Bug fix + test : Initializing states for ConvLSTM2D (keras-team#6564)

* Bug fix

* Update convolutional_recurrent_test.py

* Update convolutional_recurrent.py

keras/layers/convolutional_recurrent.py

@@ -105,7 +105,8 @@ def __init__(self, filters,
         self.return_sequences = return_sequences
         self.go_backwards = go_backwards
         self.stateful = stateful
-        self.input_spec = InputSpec(ndim=5)
+        self.input_spec = [InputSpec(ndim=5)]
+        self.state_spec = None
 
     def compute_output_shape(self, input_shape):
         if self.data_format == 'channels_first':
@@ -330,9 +331,10 @@ def __init__(self, filters,
         self.recurrent_dropout = min(1., max(0., recurrent_dropout))
 
     def build(self, input_shape):
-        # TODO: better handling of input spec
-        self.input_spec = InputSpec(shape=input_shape)
-
+        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,) + input_shape[1:])
         if self.stateful:
             self.reset_states()
         else:
@@ -413,7 +415,7 @@ def get_initial_state(self, inputs):
     def reset_states(self):
         if not self.stateful:
             raise RuntimeError('Layer must be stateful.')
-        input_shape = self.input_spec.shape
+        input_shape = self.input_spec[0].shape
         output_shape = self.compute_output_shape(input_shape)
         if not input_shape[0]:
             raise ValueError('If a RNN is stateful, a complete '

tests/keras/layers/convolutional_recurrent_test.py

@@ -3,8 +3,8 @@
 from numpy.testing import assert_allclose
 
 from keras import backend as K
-from keras.models import Sequential
-from keras.layers import convolutional_recurrent
+from keras.models import Sequential, Model
+from keras.layers import convolutional_recurrent, Input
 from keras.utils.test_utils import layer_test
 from keras import regularizers
 
@@ -116,5 +116,18 @@ def test_convolutional_recurrent():
                                'recurrent_dropout': 0.1},
                        input_shape=inputs.shape)
 
+            # check state initialization
+            layer = convolutional_recurrent.ConvLSTM2D(filters=filters,
+                                                       kernel_size=(num_row, num_col),
+                                                       data_format=data_format,
+                                                       return_sequences=return_sequences)
+            layer.build(inputs.shape)
+            x = Input(batch_shape=inputs.shape)
+            initial_state = layer.get_initial_state(x)
+            y = layer(x, initial_state=initial_state)
+            model = Model(x, y)
+            assert model.predict(inputs).shape == layer.compute_output_shape(inputs.shape)
+
+
 if __name__ == '__main__':
     pytest.main([__file__])