Allow re-use of EarlyStopping callback objects. (#3000)

An EarlyStopping callback object has internal state variables to tell it
when it has reached its stopping point.  These were initialized in __init__(),
so attempting to re-use the same object resulted in immediate stopping. This
prevents (for example) performing early stopping during cross-validation with
the scikit-learn wrapper.

This patch initializes the variables in on_train_begin(), so they are re-set
for each training fold.  Tests included.

keras/callbacks.py

@@ -327,17 +327,17 @@ def __init__(self, monitor='val_loss', patience=0, verbose=0, mode='auto'):
 
         if mode == 'min':
             self.monitor_op = np.less
-            self.best = np.Inf
         elif mode == 'max':
             self.monitor_op = np.greater
-            self.best = -np.Inf
         else:
             if 'acc' in self.monitor:
                 self.monitor_op = np.greater
-                self.best = -np.Inf
             else:
                 self.monitor_op = np.less
-                self.best = np.Inf
+
+    def on_train_begin(self, logs={}):
+        self.wait = 0       # Allow instances to be re-used
+        self.best = np.Inf if self.monitor_op == np.less else -np.Inf
 
     def on_epoch_end(self, epoch, logs={}):
         current = logs.get(self.monitor)

tests/keras/test_callbacks.py

@@ -105,6 +105,27 @@ def test_EarlyStopping():
                         validation_data=(X_test, y_test), callbacks=cbks, nb_epoch=20)
 
 
+def test_EarlyStopping_reuse():
+    patience = 3
+    data = np.random.random((100, 1))
+    labels = np.where(data > 0.5, 1, 0)
+    model = Sequential((
+        Dense(1, input_dim=1, activation='relu'),
+        Dense(1, activation='sigmoid'),
+    ))
+    model.compile(optimizer='sgd', loss='binary_crossentropy', metrics=['accuracy'])
+    stopper = callbacks.EarlyStopping(monitor='acc', patience=patience)
+    weights = model.get_weights()
+
+    hist = model.fit(data, labels, callbacks=[stopper])
+    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])
+    assert len(hist.epoch) >= patience
+
+
 def test_LearningRateScheduler():
     (X_train, y_train), (X_test, y_test) = get_test_data(nb_train=train_samples,
                                                          nb_test=test_samples,