Revert keras-team#9025 because it introduces a bug

keras/preprocessing/image.py

@@ -748,11 +748,10 @@ def fit(self, x,
 
         if self.zca_whitening:
             flat_x = np.reshape(x, (x.shape[0], x.shape[1] * x.shape[2] * x.shape[3]))
-            num_examples = flat_x.shape[0]
-            _, s, vt = linalg.svd(flat_x / np.sqrt(num_examples))
-            s_expand = np.hstack((s, np.zeros(vt.shape[0] - num_examples,
-                                              dtype=flat_x.dtype)))
-            self.principal_components = (vt.T / np.sqrt(s_expand ** 2 + self.zca_epsilon)).dot(vt)
+            sigma = np.dot(flat_x.T, flat_x) / flat_x.shape[0]
+            u, s, _ = linalg.svd(sigma)
+            s_inv = 1. / np.sqrt(s[np.newaxis] + self.zca_epsilon)
+            self.principal_components = (u * s_inv).dot(u.T)
 
 
 class Iterator(Sequence):

tests/keras/preprocessing/image_test.py

@@ -123,6 +123,9 @@ def test_image_data_generator_fit(self):
         # Test RBG
         x = np.random.random((32, 10, 10, 3))
         generator.fit(x)
+        # Test more samples than dims
+        x = np.random.random((32, 4, 4, 1))
+        generator.fit(x)
         generator = image.ImageDataGenerator(
             featurewise_center=True,
             samplewise_center=True,
@@ -136,6 +139,9 @@ def test_image_data_generator_fit(self):
         # Test RBG
         x = np.random.random((32, 3, 10, 10))
         generator.fit(x)
+        # Test more samples than dims
+        x = np.random.random((32, 1, 4, 4))
+        generator.fit(x)
 
     def test_directory_iterator(self, tmpdir):
         num_classes = 2