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提交 a3950ba1 编写于 作者: L linshuliang 提交者： RaindragonD
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Modify the line break errors of the demo program in the file visualdl_usage_en.md (#939) 

* Modify errors in VisualDL usage manual
Including:
	line break errors;
	punctuation errors;
	Add explanation for each pictures.

* Modify errors in VisualDL usage manual
Both Chinese and English Documents are modified.
Including :
	line break errors.
	spell errors

* Modify the line break error in visualdl_usage_en
The Line break errors in visualdl_usage_en will make the demo program crash into errors.
I have fixed these errors.

* Modify the line break error in visualdl_usage_en.md
The errors have been fixed.
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doc/fluid/user_guides/howto/evaluation_and_debugging/debug/visualdl_usage.md
浏览文件 @ a3950ba1
......@@ -559,7 +559,7 @@ visualdl --logdir ./log --host 0.0.0.0 --port 8080
与其他需要记录数据的组件不同，使用 graph 组件的唯一要素就是指定模型文件的存放位置，即在 `visualdl` 命令中增加选项 `--model_pb` 来指定模型文件的存放路径，则可在前端看到相应效果。
例3.1 graph 组件示例程序（下面示例展示了如何用 Paddle 保存一个 Lenet-5 模型）[Github](https://github.com/PaddlePaddle/VisualDL/blob/develop/demo/component/graph-demo.py)
例9 graph 组件示例程序（下面示例展示了如何用 Paddle 保存一个 Lenet-5 模型）[Github](https://github.com/PaddlePaddle/VisualDL/blob/develop/demo/component/graph-demo.py)
```python
# coding=utf-8
......
doc/fluid/user_guides/howto/evaluation_and_debugging/debug/visualdl_usage_en.md
浏览文件 @ a3950ba1
......@@ -55,10 +55,11 @@ Demo 2. Use LogWriter instance to create component instance
```python
# Set the name of mode to "train", and create a scalar component instance
with log_writer.mode("train") as logger:
train_scalar = logger.scalar("acc")
train_scalar = logger.scalar("acc")
# Set the name of mode to "test", and create an image component instance
with log_writer.mode("test") as shower:
test_image = shower.image("conv_image", 10, 1)
test_image = shower.image("conv_image", 10, 1)
```
### scalar -- component to draw line charts
......@@ -97,19 +98,19 @@ log_writer = LogWriter("./log", sync_cycle=20)
# Create two ScalarWriter instances, whose mode is set to be "train"
with log_writer.mode("train") as logger:
train_acc = logger.scalar("acc")
train_loss = logger.scalar("loss")
train_acc = logger.scalar("acc")
train_loss = logger.scalar("loss")
# Create a ScalarWriter instance, whose mode is set to be "test"
with log_writer.mode("test") as logger:
test_acc = logger.scalar("acc")
test_acc = logger.scalar("acc")
value = [i/1000.0 for i in range(1000)]
for step in range(1000):
# Add data
train_acc.add_record(step, value[step])
train_loss.add_record(step, 1 / (value[step] + 1))
test_acc.add_record(step, 1 - value[step])
# Add data
train_acc.add_record(step, value[step])
train_loss.add_record(step, 1 / (value[step] + 1))
test_acc.add_record(step, 1 - value[step])
```
After running the demo program above, you can start the flask server with command ``visualdl`` :
......@@ -171,17 +172,17 @@ log_writer = LogWriter('./log', sync_cycle=10)
# Create a HistogramWriter instance, whose mode is set to be "train"
with log_writer.mode("train") as logger:
param1_histogram = logger.histogram("param1", num_buckets=100)
param1_histogram = logger.histogram("param1", num_buckets=100)
# Loop
for step in range(1, 101):
# Create input data
interval_start = 1 + 2 * step/100.0
interval_end = 6 - 2 * step/100.0
data = np.random.uniform(interval_start, interval_end, size=(10000))
# Create input data
interval_start = 1 + 2 * step/100.0
interval_end = 6 - 2 * step/100.0
data = np.random.uniform(interval_start, interval_end, size=(10000))
# Use member function add_record() to add data
param1_histogram.add_record(step, data)
# Use member function add_record() to add data
param1_histogram.add_record(step, data)
```
After running the demo program above, you can start the flask server with command ``visualdl`` :
......@@ -252,14 +253,14 @@ from PIL import Image
def random_crop(img):
'''
This function is used to get a random block (100*100 pixels) of data img.
''
img = Image.open(img)
w, h = img.size
random_w = np.random.randint(0, w - 100)
random_h = np.random.randint(0, h - 100)
return img.crop((random_w, random_h, random_w + 100, random_h + 100))
'''
This function is used to get a random block (100*100 pixels) of data img.
'''
img = Image.open(img)
w, h = img.size
random_w = np.random.randint(0, w - 100)
random_h = np.random.randint(0, h - 100)
return img.crop((random_w, random_h, random_w + 100, random_h + 100))
# Create a LogWriter instance
......@@ -268,37 +269,39 @@ log_writer = LogWriter("./log", sync_cycle=10)
# Create a ImageWriter instance
ns = 2
with log_writer.mode("train") as logger:
input_image = logger.image(tag="test", num_samples=ns)
input_image = logger.image(tag="test", num_samples=ns)
# The variable sample_num is used to record the number of image data that have been sampled
sample_num = 0
for step in range(6):
# Set the condition of start_sampling()
if sample_num == 0:
input_image.start_sampling()
idx = input_image.is_sample_taken()
# # if idx != -1，sample this data, otherwise skip
if idx != -1:
# Get image data
image_path = "test.jpg"
image_data = np.array(random_crop(image_path))
# Add data
input_image.set_sample(idx, image_data.shape, image_data.flatten())
sample_num += 1
# If sampling of the present period have been completed, call finish_sample()
if sample_num % ns == 0:
input_image.finish_sampling()
sample_num = 0
# Set the condition of start_sampling()
if sample_num == 0:
input_image.start_sampling()
idx = input_image.is_sample_taken()
# if idx != -1，sample this data, otherwise skip
if idx != -1:
# Get image data
image_path = "test.jpg"
image_data = np.array(random_crop(image_path))
# Add data
input_image.set_sample(idx, image_data.shape, image_data.flatten())
sample_num += 1
# If sampling of the present period have been completed, call finish_sample()
if sample_num % ns == 0:
input_image.finish_sampling()
sample_num = 0
```
After running the demo program above, you can start the flask server with command ``visualdl`` :
```shell
visualdl --logdir ./log --host 0.0.0.0 --port 8080
```
By opening the URL [http://0.0.0.0:8080](http://0.0.0.0:8080) in your browser，then click the ``SAMPLES`` option at the top of the webpage, you will see the interface below.
<p align="center">
......@@ -342,12 +345,12 @@ log_writter = LogWriter("./log", sync_cycle=10)
# Create a TextWriter instance
with log_writter.mode("train") as logger:
vdl_text_comp = logger.text(tag="test")
vdl_text_comp = logger.text(tag="test")
# Use member function add_record() to add data
for i in range(1, 6):
vdl_text_comp.add_record(i, "这是第 %d 个 step 的数据。" % i)
vdl_text_comp.add_record(i, "This is data %d ." % i)
vdl_text_comp.add_record(i, "这是第 %d 个 step 的数据。" % i)
vdl_text_comp.add_record(i, "This is data %d ." % i)
```
After running the demo program above, you can start the flask server with command ``visualdl`` :
......@@ -420,23 +423,23 @@ from visualdl import LogWriter
def read_audio_data(audio_path):
"""
Read audio data
"""
CHUNK = 4096
f = wave.open(audio_path, "rb")
wavdata = []
chunk = f.readframes(CHUNK)
"""
Read audio data
"""
CHUNK = 4096
f = wave.open(audio_path, "rb")
wavdata = []
chunk = f.readframes(CHUNK)
while chunk:
data = np.fromstring(chunk, dtype='uint8')
wavdata.extend(data)
chunk = f.readframes(CHUNK)
while chunk:
data = np.fromstring(chunk, dtype='uint8')
wavdata.extend(data)
chunk = f.readframes(CHUNK)
# 8k sample rate, 16bit frame, 1 channel
shape = [8000, 2, 1]
# 8k sample rate, 16bit frame, 1 channel
shape = [8000, 2, 1]
return shape, wavdata
return shape, wavdata
# Create a LogWriter instance
......@@ -445,7 +448,7 @@ log_writter = LogWriter("./log", sync_cycle=10)
# Create an AudioWriter instance
ns = 2
with log_writter.mode("train") as logger:
input_audio = logger.audio(tag="test", num_samples=ns)
input_audio = logger.audio(tag="test", num_samples=ns)
# The variable sample_num is used to record the number of audio data that have been sampled
audio_sample_num = 0
......@@ -453,24 +456,24 @@ audio_sample_num = 0
for step in range(9):
# Set the condition of start_sampling()
if audio_sample_num == 0:
input_audio.start_sampling()
# Get idx
idx = input_audio.is_sample_taken()
# if idx != -1，sample this data, otherwise skip
if idx != -1:
# Read audio data
audio_path = "test.wav"
audio_shape, audio_data = read_audio_data(audio_path)
# Add data through member function set_samle()
input_audio.set_sample(idx, audio_shape, audio_data)
audio_sample_num += 1
# If sampling of the present period have been completed, call finish_sample()
if audio_sample_num % ns ==0:
input_audio.finish_sampling()
audio_sample_num = 0
input_audio.start_sampling()
# Get idx
idx = input_audio.is_sample_taken()
# if idx != -1，sample this data, otherwise skip
if idx != -1:
# Read audio data
audio_path = "test.wav"
audio_shape, audio_data = read_audio_data(audio_path)
# Add data through member function set_samle()
input_audio.set_sample(idx, audio_shape, audio_data)
audio_sample_num += 1
# If sampling of the present period have been completed, call finish_sample()
if audio_sample_num % ns ==0:
input_audio.finish_sampling()
audio_sample_num = 0
```
After running the demo program above, you can start the flask server with command ``visualdl`` :
......@@ -527,16 +530,16 @@ log_writer = LogWriter("./log", sync_cycle=10)
# Create an EmbeddingWriter instance
with log_writer.mode("train") as logger:
train_embedding = logger.embedding()
train_embedding = logger.embedding()
# Initialize data List[List(float)]
hot_vectors = np.random.uniform(1, 2, size=(10, 3))
word_dict = {
"label_1": 5,
"label_2": 4,
"label_3": 3,
"label_4": 2,
"label_5": 1,}
"label_1": 5,
"label_2": 4,
"label_3": 3,
"label_4": 2,
"label_5": 1,}
# Add data through member function add_embeddings_with_word_dict(data, Dict)
train_embedding.add_embeddings_with_word_dict(hot_vectors, word_dict)
......@@ -575,29 +578,29 @@ import paddle.fluid as fluid
def lenet_5(img):
'''
Define the Lenet-5 model
'''
conv1 = fluid.nets.simple_img_conv_pool(
input=img,
filter_size=5,
num_filters=20,
pool_size=2,
pool_stride=2,
act="relu")
conv1_bn = fluid.layers.batch_norm(input=conv1)
conv2 = fluid.nets.simple_img_conv_pool(
input=conv1_bn,
filter_size=5,
num_filters=50,
pool_size=2,
pool_stride=2,
act="relu")
predition = fluid.layers.fc(input=conv2, size=10, act="softmax")
return predition
'''
Define the Lenet-5 model
'''
conv1 = fluid.nets.simple_img_conv_pool(
input=img,
filter_size=5,
num_filters=20,
pool_size=2,
pool_stride=2,
act="relu")
conv1_bn = fluid.layers.batch_norm(input=conv1)
conv2 = fluid.nets.simple_img_conv_pool(
input=conv1_bn,
filter_size=5,
num_filters=50,
pool_size=2,
pool_stride=2,
act="relu")
predition = fluid.layers.fc(input=conv2, size=10, act="softmax")
return predition
# Variable assignment
......@@ -610,10 +613,10 @@ exe.run(fluid.default_startup_program())
# save the result to "./paddle_lenet_5_model"
fluid.io.save_inference_model(
"./paddle_lenet_5_model",
feeded_var_names=[image.name],
target_vars=[predition],
executor=exe)
"./paddle_lenet_5_model",
feeded_var_names=[image.name],
target_vars=[predition],
executor=exe)
```
After running the demo program above, you can start the flask server with command ``visualdl`` :
......
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