[RELNOTES] Sync Sequential.fit() with Model.fit() (#8192)

* Sync Sequential.fit() with Model.fit()

* Specify explicit epochs=20

...instead of relying on implicit default value.

* Revert docstring changes

* pep8

* pep8

keras/engine/training.py

@@ -1442,7 +1442,8 @@ def _get_deduped_metrics_names(self):
             deduped_out_labels.append(new_label)
         return deduped_out_labels
 
-    def fit(self, x=None,
+    def fit(self,
+            x=None,
             y=None,
             batch_size=None,
             epochs=1,

keras/models.py

@@ -830,9 +830,22 @@ def compile(self, optimizer, loss,
         self.sample_weights = self.model.sample_weights
         self.total_loss = self.model.total_loss
 
-    def fit(self, x, y, batch_size=32, epochs=10, verbose=1, callbacks=None,
-            validation_split=0., validation_data=None, shuffle=True,
-            class_weight=None, sample_weight=None, initial_epoch=0, **kwargs):
+    def fit(self,
+            x=None,
+            y=None,
+            batch_size=None,
+            epochs=1,
+            verbose=1,
+            callbacks=None,
+            validation_split=0.,
+            validation_data=None,
+            shuffle=True,
+            class_weight=None,
+            sample_weight=None,
+            initial_epoch=0,
+            steps_per_epoch=None,
+            validation_steps=None,
+            **kwargs):
         """Trains the model for a fixed number of epochs.
 
         # Arguments
@@ -904,7 +917,9 @@ def fit(self, x, y, batch_size=32, epochs=10, verbose=1, callbacks=None,
                               shuffle=shuffle,
                               class_weight=class_weight,
                               sample_weight=sample_weight,
-                              initial_epoch=initial_epoch)
+                              initial_epoch=initial_epoch,
+                              steps_per_epoch=steps_per_epoch,
+                              validation_steps=validation_steps)
 
     def evaluate(self, x, y, batch_size=32, verbose=1,
                  sample_weight=None):

tests/keras/test_callbacks.py

@@ -247,12 +247,12 @@ def test_EarlyStopping_reuse():
     stopper = callbacks.EarlyStopping(monitor='acc', patience=patience)
     weights = model.get_weights()
 
-    hist = model.fit(data, labels, callbacks=[stopper])
+    hist = model.fit(data, labels, callbacks=[stopper], epochs=20)
     assert len(hist.epoch) >= patience
 
     # This should allow training to go for at least `patience` epochs
     model.set_weights(weights)
-    hist = model.fit(data, labels, callbacks=[stopper])
+    hist = model.fit(data, labels, callbacks=[stopper], epochs=20)
     assert len(hist.epoch) >= patience