Fix some issues

fluid/ocr_ctc/train.py

@@ -11,8 +11,6 @@
 #WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #See the License for the specific language governing permissions and
 #limitations under the License.
-from __future__ import print_function
-
 import sys
 
 import paddle.v2 as paddle
@@ -42,7 +40,7 @@ def ocr_conv(input, num, with_bn):
 
 
 num_classes = 9054
-data_shape = [3, 32, 32]
+data_shape = [1, 512, 512]
 
 images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
 label = fluid.layers.data(name='label', shape=[1], dtype='int64')
@@ -57,11 +55,10 @@ sliced_feature = fluid.layers.im2sequence(
     block_x=1,
     block_y=3, )
 
-gru_forward = fluid.layers.gru(input=sliced_feature, size=200, act="relu")
-gru_backward = fluid.layers.gru(input=sliced_feature,
-                                size=200,
-                                reverse=True,
-                                act="relu")
+# TODO(wanghaoshuang): repaced by GRU
+gru_forward = fluid.layers.lstm(input=sliced_feature, size=200, act="relu")
+gru_backward = fluid.layers.lstm(
+    input=sliced_feature, size=200, reverse=True, act="relu")
 
 out = fluid.layers.fc(input=[gru_forward, gru_backward], size=num_classes + 1)
 cost = fluid.layers.warpctc(
@@ -70,17 +67,20 @@ cost = fluid.layers.warpctc(
     size=num_classes + 1,
     blank=num_classes,
     norm_by_times=True)
+avg_cost = fluid.layers.mean(x=cost)
 
+# TODO(wanghaoshuang): set clipping
 optimizer = fluid.optimizer.Momentum(
     learning_rate=((1.0e-3) / 16), momentum=0.9)
 opts = optimizer.minimize(cost)
 
 decoded_out = fluid.layers.ctc_greedy_decoder(input=output, blank=class_num)
-error = fluid.evaluator.EditDistance(input=decoded_out, label=label)
+error_evaluator = fluid.evaluator.EditDistance(input=decoded_out, label=label)
 
 BATCH_SIZE = 16
 PASS_NUM = 1
 
+# TODO(wanghaoshuang): replaced by correct data reader
 train_reader = paddle.batch(
     paddle.reader.shuffle(
         paddle.dataset.cifar.train10(), buf_size=128 * 10),
@@ -92,14 +92,11 @@ feeder = fluid.DataFeeder(place=place, feed_list=[images, label])
 exe.run(fluid.default_startup_program())
 
 for pass_id in range(PASS_NUM):
-    accuracy.reset(exe)
+    error_evaluator.reset(exe)
     for data in train_reader():
-        loss, acc = exe.run(fluid.default_main_program(),
+        loss, error = exe.run(fluid.default_main_program(),
                               feed=feeder.feed(data),
-                            fetch_list=[avg_cost] + accuracy.metrics)
-        pass_acc = accuracy.eval(exe)
-        print("loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" + str(
-            pass_acc))
-        # this model is slow, so if we can train two mini batch, we think it works properly.
-        exit(0)
-exit(1)
+                              fetch_list=[avg_cost] + error.metrics)
+        pass_error = error_evaluator.eval(exe)
+        print "loss: %s;  distance error: %s; pass_dis_error: %s;" % (
+            str(loss), str(error), str(pass_error))