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use Evaluator in book tests (#5778)

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release/0.11.0
fengjiayi 8 years ago committed by GitHub
parent d2e30a2c4f
commit d5be1d4dd0
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5 changed files with 45 additions and 25 deletions
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11
python/paddle/v2/fluid/tests/book/test_image_classification_train.py
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@ -4,6 +4,7 @@ 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.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 @@ net = vgg16_bn_drop(images)
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 @@ exe.run(framework.default_startup_program())
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 @@ for pass_id in range(PASS_NUM):
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:

8
python/paddle/v2/fluid/tests/book/test_recognize_digits_conv.py
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@ -46,7 +46,6 @@ exe = Executor(place)
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 @@ for pass_id in range(PASS_NUM):
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)

15
python/paddle/v2/fluid/tests/book/test_recognize_digits_mlp.py
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@ -3,6 +3,7 @@ import paddle.v2 as paddle
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 @@ label = layers.data(name='y', shape=[1], data_type='int64')
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 @@ exe.run(framework.default_startup_program())
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 @@ for pass_id in range(PASS_NUM):
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)

18
python/paddle/v2/fluid/tests/book/test_understand_sentiment_conv.py
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@ -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)

18
python/paddle/v2/fluid/tests/book/test_understand_sentiment_dynamic_lstm.py
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@ -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)

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