提交 3d2268e7 编写于 作者: R ruri 提交者: lvmengsi

Fix predict bug in image classification (#833)

* fix typo:Infererence

* fix predict bug in ic
上级 b5d917eb
...@@ -338,7 +338,7 @@ def train_program(): ...@@ -338,7 +338,7 @@ def train_program():
cost = fluid.layers.cross_entropy(input=predict, label=label) cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(cost) avg_cost = fluid.layers.mean(cost)
accuracy = fluid.layers.accuracy(input=predict, label=label) accuracy = fluid.layers.accuracy(input=predict, label=label)
return [avg_cost, accuracy] return [avg_cost, accuracy, predict]
``` ```
## Optimizer Function 配置 ## Optimizer Function 配置
...@@ -383,7 +383,7 @@ feed_order = ['pixel', 'label'] ...@@ -383,7 +383,7 @@ feed_order = ['pixel', 'label']
main_program = fluid.default_main_program() main_program = fluid.default_main_program()
star_program = fluid.default_startup_program() star_program = fluid.default_startup_program()
avg_cost, acc = train_program() avg_cost, acc, predict = train_program()
# Test program # Test program
test_program = main_program.clone(for_test=True) test_program = main_program.clone(for_test=True)
......
...@@ -335,7 +335,7 @@ def train_program(): ...@@ -335,7 +335,7 @@ def train_program():
cost = fluid.layers.cross_entropy(input=predict, label=label) cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(cost) avg_cost = fluid.layers.mean(cost)
accuracy = fluid.layers.accuracy(input=predict, label=label) accuracy = fluid.layers.accuracy(input=predict, label=label)
return [avg_cost, accuracy] return [avg_cost, accuracy, predict]
``` ```
## Optimizer Function Configuration ## Optimizer Function Configuration
...@@ -383,7 +383,7 @@ feed_order = ['pixel', 'label'] ...@@ -383,7 +383,7 @@ feed_order = ['pixel', 'label']
main_program = fluid.default_main_program() main_program = fluid.default_main_program()
star_program = fluid.default_startup_program() star_program = fluid.default_startup_program()
avg_cost, acc = train_program() avg_cost, acc, predict = train_program()
# Test program # Test program
test_program = main_program.clone(for_test=True) test_program = main_program.clone(for_test=True)
......
...@@ -380,7 +380,7 @@ def train_program(): ...@@ -380,7 +380,7 @@ def train_program():
cost = fluid.layers.cross_entropy(input=predict, label=label) cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(cost) avg_cost = fluid.layers.mean(cost)
accuracy = fluid.layers.accuracy(input=predict, label=label) accuracy = fluid.layers.accuracy(input=predict, label=label)
return [avg_cost, accuracy] return [avg_cost, accuracy, predict]
``` ```
## Optimizer Function 配置 ## Optimizer Function 配置
...@@ -425,7 +425,7 @@ feed_order = ['pixel', 'label'] ...@@ -425,7 +425,7 @@ feed_order = ['pixel', 'label']
main_program = fluid.default_main_program() main_program = fluid.default_main_program()
star_program = fluid.default_startup_program() star_program = fluid.default_startup_program()
avg_cost, acc = train_program() avg_cost, acc, predict = train_program()
# Test program # Test program
test_program = main_program.clone(for_test=True) test_program = main_program.clone(for_test=True)
......
...@@ -377,7 +377,7 @@ def train_program(): ...@@ -377,7 +377,7 @@ def train_program():
cost = fluid.layers.cross_entropy(input=predict, label=label) cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(cost) avg_cost = fluid.layers.mean(cost)
accuracy = fluid.layers.accuracy(input=predict, label=label) accuracy = fluid.layers.accuracy(input=predict, label=label)
return [avg_cost, accuracy] return [avg_cost, accuracy, predict]
``` ```
## Optimizer Function Configuration ## Optimizer Function Configuration
...@@ -425,7 +425,7 @@ feed_order = ['pixel', 'label'] ...@@ -425,7 +425,7 @@ feed_order = ['pixel', 'label']
main_program = fluid.default_main_program() main_program = fluid.default_main_program()
star_program = fluid.default_startup_program() star_program = fluid.default_startup_program()
avg_cost, acc = train_program() avg_cost, acc, predict = train_program()
# Test program # Test program
test_program = main_program.clone(for_test=True) test_program = main_program.clone(for_test=True)
......
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