Bug fix of Bidirectional(LSTM(..., stateful=True)) (keras-team#4424)

* Bug fix of Bidirectional(LSTM(..., stateful=True)) keras-team#4421

* Add Recurrent.from_config() test

keras/layers/recurrent.py

@@ -242,7 +242,7 @@ def get_config(self):
                   'stateful': self.stateful,
                   'unroll': self.unroll,
                   'consume_less': self.consume_less}
-        if self.stateful:
+        if self.stateful and self.input_spec[0].shape:
             config['batch_input_shape'] = self.input_spec[0].shape
         else:
             config['input_dim'] = self.input_dim

tests/keras/layers/test_recurrent.py

@@ -157,5 +157,13 @@ def test_masking_layer():
     model.fit(I, V, nb_epoch=1, batch_size=100, verbose=1)
 
 
+@rnn_test
+def test_from_config(layer_class):
+    for stateful in (False, True):
+        l1 = layer_class(output_dim=1, stateful=stateful)
+        l2 = layer_class.from_config(l1.get_config())
+        assert l1.get_config() == l2.get_config()
+
+
 if __name__ == '__main__':
     pytest.main([__file__])

tests/keras/layers/test_wrappers.py

@@ -115,6 +115,13 @@ def test_Bidirectional():
         model.compile(loss='mse', optimizer='sgd')
         model.fit(x, y, nb_epoch=1, batch_size=1)
 
+        # Bidirectional and stateful
+        input = Input(batch_shape=(1, timesteps, dim))
+        output = wrappers.Bidirectional(rnn(output_dim, stateful=True), merge_mode=mode)(input)
+        model = Model(input, output)
+        model.compile(loss='mse', optimizer='sgd')
+        model.fit(x, y, nb_epoch=1, batch_size=1)
+
 
 if __name__ == '__main__':
     pytest.main([__file__])