use Evaluator in book tests (#5778)

Author: JiayiFeng

python/paddle/v2/fluid/tests/book/test_image_classification_train.py

@@ -4,6 +4,7 @@
 import paddle.v2.fluid.framework as framework
 import paddle.v2.fluid.layers as layers
 import paddle.v2.fluid.nets as nets
+import paddle.v2.fluid.evaluator as evaluator
 from paddle.v2.fluid.executor import Executor
 from paddle.v2.fluid.initializer import XavierInitializer
 from paddle.v2.fluid.optimizer import AdamOptimizer
@@ -103,12 +104,13 @@ def conv_block(input, num_filter, groups, dropouts):
 predict = layers.fc(input=net, size=classdim, act='softmax')
 cost = layers.cross_entropy(input=predict, label=label)
 avg_cost = layers.mean(x=cost)
-accuracy = layers.accuracy(input=predict, label=label)
 
 # optimizer = SGDOptimizer(learning_rate=0.001)
 optimizer = AdamOptimizer(learning_rate=0.001)
 opts = optimizer.minimize(avg_cost)
 
+accuracy, acc_out = evaluator.accuracy(input=predict, label=label)
+
 BATCH_SIZE = 128
 PASS_NUM = 1
 
@@ -124,6 +126,7 @@ def conv_block(input, num_filter, groups, dropouts):
 
 for pass_id in range(PASS_NUM):
     batch_id = 0
+    accuracy.reset(exe)
     for data in train_reader():
         img_data = np.array(map(lambda x: x[0].reshape(data_shape),
                                 data)).astype("float32")
@@ -141,12 +144,14 @@ def conv_block(input, num_filter, groups, dropouts):
         outs = exe.run(framework.default_main_program(),
                        feed={"pixel": tensor_img,
                              "label": tensor_y},
-                       fetch_list=[avg_cost, accuracy])
+                       fetch_list=[avg_cost, acc_out])
 
         loss = np.array(outs[0])
         acc = np.array(outs[1])
+        pass_acc = accuracy.eval(exe)
         print("pass_id:" + str(pass_id) + " batch_id:" + str(batch_id) +
-              " loss:" + str(loss) + " acc:" + str(acc))
+              " loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" + str(
+                  pass_acc))
         batch_id = batch_id + 1
 
         if batch_id > 1:

python/paddle/v2/fluid/tests/book/test_recognize_digits_conv.py

@@ -46,7 +46,6 @@
 exe.run(framework.default_startup_program())
 
 for pass_id in range(PASS_NUM):
-    count = 0
     accuracy.reset(exe)
     for data in train_reader():
         img_data = np.array(map(lambda x: x[0].reshape([1, 28, 28]),
@@ -66,13 +65,14 @@
         loss = np.array(outs[0])
         acc = np.array(outs[1])
         pass_acc = accuracy.eval(exe)
-        print "pass id : ", pass_id, pass_acc
+        print("pass_id=" + str(pass_id) + " acc=" + str(acc) + " pass_acc=" +
+              str(pass_acc))
         # print loss, acc
-        if loss < 10.0 and acc > 0.9:
+        if loss < 10.0 and pass_acc > 0.9:
             # if avg cost less than 10.0 and accuracy is larger than 0.9, we think our code is good.
             exit(0)
 
     pass_acc = accuracy.eval(exe)
-    print "pass id : ", pass_id, pass_acc
+    print("pass_id=" + str(pass_id) + " pass_acc=" + str(pass_acc))
 
 exit(1)

python/paddle/v2/fluid/tests/book/test_recognize_digits_mlp.py

@@ -3,6 +3,7 @@
 import paddle.v2.fluid.core as core
 import paddle.v2.fluid.framework as framework
 import paddle.v2.fluid.layers as layers
+import paddle.v2.fluid.evaluator as evaluator
 from paddle.v2.fluid.executor import Executor
 from paddle.v2.fluid.initializer import UniformInitializer
 from paddle.v2.fluid.optimizer import MomentumOptimizer
@@ -30,11 +31,12 @@
 
 cost = layers.cross_entropy(input=predict, label=label)
 avg_cost = layers.mean(x=cost)
-accuracy = layers.accuracy(input=predict, label=label)
 
 optimizer = MomentumOptimizer(learning_rate=0.001, momentum=0.9)
 opts = optimizer.minimize(avg_cost)
 
+accuracy, acc_out = evaluator.accuracy(input=predict, label=label)
+
 train_reader = paddle.batch(
     paddle.reader.shuffle(
         paddle.dataset.mnist.train(), buf_size=8192),
@@ -47,6 +49,7 @@
 
 PASS_NUM = 100
 for pass_id in range(PASS_NUM):
+    accuracy.reset(exe)
     for data in train_reader():
         x_data = np.array(map(lambda x: x[0], data)).astype("float32")
         y_data = np.array(map(lambda x: x[1], data)).astype("int64")
@@ -61,9 +64,13 @@
         outs = exe.run(framework.default_main_program(),
                        feed={'x': tensor_x,
                              'y': tensor_y},
-                       fetch_list=[avg_cost, accuracy])
+                       fetch_list=[avg_cost, acc_out])
         out = np.array(outs[0])
         acc = np.array(outs[1])
-        if out[0] < 5.0:
-            exit(0)  # if avg cost less than 5.0, we think our code is good.
+        pass_acc = accuracy.eval(exe)
+
+        if pass_acc > 0.7:
+            exit(0)
+            # print("pass_id=" + str(pass_id) + " auc=" +
+            #      str(acc) + " pass_acc=" + str(pass_acc))
 exit(1)

python/paddle/v2/fluid/tests/book/test_understand_sentiment_conv.py

@@ -1,6 +1,7 @@
 import numpy as np
 import paddle.v2 as paddle
 import paddle.v2.fluid.core as core
+import paddle.v2.fluid.evaluator as evaluator
 import paddle.v2.fluid.framework as framework
 import paddle.v2.fluid.layers as layers
 import paddle.v2.fluid.nets as nets
@@ -32,8 +33,8 @@ def convolution_net(input_dim, class_dim=2, emb_dim=32, hid_dim=32):
     avg_cost = layers.mean(x=cost)
     adam_optimizer = AdamOptimizer(learning_rate=0.002)
     opts = adam_optimizer.minimize(avg_cost)
-    acc = layers.accuracy(input=prediction, label=label)
-    return avg_cost, acc
+    accuracy, acc_out = evaluator.accuracy(input=prediction, label=label)
+    return avg_cost, accuracy, acc_out
 
 
 def to_lodtensor(data, place):
@@ -59,7 +60,8 @@ def main():
     dict_dim = len(word_dict)
     class_dim = 2
 
-    cost, acc = convolution_net(input_dim=dict_dim, class_dim=class_dim)
+    cost, accuracy, acc_out = convolution_net(
+        input_dim=dict_dim, class_dim=class_dim)
 
     train_data = paddle.batch(
         paddle.reader.shuffle(
@@ -71,6 +73,7 @@ def main():
     exe.run(framework.default_startup_program())
 
     for pass_id in xrange(PASS_NUM):
+        accuracy.reset(exe)
         for data in train_data():
             tensor_words = to_lodtensor(map(lambda x: x[0], data), place)
 
@@ -83,12 +86,13 @@ def main():
             outs = exe.run(framework.default_main_program(),
                            feed={"words": tensor_words,
                                  "label": tensor_label},
-                           fetch_list=[cost, acc])
+                           fetch_list=[cost, acc_out])
             cost_val = np.array(outs[0])
             acc_val = np.array(outs[1])
-
-            print("cost=" + str(cost_val) + " acc=" + str(acc_val))
-            if cost_val < 1.0 and acc_val > 0.7:
+            pass_acc = accuracy.eval(exe)
+            print("cost=" + str(cost_val) + " acc=" + str(acc_val) +
+                  " pass_acc=" + str(pass_acc))
+            if cost_val < 1.0 and pass_acc > 0.8:
                 exit(0)
     exit(1)
 

python/paddle/v2/fluid/tests/book/test_understand_sentiment_dynamic_lstm.py

@@ -1,6 +1,7 @@
 import numpy as np
 import paddle.v2 as paddle
 import paddle.v2.fluid.core as core
+import paddle.v2.fluid.evaluator as evaluator
 import paddle.v2.fluid.framework as framework
 import paddle.v2.fluid.layers as layers
 from paddle.v2.fluid.executor import Executor
@@ -41,8 +42,8 @@ def stacked_lstm_net(input_dim,
     avg_cost = layers.mean(x=cost)
     adam_optimizer = AdamOptimizer(learning_rate=0.002)
     opts = adam_optimizer.minimize(avg_cost)
-    acc = layers.accuracy(input=prediction, label=label)
-    return avg_cost, acc
+    accuracy, acc_out = evaluator.accuracy(input=prediction, label=label)
+    return avg_cost, accuracy, acc_out
 
 
 def to_lodtensor(data, place):
@@ -69,7 +70,8 @@ def main():
     dict_dim = len(word_dict)
     class_dim = 2
 
-    cost, acc = stacked_lstm_net(input_dim=dict_dim, class_dim=class_dim)
+    cost, accuracy, acc_out = stacked_lstm_net(
+        input_dim=dict_dim, class_dim=class_dim)
 
     train_data = paddle.batch(
         paddle.reader.shuffle(
@@ -81,6 +83,7 @@ def main():
     exe.run(framework.default_startup_program())
 
     for pass_id in xrange(PASS_NUM):
+        accuracy.reset(exe)
         for data in train_data():
             tensor_words = to_lodtensor(map(lambda x: x[0], data), place)
 
@@ -93,12 +96,13 @@ def main():
             outs = exe.run(framework.default_main_program(),
                            feed={"words": tensor_words,
                                  "label": tensor_label},
-                           fetch_list=[cost, acc])
+                           fetch_list=[cost, acc_out])
             cost_val = np.array(outs[0])
             acc_val = np.array(outs[1])
-
-            print("cost=" + str(cost_val) + " acc=" + str(acc_val))
-            if cost_val < 1.0 and acc_val > 0.7:
+            pass_acc = accuracy.eval(exe)
+            print("cost=" + str(cost_val) + " acc=" + str(acc_val) +
+                  " pass_acc=" + str(pass_acc))
+            if cost_val < 1.0 and acc_val > 0.8:
                 exit(0)
     exit(1)