PR #47726: updated convolutional_recurrent.py to include 1D, 2D, and 3D ConvLSTM instances.

Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/47726

Updating ConvLSTM to include 1D & 3D cases, as suggested by [this thread](https://github.com/tensorflow/tensorflow/issues/42399).

- Implementation involves creating Base N-dimensional `ConvRNN`, `ConvLSTMCell`, and `ConvLSTM` classes which are inherited from by the concrete implementations in `ConvLSTM1D`, `ConvLSTM2D`, and `ConvLSTM3D`. Currently limited to 3D as maximum Convolution dimensionality as that is the highest rank implemented in `keras.backend`.

- Rewrote tests to include 1D & 3D cases. The error message present in ConvLSTM2D as documented by [this thread](https://github.com/tensorflow/tensorflow/issues/36901) is still persistent.
Copybara import of the project:

--
b2b92b83478dd2a821af9a83a65077e99fe5300d by Matt Lyon <matthewlyon18@gmail.com>:

updated ConvLSTM

--
960eb402f8c2d4c1f5b14703824c0307ed8bc000 by Matt Lyon <matthewlyon18@gmail.com>:

updates to coding style

--
660d02ebccbd1327d2674ff35bc0af2303c612d5 by Matt Lyon <matthewlyon18@gmail.com>:

fix docstrings

PiperOrigin-RevId: 379750075

keras/api/golden/v1/tensorflow.keras.layers.-conv-l-s-t-m1-d.pbtxt

+path: "tensorflow.keras.layers.ConvLSTM1D"
+tf_class {
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+  is_instance: "<class \'keras.layers.convolutional_recurrent.ConvLSTM\'>"
+  is_instance: "<class \'keras.layers.convolutional_recurrent.ConvRNN\'>"
+  is_instance: "<class \'keras.layers.recurrent.RNN\'>"
+  is_instance: "<class \'keras.engine.base_layer.Layer\'>"
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keras/api/golden/v1/tensorflow.keras.layers.-conv-l-s-t-m2-d.pbtxt

 path: "tensorflow.keras.layers.ConvLSTM2D"
 tf_class {
   is_instance: "<class \'keras.layers.convolutional_recurrent.ConvLSTM2D\'>"
-  is_instance: "<class \'keras.layers.convolutional_recurrent.ConvRNN2D\'>"
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   is_instance: "<class \'keras.layers.recurrent.RNN\'>"
   is_instance: "<class \'keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.module.module.Module\'>"

keras/api/golden/v1/tensorflow.keras.layers.-conv-l-s-t-m3-d.pbtxt

+path: "tensorflow.keras.layers.ConvLSTM3D"
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+    argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_config"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_initial_state"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_losses_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_updates_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_weights"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "reset_states"
+    argspec: "args=[\'self\', \'states\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "set_weights"
+    argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "with_name_scope"
+    argspec: "args=[\'cls\', \'method\'], varargs=None, keywords=None, defaults=None"
+  }
+}

keras/api/golden/v1/tensorflow.keras.layers.pbtxt

@@ -92,10 +92,18 @@ tf_module {
     name: "Conv3DTranspose"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "ConvLSTM1D"
+    mtype: "<type \'type\'>"
+  }
   member {
     name: "ConvLSTM2D"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "ConvLSTM3D"
+    mtype: "<type \'type\'>"
+  }
   member {
     name: "Convolution1D"
     mtype: "<type \'type\'>"

keras/api/golden/v2/tensorflow.keras.layers.-conv-l-s-t-m1-d.pbtxt

+path: "tensorflow.keras.layers.ConvLSTM1D"
+tf_class {
+  is_instance: "<class \'keras.layers.convolutional_recurrent.ConvLSTM1D\'>"
+  is_instance: "<class \'keras.layers.convolutional_recurrent.ConvLSTM\'>"
+  is_instance: "<class \'keras.layers.convolutional_recurrent.ConvRNN\'>"
+  is_instance: "<class \'keras.layers.recurrent.RNN\'>"
+  is_instance: "<class \'keras.engine.base_layer.Layer\'>"
+  is_instance: "<class \'tensorflow.python.module.module.Module\'>"
+  is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
+  is_instance: "<class \'tensorflow.python.training.tracking.base.Trackable\'>"
+  is_instance: "<class \'keras.utils.version_utils.LayerVersionSelector\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "activation"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "activity_regularizer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "bias_constraint"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "bias_initializer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "bias_regularizer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "compute_dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "data_format"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dilation_rate"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dropout"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dtype_policy"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dynamic"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "filters"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "inbound_nodes"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_mask"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_spec"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "kernel_constraint"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "kernel_initializer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "kernel_regularizer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "kernel_size"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "losses"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "metrics"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name_scope"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "outbound_nodes"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_mask"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "padding"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "recurrent_activation"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "recurrent_constraint"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "recurrent_dropout"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "recurrent_initializer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "recurrent_regularizer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "stateful"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "states"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "strides"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "submodules"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "supports_masking"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "unit_forget_bias"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "updates"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "use_bias"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "variable_dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "weights"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'dilation_rate\', \'activation\', \'recurrent_activation\', \'use_bias\', \'kernel_initializer\', \'recurrent_initializer\', \'bias_initializer\', \'unit_forget_bias\', \'kernel_regularizer\', \'recurrent_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'recurrent_constraint\', \'bias_constraint\', \'return_sequences\', \'return_state\', \'go_backwards\', \'stateful\', \'dropout\', \'recurrent_dropout\'], varargs=None, keywords=kwargs, defaults=[\'1\', \'valid\', \'None\', \'1\', \'tanh\', \'hard_sigmoid\', \'True\', \'glorot_uniform\', \'orthogonal\', \'zeros\', \'True\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'False\', \'False\', \'False\', \'False\', \'0.0\', \'0.0\'], "
+  }
+  member_method {
+    name: "add_loss"
+    argspec: "args=[\'self\', \'losses\'], varargs=None, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "add_metric"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_update"
+    argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_variable"
+    argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "add_weight"
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], "
+  }
+  member_method {
+    name: "apply"
+    argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "build"
+    argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "call"
+    argspec: "args=[\'self\', \'inputs\', \'mask\', \'training\', \'initial_state\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "compute_mask"
+    argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "compute_output_shape"
+    argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "compute_output_signature"
+    argspec: "args=[\'self\', \'input_signature\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "count_params"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "finalize_state"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "from_config"
+    argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_config"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_initial_state"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_losses_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_updates_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_weights"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "reset_states"
+    argspec: "args=[\'self\', \'states\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "set_weights"
+    argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "with_name_scope"
+    argspec: "args=[\'cls\', \'method\'], varargs=None, keywords=None, defaults=None"
+  }
+}

keras/api/golden/v2/tensorflow.keras.layers.-conv-l-s-t-m2-d.pbtxt

 path: "tensorflow.keras.layers.ConvLSTM2D"
 tf_class {
   is_instance: "<class \'keras.layers.convolutional_recurrent.ConvLSTM2D\'>"
-  is_instance: "<class \'keras.layers.convolutional_recurrent.ConvRNN2D\'>"
+  is_instance: "<class \'keras.layers.convolutional_recurrent.ConvLSTM\'>"
+  is_instance: "<class \'keras.layers.convolutional_recurrent.ConvRNN\'>"
   is_instance: "<class \'keras.layers.recurrent.RNN\'>"
   is_instance: "<class \'keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.module.module.Module\'>"

keras/api/golden/v2/tensorflow.keras.layers.-conv-l-s-t-m3-d.pbtxt

+path: "tensorflow.keras.layers.ConvLSTM3D"
+tf_class {
+  is_instance: "<class \'keras.layers.convolutional_recurrent.ConvLSTM3D\'>"
+  is_instance: "<class \'keras.layers.convolutional_recurrent.ConvLSTM\'>"
+  is_instance: "<class \'keras.layers.convolutional_recurrent.ConvRNN\'>"
+  is_instance: "<class \'keras.layers.recurrent.RNN\'>"
+  is_instance: "<class \'keras.engine.base_layer.Layer\'>"
+  is_instance: "<class \'tensorflow.python.module.module.Module\'>"
+  is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
+  is_instance: "<class \'tensorflow.python.training.tracking.base.Trackable\'>"
+  is_instance: "<class \'keras.utils.version_utils.LayerVersionSelector\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "activation"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "activity_regularizer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "bias_constraint"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "bias_initializer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "bias_regularizer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "compute_dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "data_format"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dilation_rate"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dropout"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dtype_policy"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dynamic"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "filters"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "inbound_nodes"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_mask"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_spec"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "kernel_constraint"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "kernel_initializer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "kernel_regularizer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "kernel_size"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "losses"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "metrics"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name_scope"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "outbound_nodes"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_mask"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "padding"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "recurrent_activation"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "recurrent_constraint"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "recurrent_dropout"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "recurrent_initializer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "recurrent_regularizer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "stateful"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "states"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "strides"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "submodules"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "supports_masking"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "unit_forget_bias"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "updates"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "use_bias"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "variable_dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "weights"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'dilation_rate\', \'activation\', \'recurrent_activation\', \'use_bias\', \'kernel_initializer\', \'recurrent_initializer\', \'bias_initializer\', \'unit_forget_bias\', \'kernel_regularizer\', \'recurrent_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'recurrent_constraint\', \'bias_constraint\', \'return_sequences\', \'return_state\', \'go_backwards\', \'stateful\', \'dropout\', \'recurrent_dropout\'], varargs=None, keywords=kwargs, defaults=[\'(1, 1, 1)\', \'valid\', \'None\', \'(1, 1, 1)\', \'tanh\', \'hard_sigmoid\', \'True\', \'glorot_uniform\', \'orthogonal\', \'zeros\', \'True\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'False\', \'False\', \'False\', \'False\', \'0.0\', \'0.0\'], "
+  }
+  member_method {
+    name: "add_loss"
+    argspec: "args=[\'self\', \'losses\'], varargs=None, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "add_metric"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_update"
+    argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_variable"
+    argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "add_weight"
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], "
+  }
+  member_method {
+    name: "apply"
+    argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "build"
+    argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "call"
+    argspec: "args=[\'self\', \'inputs\', \'mask\', \'training\', \'initial_state\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "compute_mask"
+    argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "compute_output_shape"
+    argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "compute_output_signature"
+    argspec: "args=[\'self\', \'input_signature\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "count_params"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "finalize_state"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "from_config"
+    argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_config"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_initial_state"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_losses_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_updates_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_weights"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "reset_states"
+    argspec: "args=[\'self\', \'states\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "set_weights"
+    argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "with_name_scope"
+    argspec: "args=[\'cls\', \'method\'], varargs=None, keywords=None, defaults=None"
+  }
+}

keras/api/golden/v2/tensorflow.keras.layers.pbtxt

@@ -92,10 +92,18 @@ tf_module {
     name: "Conv3DTranspose"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "ConvLSTM1D"
+    mtype: "<type \'type\'>"
+  }
   member {
     name: "ConvLSTM2D"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "ConvLSTM3D"
+    mtype: "<type \'type\'>"
+  }
   member {
     name: "Convolution1D"
     mtype: "<type \'type\'>"

keras/layers/__init__.py

@@ -224,7 +224,9 @@ else:
   LSTMCellV1 = LSTMCell
 
 # Convolutional-recurrent layers.
+from keras.layers.convolutional_recurrent import ConvLSTM1D
 from keras.layers.convolutional_recurrent import ConvLSTM2D
+from keras.layers.convolutional_recurrent import ConvLSTM3D
 
 # CuDNN recurrent layers.
 from keras.layers.cudnn_recurrent import CuDNNLSTM

keras/layers/convolutional_recurrent_test.py

@@ -25,7 +25,63 @@ from keras import testing_utils
 
 
 @keras_parameterized.run_all_keras_modes
-class ConvLSTMTest(keras_parameterized.TestCase):
+class ConvLSTM1DTest(keras_parameterized.TestCase):
+
+  @parameterized.named_parameters(
+      *testing_utils.generate_combinations_with_testcase_name(
+          data_format=['channels_first', 'channels_last'],
+          return_sequences=[True, False]))
+  def test_conv_lstm(self, data_format, return_sequences):
+    num_row = 3
+    filters = 3
+    num_samples = 1
+    input_channel = 2
+    input_num_row = 5
+    sequence_len = 2
+    if data_format == 'channels_first':
+      inputs = np.random.rand(num_samples, sequence_len, input_channel,
+                              input_num_row)
+    else:
+      inputs = np.random.rand(num_samples, sequence_len, input_num_row,
+                              input_channel)
+
+    # test for return state:
+    x = keras.Input(batch_shape=inputs.shape)
+    kwargs = {
+        'data_format': data_format,
+        'return_sequences': return_sequences,
+        'return_state': True,
+        'stateful': True,
+        'filters': filters,
+        'kernel_size': num_row,
+        'padding': 'valid',
+    }
+    layer = keras.layers.ConvLSTM1D(**kwargs)
+    layer.build(inputs.shape)
+    outputs = layer(x)
+    _, states = outputs[0], outputs[1:]
+    self.assertEqual(len(states), 2)
+    model = keras.models.Model(x, states[0])
+
+    state = model.predict(inputs)
+
+    self.assertAllClose(keras.backend.eval(layer.states[0]), state, atol=1e-4)
+
+    # test for output shape:
+    testing_utils.layer_test(
+        keras.layers.ConvLSTM1D,
+        kwargs={
+            'data_format': data_format,
+            'return_sequences': return_sequences,
+            'filters': filters,
+            'kernel_size': num_row,
+            'padding': 'valid'
+        },
+        input_shape=inputs.shape)
+
+
+@keras_parameterized.run_all_keras_modes
+class ConvLSTM2DTest(keras_parameterized.TestCase):
 
   @parameterized.named_parameters(
       *testing_utils.generate_combinations_with_testcase_name(
@@ -66,8 +122,7 @@ class ConvLSTMTest(keras_parameterized.TestCase):
     model = keras.models.Model(x, states[0])
     state = model.predict(inputs)
 
-    self.assertAllClose(
-        keras.backend.eval(layer.states[0]), state, atol=1e-4)
+    self.assertAllClose(keras.backend.eval(layer.states[0]), state, atol=1e-4)
 
     # test for output shape:
     testing_utils.layer_test(
@@ -109,8 +164,7 @@ class ConvLSTMTest(keras_parameterized.TestCase):
       out1 = model.predict(np.ones_like(inputs))
 
       # train once so that the states change
-      model.train_on_batch(np.ones_like(inputs),
-                           np.random.random(out1.shape))
+      model.train_on_batch(np.ones_like(inputs), np.random.random(out1.shape))
       out2 = model.predict(np.ones_like(inputs))
 
       # if the state is not reset, output should be different
@@ -231,5 +285,65 @@ class ConvLSTMTest(keras_parameterized.TestCase):
     model.predict([x_1, x_2])
 
 
+@keras_parameterized.run_all_keras_modes
+class ConvLSTM3DTest(keras_parameterized.TestCase):
+
+  @parameterized.named_parameters(
+      *testing_utils.generate_combinations_with_testcase_name(
+          data_format=['channels_first', 'channels_last'],
+          return_sequences=[True, False]))
+  def test_conv_lstm(self, data_format, return_sequences):
+    num_height = 3
+    num_width = 3
+    num_depth = 3
+    filters = 3
+    num_samples = 1
+    input_channel = 2
+    input_height = 5
+    input_width = 5
+    input_depth = 5
+    sequence_len = 2
+    if data_format == 'channels_first':
+      inputs = np.random.rand(num_samples, sequence_len, input_channel,
+                              input_height, input_width, input_depth)
+    else:
+      inputs = np.random.rand(num_samples, sequence_len, input_height,
+                              input_width, input_depth, input_channel)
+
+    # test for return state:
+    x = keras.Input(batch_shape=inputs.shape)
+    kwargs = {
+        'data_format': data_format,
+        'return_sequences': return_sequences,
+        'return_state': True,
+        'stateful': True,
+        'filters': filters,
+        'kernel_size': (num_height, num_width, num_depth),
+        'padding': 'same'
+    }
+    layer = keras.layers.ConvLSTM3D(**kwargs)
+    layer.build(inputs.shape)
+    outputs = layer(x)
+    _, states = outputs[0], outputs[1:]
+    self.assertEqual(len(states), 2)
+    model = keras.models.Model(x, states[0])
+
+    state = model.predict(inputs)
+
+    self.assertAllClose(keras.backend.eval(layer.states[0]), state, atol=1e-4)
+
+    # test for output shape:
+    testing_utils.layer_test(
+        keras.layers.ConvLSTM3D,
+        kwargs={
+            'data_format': data_format,
+            'return_sequences': return_sequences,
+            'filters': filters,
+            'kernel_size': (num_height, num_width, num_depth),
+            'padding': 'valid'
+        },
+        input_shape=inputs.shape)
+
+
 if __name__ == '__main__':
   tf.test.main()