]`) should be the same as the size of the cell output. return_sequences: Boolean. Whether to return the last output. in the output sequence, or the full sequence. return_state: Boolean. Whether to return the last state in addition to the output. go_backwards: Boolean (default False). If True, process the input sequence backwards and return the reversed sequence. 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. input_shape: Use this argument to specify the shape of the input when this layer is the first one in a model. 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 for use with cells that use dropout. initial_state: List of initial state tensors to be passed to the first call of the cell. constants: List of constant tensors to be passed to the cell at each timestep. Input shape: 5D tensor with shape: `(samples, timesteps, channels, rows, cols)` if data_format='channels_first' or 5D tensor with shape: `(samples, timesteps, rows, cols, channels)` if data_format='channels_last'. 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'. Masking: This layer supports masking for input data with a variable number of timesteps. Note on using statefulness in RNNs: You can set RNN layers to be 'stateful', which means that the states computed for the samples in one batch will be reused as initial states for the samples in the next batch. This assumes a one-to-one mapping between samples in different successive batches. To enable statefulness: - Specify `stateful=True` in the layer constructor. - Specify a fixed batch size for your model, by passing - If sequential model: `batch_input_shape=(...)` to the first layer in your model. - If functional model with 1 or more Input layers: `batch_shape=(...)` to all the first layers in your model. This is the expected shape of your inputs *including the batch size*. It should be a tuple of integers, e.g. `(32, 10, 100, 100, 32)`. Note that the number of rows and columns should be specified too. - Specify `shuffle=False` when calling fit(). To reset the states of your model, call `.reset_states()` on either a specific layer, or on your entire model. Note on specifying the initial state of RNNs: You can specify the initial state of RNN layers symbolically by calling them with the keyword argument `initial_state`. The value of `initial_state` should be a tensor or list of tensors representing the initial state of the RNN layer. You can specify the initial state of RNN layers numerically by calling `reset_states` with the keyword argument `states`. The value of `states` should be a numpy array or list of numpy arrays representing the initial state of the RNN layer. Note on passing external constants to RNNs: You can pass "external" constants to the cell using the `constants` keyword argument of `RNN.__call__` (as well as `RNN.call`) method. This requires that the `cell.call` method accepts the same keyword argument `constants`. Such constants can be used to condition the cell transformation on additional static inputs (not changing over time), a.k.a. an attention mechanism. """ def __init__(self, cell, return_sequences=False, return_state=False, go_backwards=False, stateful=False, unroll=False, **kwargs): if unroll: raise TypeError('Unrolling isn\'t possible with ' 'convolutional RNNs.') if isinstance(cell, (list, tuple)): # The StackedConvRNN2DCells isn't implemented yet. raise TypeError('It is not possible at the moment to' 'stack convolutional cells.') super(ConvRNN2D, self).__init__(cell, return_sequences, return_state, go_backwards, stateful, unroll, **kwargs) self.input_spec = [InputSpec(ndim=5)] self.states = None self._num_constants = None @tf_utils.shape_type_conversion def compute_output_shape(self, input_shape): if isinstance(input_shape, list): input_shape = input_shape[0] cell = self.cell if cell.data_format == 'channels_first': rows = input_shape[3] cols = input_shape[4] elif cell.data_format == 'channels_last': rows = input_shape[2] cols = input_shape[3] rows = conv_utils.conv_output_length(rows, cell.kernel_size[0], padding=cell.padding, stride=cell.strides[0], dilation=cell.dilation_rate[0]) cols = conv_utils.conv_output_length(cols, cell.kernel_size[1], padding=cell.padding, stride=cell.strides[1], dilation=cell.dilation_rate[1]) if cell.data_format == 'channels_first': output_shape = input_shape[:2] + (cell.filters, rows, cols) elif cell.data_format == 'channels_last': output_shape = input_shape[:2] + (rows, cols, cell.filters) if not self.return_sequences: output_shape = output_shape[:1] + output_shape[2:] if self.return_state: output_shape = [output_shape] if cell.data_format == 'channels_first': output_shape += [(input_shape[0], cell.filters, rows, cols) for _ in range(2)] elif cell.data_format == 'channels_last': output_shape += [(input_shape[0], rows, cols, cell.filters) for _ in range(2)] return output_shape @tf_utils.shape_type_conversion def build(self, input_shape): # Note input_shape will be list of shapes of initial states and # constants if these are passed in __call__. if self._num_constants is not None: constants_shape = input_shape[-self._num_constants:] # pylint: disable=E1130 else: constants_shape = None if isinstance(input_shape, list): input_shape = input_shape[0] batch_size = input_shape[0] if self.stateful else None self.input_spec[0] = InputSpec(shape=(batch_size, None) + input_shape[2:5]) # allow cell (if layer) to build before we set or validate state_spec if isinstance(self.cell, Layer): step_input_shape = (input_shape[0],) + input_shape[2:] if constants_shape is not None: self.cell.build([step_input_shape] + constants_shape) else: self.cell.build(step_input_shape) # set or validate state_spec if hasattr(self.cell.state_size, '__len__'): state_size = list(self.cell.state_size) else: state_size = [self.cell.state_size] if self.state_spec is not None: # initial_state was passed in call, check compatibility if self.cell.data_format == 'channels_first': ch_dim = 1 elif self.cell.data_format == 'channels_last': ch_dim = 3 if [spec.shape[ch_dim] for spec in self.state_spec] != state_size: raise ValueError( 'An initial_state was passed that is not compatible with ' '`cell.state_size`. Received `state_spec`={}; ' 'However `cell.state_size` is ' '{}'.format([spec.shape for spec in self.state_spec], self.cell.state_size)) else: if self.cell.data_format == 'channels_first': self.state_spec = [InputSpec(shape=(None, dim, None, None)) for dim in state_size] elif self.cell.data_format == 'channels_last': self.state_spec = [InputSpec(shape=(None, None, None, dim)) for dim in state_size] if self.stateful: self.reset_states() self.built = True def get_initial_state(self, inputs): # (samples, timesteps, rows, cols, filters) initial_state = backend.zeros_like(inputs) # (samples, rows, cols, filters) initial_state = backend.sum(initial_state, axis=1) shape = list(self.cell.kernel_shape) shape[-1] = self.cell.filters initial_state = self.cell.input_conv(initial_state, array_ops.zeros(tuple(shape), initial_state.dtype), padding=self.cell.padding) if hasattr(self.cell.state_size, '__len__'): return [initial_state for _ in self.cell.state_size] else: return [initial_state] def call(self, inputs, mask=None, training=None, initial_state=None, constants=None): # note that the .build() method of subclasses MUST define # self.input_spec and self.state_spec with complete input shapes. inputs, initial_state, constants = self._process_inputs( inputs, initial_state, constants) if isinstance(mask, list): mask = mask[0] timesteps = backend.int_shape(inputs)[1] kwargs = {} if generic_utils.has_arg(self.cell.call, 'training'): kwargs['training'] = training if constants: if not generic_utils.has_arg(self.cell.call, 'constants'): raise ValueError('RNN cell does not support constants') def step(inputs, states): constants = states[-self._num_constants:] # pylint: disable=invalid-unary-operand-type states = states[:-self._num_constants] # pylint: disable=invalid-unary-operand-type return self.cell.call(inputs, states, constants=constants, **kwargs) else: def step(inputs, states): return self.cell.call(inputs, states, **kwargs) last_output, outputs, states = backend.rnn(step, inputs, initial_state, constants=constants, go_backwards=self.go_backwards, mask=mask, input_length=timesteps) if self.stateful: updates = [ backend.update(self_state, state) for self_state, state in zip(self.states, states) ] self.add_update(updates) if self.return_sequences: output = outputs else: output = last_output if self.return_state: if not isinstance(states, (list, tuple)): states = [states] else: states = list(states) return [output] + states else: return output def reset_states(self, states=None): if not self.stateful: raise AttributeError('Layer must be stateful.') input_shape = self.input_spec[0].shape state_shape = self.compute_output_shape(input_shape) if self.return_state: state_shape = state_shape[0] if self.return_sequences: state_shape = state_shape[:1].concatenate(state_shape[2:]) if None in state_shape: raise ValueError('If a RNN is stateful, it needs to know ' 'its batch size. Specify the batch size ' 'of your input tensors: \n' '- If using a Sequential model, ' 'specify the batch size by passing ' 'a `batch_input_shape` ' 'argument to your first layer.\n' '- If using the functional API, specify ' 'the time dimension by passing a ' '`batch_shape` argument to your Input layer.\n' 'The same thing goes for the number of rows and ' 'columns.') # helper function def get_tuple_shape(nb_channels): result = list(state_shape) if self.cell.data_format == 'channels_first': result[1] = nb_channels elif self.cell.data_format == 'channels_last': result[3] = nb_channels else: raise KeyError return tuple(result) # initialize state if None if self.states[0] is None: if hasattr(self.cell.state_size, '__len__'): self.states = [backend.zeros(get_tuple_shape(dim)) for dim in self.cell.state_size] else: self.states = [backend.zeros(get_tuple_shape(self.cell.state_size))] elif states is None: if hasattr(self.cell.state_size, '__len__'): for state, dim in zip(self.states, self.cell.state_size): backend.set_value(state, np.zeros(get_tuple_shape(dim))) else: backend.set_value(self.states[0], np.zeros(get_tuple_shape(self.cell.state_size))) else: if not isinstance(states, (list, tuple)): states = [states] if len(states) != len(self.states): raise ValueError('Layer ' + self.name + ' expects ' + str(len(self.states)) + ' states, ' + 'but it received ' + str(len(states)) + ' state values. Input received: ' + str(states)) for index, (value, state) in enumerate(zip(states, self.states)): if hasattr(self.cell.state_size, '__len__'): dim = self.cell.state_size[index] else: dim = self.cell.state_size if value.shape != get_tuple_shape(dim): raise ValueError('State ' + str(index) + ' is incompatible with layer ' + self.name + ': expected shape=' + str(get_tuple_shape(dim)) + ', found shape=' + str(value.shape)) # TODO(anjalisridhar): consider batch calls to `set_value`. backend.set_value(state, value) class ConvLSTM2DCell(DropoutRNNCellMixin, Layer): """Cell class for the ConvLSTM2D layer. Args: 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`. 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. If you don't specify anything, no activation is applied (ie. "linear" activation: `a(x) = 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]( http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf) 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. 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. 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 4D tensor. states: List of state tensors corresponding to the previous timestep. training: Python boolean indicating whether the layer should behave in training mode or in inference mode. Only relevant when `dropout` or `recurrent_dropout` is used. """ def __init__(self, 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, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0., recurrent_dropout=0., **kwargs): super(ConvLSTM2DCell, self).__init__(**kwargs) self.filters = filters self.kernel_size = conv_utils.normalize_tuple(kernel_size, 2, 'kernel_size') self.strides = conv_utils.normalize_tuple(strides, 2, 'strides') self.padding = conv_utils.normalize_padding(padding) self.data_format = conv_utils.normalize_data_format(data_format) self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, 2, 'dilation_rate') self.activation = activations.get(activation) self.recurrent_activation = activations.get(recurrent_activation) self.use_bias = use_bias self.kernel_initializer = initializers.get(kernel_initializer) self.recurrent_initializer = initializers.get(recurrent_initializer) self.bias_initializer = initializers.get(bias_initializer) self.unit_forget_bias = unit_forget_bias self.kernel_regularizer = regularizers.get(kernel_regularizer) self.recurrent_regularizer = regularizers.get(recurrent_regularizer) self.bias_regularizer = regularizers.get(bias_regularizer) self.kernel_constraint = constraints.get(kernel_constraint) self.recurrent_constraint = constraints.get(recurrent_constraint) self.bias_constraint = constraints.get(bias_constraint) self.dropout = min(1., max(0., dropout)) self.recurrent_dropout = min(1., max(0., recurrent_dropout)) self.state_size = (self.filters, self.filters) def build(self, input_shape): if self.data_format == 'channels_first': channel_axis = 1 else: channel_axis = -1 if input_shape[channel_axis] is None: raise ValueError('The channel dimension of the inputs ' 'should be defined. Found `None`.') input_dim = input_shape[channel_axis] kernel_shape = self.kernel_size + (input_dim, self.filters * 4) self.kernel_shape = kernel_shape recurrent_kernel_shape = self.kernel_size + (self.filters, self.filters * 4) self.kernel = self.add_weight(shape=kernel_shape, initializer=self.kernel_initializer, name='kernel', regularizer=self.kernel_regularizer, constraint=self.kernel_constraint) self.recurrent_kernel = self.add_weight( shape=recurrent_kernel_shape, initializer=self.recurrent_initializer, name='recurrent_kernel', regularizer=self.recurrent_regularizer, constraint=self.recurrent_constraint) if self.use_bias: if self.unit_forget_bias: def bias_initializer(_, *args, **kwargs): return backend.concatenate([ self.bias_initializer((self.filters,), *args, **kwargs), initializers.get('ones')((self.filters,), *args, **kwargs), self.bias_initializer((self.filters * 2,), *args, **kwargs), ]) else: bias_initializer = self.bias_initializer self.bias = self.add_weight( shape=(self.filters * 4,), name='bias', initializer=bias_initializer, regularizer=self.bias_regularizer, constraint=self.bias_constraint) else: self.bias = None self.built = True def call(self, inputs, states, training=None): h_tm1 = states[0] # previous memory state c_tm1 = states[1] # previous carry state # dropout matrices for input units dp_mask = self.get_dropout_mask_for_cell(inputs, training, count=4) # dropout matrices for recurrent units rec_dp_mask = self.get_recurrent_dropout_mask_for_cell( h_tm1, training, count=4) if 0 < self.dropout < 1.: inputs_i = inputs * dp_mask[0] inputs_f = inputs * dp_mask[1] inputs_c = inputs * dp_mask[2] inputs_o = inputs * dp_mask[3] else: inputs_i = inputs inputs_f = inputs inputs_c = inputs inputs_o = inputs if 0 < self.recurrent_dropout < 1.: h_tm1_i = h_tm1 * rec_dp_mask[0] h_tm1_f = h_tm1 * rec_dp_mask[1] h_tm1_c = h_tm1 * rec_dp_mask[2] h_tm1_o = h_tm1 * rec_dp_mask[3] else: h_tm1_i = h_tm1 h_tm1_f = h_tm1 h_tm1_c = h_tm1 h_tm1_o = h_tm1 (kernel_i, kernel_f, kernel_c, kernel_o) = array_ops.split(self.kernel, 4, axis=3) (recurrent_kernel_i, recurrent_kernel_f, recurrent_kernel_c, recurrent_kernel_o) = array_ops.split(self.recurrent_kernel, 4, axis=3) if self.use_bias: bias_i, bias_f, bias_c, bias_o = array_ops.split(self.bias, 4) else: bias_i, bias_f, bias_c, bias_o = None, None, None, None x_i = self.input_conv(inputs_i, kernel_i, bias_i, padding=self.padding) x_f = self.input_conv(inputs_f, kernel_f, bias_f, padding=self.padding) x_c = self.input_conv(inputs_c, kernel_c, bias_c, padding=self.padding) x_o = self.input_conv(inputs_o, kernel_o, bias_o, padding=self.padding) h_i = self.recurrent_conv(h_tm1_i, recurrent_kernel_i) h_f = self.recurrent_conv(h_tm1_f, recurrent_kernel_f) h_c = self.recurrent_conv(h_tm1_c, recurrent_kernel_c) h_o = self.recurrent_conv(h_tm1_o, recurrent_kernel_o) i = self.recurrent_activation(x_i + h_i) f = self.recurrent_activation(x_f + h_f) c = f * c_tm1 + i * self.activation(x_c + h_c) o = self.recurrent_activation(x_o + h_o) h = o * self.activation(c) return h, [h, c] def input_conv(self, x, w, b=None, padding='valid'): conv_out = backend.conv2d(x, w, strides=self.strides, padding=padding, data_format=self.data_format, dilation_rate=self.dilation_rate) if b is not None: conv_out = backend.bias_add(conv_out, b, data_format=self.data_format) return conv_out def recurrent_conv(self, x, w): conv_out = backend.conv2d(x, w, strides=(1, 1), padding='same', data_format=self.data_format) return conv_out def get_config(self): config = {'filters': self.filters, 'kernel_size': self.kernel_size, 'strides': self.strides, 'padding': self.padding, 'data_format': self.data_format, 'dilation_rate': self.dilation_rate, 'activation': activations.serialize(self.activation), 'recurrent_activation': activations.serialize( self.recurrent_activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize( self.kernel_initializer), 'recurrent_initializer': initializers.serialize( self.recurrent_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'unit_forget_bias': self.unit_forget_bias, 'kernel_regularizer': regularizers.serialize( self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize( self.recurrent_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'kernel_constraint': constraints.serialize( self.kernel_constraint), 'recurrent_constraint': constraints.serialize( self.recurrent_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout} base_config = super(ConvLSTM2DCell, self).get_config() return dict(list(base_config.items()) + list(config.items())) @keras_export('keras.layers.ConvLSTM2D') class ConvLSTM2D(ConvRNN2D): """2D Convolutional LSTM layer. 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. Args: 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]( http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf) 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 float tensor (see input shape description below). 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](http://arxiv.org/abs/1506.04214v1) (the current implementation does not include the feedback loop on the cells output). Example: ```python steps = 10 height = 32 width = 32 input_channels = 3 output_channels = 6 inputs = tf.keras.Input(shape=(steps, height, width, input_channels)) layer = tf.keras.layers.ConvLSTM2D(filters=output_channels, kernel_size=3) outputs = layer(inputs) ``` """ def __init__(self, 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., recurrent_dropout=0., **kwargs): cell = ConvLSTM2DCell(filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, dilation_rate=dilation_rate, activation=activation, recurrent_activation=recurrent_activation, use_bias=use_bias, kernel_initializer=kernel_initializer, recurrent_initializer=recurrent_initializer, bias_initializer=bias_initializer, unit_forget_bias=unit_forget_bias, kernel_regularizer=kernel_regularizer, recurrent_regularizer=recurrent_regularizer, bias_regularizer=bias_regularizer, kernel_constraint=kernel_constraint, recurrent_constraint=recurrent_constraint, bias_constraint=bias_constraint, dropout=dropout, recurrent_dropout=recurrent_dropout, dtype=kwargs.get('dtype')) super(ConvLSTM2D, self).__init__(cell, return_sequences=return_sequences, return_state=return_state, go_backwards=go_backwards, stateful=stateful, **kwargs) self.activity_regularizer = regularizers.get(activity_regularizer) def call(self, inputs, mask=None, training=None, initial_state=None): return super(ConvLSTM2D, self).call(inputs, mask=mask, training=training, initial_state=initial_state) @property def filters(self): return self.cell.filters @property def kernel_size(self): return self.cell.kernel_size @property def strides(self): return self.cell.strides @property def padding(self): return self.cell.padding @property def data_format(self): return self.cell.data_format @property def dilation_rate(self): return self.cell.dilation_rate @property def activation(self): return self.cell.activation @property def recurrent_activation(self): return self.cell.recurrent_activation @property def use_bias(self): return self.cell.use_bias @property def kernel_initializer(self): return self.cell.kernel_initializer @property def recurrent_initializer(self): return self.cell.recurrent_initializer @property def bias_initializer(self): return self.cell.bias_initializer @property def unit_forget_bias(self): return self.cell.unit_forget_bias @property def kernel_regularizer(self): return self.cell.kernel_regularizer @property def recurrent_regularizer(self): return self.cell.recurrent_regularizer @property def bias_regularizer(self): return self.cell.bias_regularizer @property def kernel_constraint(self): return self.cell.kernel_constraint @property def recurrent_constraint(self): return self.cell.recurrent_constraint @property def bias_constraint(self): return self.cell.bias_constraint @property def dropout(self): return self.cell.dropout @property def recurrent_dropout(self): return self.cell.recurrent_dropout def get_config(self): config = {'filters': self.filters, 'kernel_size': self.kernel_size, 'strides': self.strides, 'padding': self.padding, 'data_format': self.data_format, 'dilation_rate': self.dilation_rate, 'activation': activations.serialize(self.activation), 'recurrent_activation': activations.serialize( self.recurrent_activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize( self.kernel_initializer), 'recurrent_initializer': initializers.serialize( self.recurrent_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'unit_forget_bias': self.unit_forget_bias, 'kernel_regularizer': regularizers.serialize( self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize( self.recurrent_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize( self.activity_regularizer), 'kernel_constraint': constraints.serialize( self.kernel_constraint), 'recurrent_constraint': constraints.serialize( self.recurrent_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout} base_config = super(ConvLSTM2D, self).get_config() del base_config['cell'] return dict(list(base_config.items()) + list(config.items())) @classmethod def from_config(cls, config): return cls(**config)