fix image_ocr example steps_per_epoch and validation_steps params (keras-team#7696)

* fix image_ocr example steps_per_epoch and validation_steps params

* Update image_ocr.py

* Update image_ocr.py

* Update image_ocr.py

Author: sosoxuc

examples/image_ocr.py

@@ -409,6 +409,7 @@ def train(run_name, start_epoch, stop_epoch, img_w):
     pool_size = 2
     time_dense_size = 32
     rnn_size = 512
+    minibatch_size = 32
 
     if K.image_data_format() == 'channels_first':
         input_shape = (1, img_w, img_h)
@@ -420,7 +421,7 @@ def train(run_name, start_epoch, stop_epoch, img_w):
 
     img_gen = TextImageGenerator(monogram_file=os.path.join(fdir, 'wordlist_mono_clean.txt'),
                                  bigram_file=os.path.join(fdir, 'wordlist_bi_clean.txt'),
-                                 minibatch_size=32,
+                                 minibatch_size=minibatch_size,
                                  img_w=img_w,
                                  img_h=img_h,
                                  downsample_factor=(pool_size ** 2),
@@ -479,9 +480,13 @@ def train(run_name, start_epoch, stop_epoch, img_w):
 
     viz_cb = VizCallback(run_name, test_func, img_gen.next_val())
 
-    model.fit_generator(generator=img_gen.next_train(), steps_per_epoch=(words_per_epoch - val_words),
-                        epochs=stop_epoch, validation_data=img_gen.next_val(), validation_steps=val_words,
-                        callbacks=[viz_cb, img_gen], initial_epoch=start_epoch)
+    model.fit_generator(generator=img_gen.next_train(), 
+                        steps_per_epoch=(words_per_epoch - val_words) // minibatch_size,
+                        epochs=stop_epoch, 
+                        validation_data=img_gen.next_val(), 
+                        validation_steps=val_words // minibatch_size,
+                        callbacks=[viz_cb, img_gen], 
+                        initial_epoch=start_epoch)
 
 
 if __name__ == '__main__':