如何使用 HParams 仪表板从超参数调整中绘制平行坐标图?
How to plot parallel coordinae plot ftrom Hyperparameter Tuning with the HParams Dashboard?
我正在尝试在此 Tensorflow tutorial 中复制超参数调整教程中的平行坐标图,并且我已经编写了自己的 csv 文件来存储我的结果。
我读取 csv 文件的输出是这样的:
conv_layers filters dropout accuracy
0 4 16 0.5 0.447917
1 4 16 0.6 0.458333
2 4 32 0.5 0.635417
3 4 32 0.6 0.447917
4 4 64 0.5 0.604167
5 4 64 0.6 0.645833
6 8 16 0.5 0.437500
7 8 16 0.6 0.437500
8 8 32 0.5 0.437500
9 8 32 0.6 0.562500
10 8 64 0.5 0.562500
11 8 64 0.6 0.437500
如何创建与 python 教程中相同的情节?
所以我使用 plotly
找到了答案
import os
import sys
import pandas as pd
from plotly.offline import init_notebook_mode, iplot
import plotly.graph_objects as go
init_notebook_mode(connected=True)
df = pd.read_csv('path/to/csv')
fig = go.Figure(data=
go.Parcoords(
line = dict(color = df['accuracy'],
colorbar = [],
colorscale = [[0, '#6C9E12'], ##
[0.25,'#0D5F67'], ##
[0.5,'#AA1B13'], ##
[0.75, '#69178C'], ##
[1, '#DE9733']]),
dimensions = list([
dict(range = [0,12],
label = 'Conv_layers', values = df['conv_layers']),
dict(range = [8,64],
label = 'filter_number', values = df['filters']),
dict(range = [0.2,0.8],
label = 'dropout_rate', values = df['dropout']),
dict(range = [0.2,0.8],
label = 'dense_num', values = df['dense']),
dict(range = [0.1,1.0],
label = 'accuracy', values = df['accuracy'])
])
)
)
fig.update_layout(
plot_bgcolor = '#E5E5E5',
paper_bgcolor = '#E5E5E5',
title="Parallel Coordinates Plot"
)
# print the plot
fig.show()
我正在尝试在此 Tensorflow tutorial 中复制超参数调整教程中的平行坐标图,并且我已经编写了自己的 csv 文件来存储我的结果。 我读取 csv 文件的输出是这样的:
conv_layers filters dropout accuracy
0 4 16 0.5 0.447917
1 4 16 0.6 0.458333
2 4 32 0.5 0.635417
3 4 32 0.6 0.447917
4 4 64 0.5 0.604167
5 4 64 0.6 0.645833
6 8 16 0.5 0.437500
7 8 16 0.6 0.437500
8 8 32 0.5 0.437500
9 8 32 0.6 0.562500
10 8 64 0.5 0.562500
11 8 64 0.6 0.437500
如何创建与 python 教程中相同的情节?
所以我使用 plotly
找到了答案import os
import sys
import pandas as pd
from plotly.offline import init_notebook_mode, iplot
import plotly.graph_objects as go
init_notebook_mode(connected=True)
df = pd.read_csv('path/to/csv')
fig = go.Figure(data=
go.Parcoords(
line = dict(color = df['accuracy'],
colorbar = [],
colorscale = [[0, '#6C9E12'], ##
[0.25,'#0D5F67'], ##
[0.5,'#AA1B13'], ##
[0.75, '#69178C'], ##
[1, '#DE9733']]),
dimensions = list([
dict(range = [0,12],
label = 'Conv_layers', values = df['conv_layers']),
dict(range = [8,64],
label = 'filter_number', values = df['filters']),
dict(range = [0.2,0.8],
label = 'dropout_rate', values = df['dropout']),
dict(range = [0.2,0.8],
label = 'dense_num', values = df['dense']),
dict(range = [0.1,1.0],
label = 'accuracy', values = df['accuracy'])
])
)
)
fig.update_layout(
plot_bgcolor = '#E5E5E5',
paper_bgcolor = '#E5E5E5',
title="Parallel Coordinates Plot"
)
# print the plot
fig.show()