unify paddle 1.6 api in understand_sentiment (#824)

* unify paddle 1.6 api in understand_sentiment

06.understand_sentiment/README.cn.md

@@ -219,8 +219,7 @@ def stacked_lstm_net(data, input_dim, class_dim, emb_dim, hid_dim, stacked_num):
 ```python
 def inference_program(word_dict):
     data = fluid.data(
-        name="words", shape=[-1], dtype="int64", lod_level=1)
-
+        name="words", shape=[None], dtype="int64", lod_level=1)
     dict_dim = len(word_dict)
     net = convolution_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM)
     # net = stacked_lstm_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM, STACKED_NUM)
@@ -235,7 +234,7 @@ def inference_program(word_dict):
 
 ```python
 def train_program(prediction):
-    label = fluid.data(name="label", shape=[-1,1], dtype="int64")
+    label = fluid.data(name="label", shape=[None, 1], dtype="int64")
     cost = fluid.layers.cross_entropy(input=prediction, label=label)
     avg_cost = fluid.layers.mean(cost)
     accuracy = fluid.layers.accuracy(input=prediction, label=label)

06.understand_sentiment/README.md

@@ -208,8 +208,7 @@ Next we define the prediction program (`inference_program`). We use `convolution
 ```python
 def inference_program(word_dict):
     data = fluid.data(
-        name="words", shape=[-1], dtype="int64", lod_level=1)
-
+        name="words", shape=[None], dtype="int64", lod_level=1)
     dict_dim = len(word_dict)
     net = convolution_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM)
     # net = stacked_lstm_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM, STACKED_NUM)
@@ -224,7 +223,7 @@ During the testing, the classifier calculates the probability of each output. Th
 
 ```python
 def train_program(prediction):
-    label = fluid.data(name="label", shape=[-1, 1], dtype="int64")
+    label = fluid.data(name="label", shape=[None, 1], dtype="int64")
     cost = fluid.layers.cross_entropy(input=prediction, label=label)
     avg_cost = fluid.layers.mean(cost)
     accuracy = fluid.layers.accuracy(input=prediction, label=label)

06.understand_sentiment/index.cn.html

@@ -261,8 +261,7 @@ def stacked_lstm_net(data, input_dim, class_dim, emb_dim, hid_dim, stacked_num):
 ```python
 def inference_program(word_dict):
     data = fluid.data(
-        name="words", shape=[-1], dtype="int64", lod_level=1)
-
+        name="words", shape=[None], dtype="int64", lod_level=1)
     dict_dim = len(word_dict)
     net = convolution_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM)
     # net = stacked_lstm_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM, STACKED_NUM)
@@ -277,7 +276,7 @@ def inference_program(word_dict):
 
 ```python
 def train_program(prediction):
-    label = fluid.data(name="label", shape=[-1,1], dtype="int64")
+    label = fluid.data(name="label", shape=[None, 1], dtype="int64")
     cost = fluid.layers.cross_entropy(input=prediction, label=label)
     avg_cost = fluid.layers.mean(cost)
     accuracy = fluid.layers.accuracy(input=prediction, label=label)

06.understand_sentiment/index.html

@@ -250,8 +250,7 @@ Next we define the prediction program (`inference_program`). We use `convolution
 ```python
 def inference_program(word_dict):
     data = fluid.data(
-        name="words", shape=[-1], dtype="int64", lod_level=1)
-
+        name="words", shape=[None], dtype="int64", lod_level=1)
     dict_dim = len(word_dict)
     net = convolution_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM)
     # net = stacked_lstm_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM, STACKED_NUM)
@@ -266,7 +265,7 @@ During the testing, the classifier calculates the probability of each output. Th
 
 ```python
 def train_program(prediction):
-    label = fluid.data(name="label", shape=[-1, 1], dtype="int64")
+    label = fluid.data(name="label", shape=[None, 1], dtype="int64")
     cost = fluid.layers.cross_entropy(input=prediction, label=label)
     avg_cost = fluid.layers.mean(cost)
     accuracy = fluid.layers.accuracy(input=prediction, label=label)

06.understand_sentiment/train_conv.py

@@ -62,14 +62,13 @@ def convolution_net(data, input_dim, class_dim, emb_dim, hid_dim):
 
 def inference_program(word_dict):
     dict_dim = len(word_dict)
-    data = fluid.data(name="words", shape=[-1], dtype="int64", lod_level=1)
-
+    data = fluid.data(name="words", shape=[None], dtype="int64", lod_level=1)
     net = convolution_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM)
     return net
 
 
 def train_program(prediction):
-    label = fluid.data(name="label", shape=[-1, 1], dtype="int64")
+    label = fluid.data(name="label", shape=[None, 1], dtype="int64")
     cost = fluid.layers.cross_entropy(input=prediction, label=label)
     avg_cost = fluid.layers.mean(cost)
     accuracy = fluid.layers.accuracy(input=prediction, label=label)

06.understand_sentiment/train_dyn_rnn.py

@@ -53,15 +53,14 @@ def dynamic_rnn_lstm(data, input_dim, class_dim, emb_dim, lstm_size):
 
 
 def inference_program(word_dict):
-    data = fluid.data(name="words", shape=[-1], dtype="int64", lod_level=1)
-
+    data = fluid.data(name="words", shape=[None], dtype="int64", lod_level=1)
     dict_dim = len(word_dict)
     pred = dynamic_rnn_lstm(data, dict_dim, CLASS_DIM, EMB_DIM, LSTM_SIZE)
     return pred
 
 
 def train_program(prediction):
-    label = fluid.data(name="label", shape=[-1, 1], dtype="int64")
+    label = fluid.data(name="label", shape=[None, 1], dtype="int64")
     cost = fluid.layers.cross_entropy(input=prediction, label=label)
     avg_cost = fluid.layers.mean(cost)
     accuracy = fluid.layers.accuracy(input=prediction, label=label)

06.understand_sentiment/train_stacked_lstm.py

@@ -68,8 +68,7 @@ def stacked_lstm_net(data, input_dim, class_dim, emb_dim, hid_dim, stacked_num):
 
 
 def inference_program(word_dict):
-    data = fluid.data(name="words", shape=[-1], dtype="int64", lod_level=1)
-
+    data = fluid.data(name="words", shape=[None], dtype="int64", lod_level=1)
     dict_dim = len(word_dict)
     net = stacked_lstm_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM,
                            STACKED_NUM)
@@ -78,7 +77,7 @@ def inference_program(word_dict):
 
 def train_program(prediction):
     # prediction = inference_program(word_dict)
-    label = fluid.data(name="label", shape=[-1, 1], dtype="int64")
+    label = fluid.data(name="label", shape=[None, 1], dtype="int64")
     cost = fluid.layers.cross_entropy(input=prediction, label=label)
     avg_cost = fluid.layers.mean(cost)
     accuracy = fluid.layers.accuracy(input=prediction, label=label)