plot_model 的執行，需要安裝 pydot 和 graphviz
```
pip3 install pydot
```
## plot_model 的環境安裝
在 windows 上，graphviz 必須手動安裝，安裝檔可以到他們官網下載。
[http://www.graphviz.org/Download_windows.php](http://www.graphviz.org/Download_windows.php)
安裝好之後必須在環境變數 path 加上 C:\Program Files (x86)\Graphviz2.30\bin 才能使用。
cmd 下輸入指令：
```
dot -version
```
若顯示以下訊息，表示正確安裝。
```
dot - graphviz version 2.30.1 (20130214.1330)
libdir = "C:\Program Files (x86)\Graphviz2.30\bin"
Activated plugin library: gvplugin_pango.dll
Using textlayout: textlayout:cairo
Activated plugin library: gvplugin_dot_layout.dll
Using layout: dot:dot_layout
Activated plugin library: gvplugin_core.dll
Using render: dot:core
Using device: dot:dot:core
The plugin configuration file:
C:\Program Files (x86)\Graphviz2.30\bin\config6
was successfully loaded.
render : cairo dot fig gd gdiplus map pic pov ps svg tk vml vrml xdot
layout : circo dot fdp neato nop nop1 nop2 osage patchwork sfdp twopi
textlayout : textlayout
device : bmp canon cmap cmapx cmapx_np dot emf emfplus eps fig gd gd2 gif gv imap imap_np ismap jpe jpeg jpg metafile pdf p
ic plain plain-ext png pov ps ps2 svg svgz tif tiff tk vml vmlz vrml wbmp xdot
loadimage : (lib) bmp eps gd gd2 gif jpe jpeg jpg png ps svg
```
## 參考文件
plot_model 在 windows 上的環境建立：[https://zhuanlan.zhihu.com/p/28158957](https://zhuanlan.zhihu.com/p/28158957)
graphviz 的使用：[https://www.openfoundry.org/tw/foss-programs/8820-graphviz-](https://www.openfoundry.org/tw/foss-programs/8820-graphviz-)
2017/10/19
Python - 電腦字體數字辨識
markdown
續前篇:[Python - draw text on image and image to numpy array](http://etrex.blogspot.tw/2017/10/python-draw-text-on-image-and-image-to.html)
## 目標
嘗試建立一個簡單的類神經網路，只針對電腦字體數字 0~9 共 10 張圖片作訓練，訓練資料就是測試資料，在 40 次左右的訓練後正確度可達到 100%。
## 本文包含
* keras 的使用
* keras Model 輸出成圖片
## 程式碼
```
import numpy as np
from PIL import Image
from PIL import ImageFont
from PIL import ImageDraw
image_size = (8,13)
font_size = 10
x_train = np.array([])
y_train = np.array([])
for i in range(10):
# 空白圖片生成
image = Image.new('L', image_size, 0)
# 取得繪圖器
draw = ImageDraw.Draw(image)
# 字型設定
# font = ImageFont.truetype("C:/Windows/Fonts/cour.ttf", font_size)
font = ImageFont.truetype("C:/Windows/Fonts/msjh.ttc", font_size)
# 關閉反鋸齒
draw.fontmode = '1'
# 測量文字尺寸
text_size = draw.textsize(str(i),font)
# print('text_size:', text_size)
# 文字置中
text_position = ((image_size[0]-text_size[0])//2,(image_size[1]-text_size[1])//2)
# print('text_position:', text_position)
# 畫上文字
draw.text(text_position, str(i), 255, font)
# 存檔
image.save(str(i)+'.bmp')
# 轉成 numpy array
na = np.array(image.getdata()).reshape(image.size[1], image.size[0])
# 加入訓練資料
x_train = np.append(x_train,na)
y_train = np.append(y_train,i)
import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import RMSprop
# 每次更新時所採用的資料筆數
batch_size = 1
# 總共有幾種數字
num_classes = 10
# 要更新幾次
epochs = 100
# 把資料轉成 model 需要的格式
x_train = x_train.reshape(10, 104)
x_train = x_train.astype('float32')
x_train /= 255
print(x_train.shape[0], 'train samples')
y_train = keras.utils.to_categorical(y_train, num_classes)
# 建立 model
model = Sequential()
# 一層 + softmax
model.add(Dense(num_classes, input_shape=(104,), activation='softmax'))
# ??
model.summary()
# 選擇 loss function 和 optimizer
model.compile(loss='categorical_crossentropy',
optimizer=RMSprop(),
metrics=['accuracy'])
# 開始訓練
history = model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_train, y_train))
# 計算分數
score = model.evaluate(x_train, y_train, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
# 儲存 model 至圖片
from keras.utils import plot_model
plot_model(model, to_file='model.png')
```
## 輸出
```
Using TensorFlow backend.
10 train samples
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_1 (Dense) (None, 10) 1050
=================================================================
Total params: 1,050
Trainable params: 1,050
Non-trainable params: 0
_________________________________________________________________
Train on 10 samples, validate on 10 samples
Epoch 1/100
10/10 [==============================] - ETA: 0s - loss: 2.5473 - acc: 0.0000e+00 - val_loss: 2.4555 - val_acc: 0.1000
Epoch 2/100
10/10 [==============================] - ETA: 0s - loss: 2.4563 - acc: 0.1000 - val_loss: 2.3924 - val_acc: 0.1000
Epoch 3/100
10/10 [==============================] - ETA: 0s - loss: 2.3926 - acc: 0.1000 - val_loss: 2.3334 - val_acc: 0.2000
Epoch 4/100
10/10 [==============================] - ETA: 0s - loss: 2.3336 - acc: 0.2000 - val_loss: 2.2760 - val_acc: 0.2000
Epoch 5/100
10/10 [==============================] - ETA: 0s - loss: 2.2766 - acc: 0.2000 - val_loss: 2.2196 - val_acc: 0.2000
Epoch 6/100
10/10 [==============================] - ETA: 0s - loss: 2.2203 - acc: 0.2000 - val_loss: 2.1636 - val_acc: 0.4000
Epoch 7/100
10/10 [==============================] - ETA: 0s - loss: 2.1640 - acc: 0.4000 - val_loss: 2.1090 - val_acc: 0.5000
Epoch 8/100
10/10 [==============================] - ETA: 0s - loss: 2.1096 - acc: 0.4000 - val_loss: 2.0545 - val_acc: 0.5000
Epoch 9/100
10/10 [==============================] - ETA: 0s - loss: 2.0556 - acc: 0.5000 - val_loss: 2.0013 - val_acc: 0.5000
Epoch 10/100
10/10 [==============================] - ETA: 0s - loss: 2.0023 - acc: 0.5000 - val_loss: 1.9484 - val_acc: 0.5000
Epoch 11/100
10/10 [==============================] - ETA: 0s - loss: 1.9493 - acc: 0.5000 - val_loss: 1.8971 - val_acc: 0.5000
Epoch 12/100
10/10 [==============================] - ETA: 0s - loss: 1.8985 - acc: 0.5000 - val_loss: 1.8453 - val_acc: 0.5000
Epoch 13/100
10/10 [==============================] - ETA: 0s - loss: 1.8464 - acc: 0.5000 - val_loss: 1.7949 - val_acc: 0.5000
Epoch 14/100
10/10 [==============================] - ETA: 0s - loss: 1.7962 - acc: 0.5000 - val_loss: 1.7448 - val_acc: 0.5000
Epoch 15/100
10/10 [==============================] - ETA: 0s - loss: 1.7464 - acc: 0.5000 - val_loss: 1.6958 - val_acc: 0.5000
Epoch 16/100
10/10 [==============================] - ETA: 0s - loss: 1.6971 - acc: 0.5000 - val_loss: 1.6471 - val_acc: 0.5000
Epoch 17/100
10/10 [==============================] - ETA: 0s - loss: 1.6486 - acc: 0.5000 - val_loss: 1.5994 - val_acc: 0.5000
Epoch 18/100
10/10 [==============================] - ETA: 0s - loss: 1.6007 - acc: 0.5000 - val_loss: 1.5520 - val_acc: 0.7000
Epoch 19/100
10/10 [==============================] - ETA: 0s - loss: 1.5538 - acc: 0.7000 - val_loss: 1.5064 - val_acc: 0.7000
Epoch 20/100
10/10 [==============================] - ETA: 0s - loss: 1.5078 - acc: 0.7000 - val_loss: 1.4612 - val_acc: 0.7000
Epoch 21/100
10/10 [==============================] - ETA: 0s - loss: 1.4629 - acc: 0.7000 - val_loss: 1.4168 - val_acc: 0.7000
Epoch 22/100
10/10 [==============================] - ETA: 0s - loss: 1.4190 - acc: 0.7000 - val_loss: 1.3736 - val_acc: 0.7000
Epoch 23/100
10/10 [==============================] - ETA: 0s - loss: 1.3758 - acc: 0.7000 - val_loss: 1.3311 - val_acc: 0.8000
Epoch 24/100
10/10 [==============================] - ETA: 0s - loss: 1.3331 - acc: 0.8000 - val_loss: 1.2891 - val_acc: 0.8000
Epoch 25/100
10/10 [==============================] - ETA: 0s - loss: 1.2913 - acc: 0.8000 - val_loss: 1.2488 - val_acc: 0.9000
Epoch 26/100
10/10 [==============================] - ETA: 0s - loss: 1.2513 - acc: 0.9000 - val_loss: 1.2091 - val_acc: 0.9000
Epoch 27/100
10/10 [==============================] - ETA: 0s - loss: 1.2114 - acc: 0.9000 - val_loss: 1.1701 - val_acc: 0.9000
Epoch 28/100
10/10 [==============================] - ETA: 0s - loss: 1.1721 - acc: 0.9000 - val_loss: 1.1324 - val_acc: 0.9000
Epoch 29/100
10/10 [==============================] - ETA: 0s - loss: 1.1349 - acc: 0.9000 - val_loss: 1.0957 - val_acc: 0.9000
Epoch 30/100
10/10 [==============================] - ETA: 0s - loss: 1.0984 - acc: 0.9000 - val_loss: 1.0596 - val_acc: 0.9000
Epoch 31/100
10/10 [==============================] - ETA: 0s - loss: 1.0620 - acc: 0.9000 - val_loss: 1.0243 - val_acc: 0.9000
Epoch 32/100
10/10 [==============================] - ETA: 0s - loss: 1.0269 - acc: 0.9000 - val_loss: 0.9905 - val_acc: 1.0000
Epoch 33/100
10/10 [==============================] - ETA: 0s - loss: 0.9932 - acc: 1.0000 - val_loss: 0.9571 - val_acc: 1.0000
Epoch 34/100
10/10 [==============================] - ETA: 0s - loss: 0.9597 - acc: 1.0000 - val_loss: 0.9247 - val_acc: 1.0000
Epoch 35/100
10/10 [==============================] - ETA: 0s - loss: 0.9276 - acc: 1.0000 - val_loss: 0.8934 - val_acc: 1.0000
Epoch 36/100
10/10 [==============================] - ETA: 0s - loss: 0.8962 - acc: 1.0000 - val_loss: 0.8629 - val_acc: 1.0000
Epoch 37/100
10/10 [==============================] - ETA: 0s - loss: 0.8655 - acc: 1.0000 - val_loss: 0.8330 - val_acc: 1.0000
Epoch 38/100
10/10 [==============================] - ETA: 0s - loss: 0.8359 - acc: 1.0000 - val_loss: 0.8047 - val_acc: 1.0000
Epoch 39/100
10/10 [==============================] - ETA: 0s - loss: 0.8077 - acc: 1.0000 - val_loss: 0.7766 - val_acc: 1.0000
Epoch 40/100
10/10 [==============================] - ETA: 0s - loss: 0.7797 - acc: 1.0000 - val_loss: 0.7501 - val_acc: 1.0000
Epoch 41/100
10/10 [==============================] - ETA: 0s - loss: 0.7529 - acc: 1.0000 - val_loss: 0.7239 - val_acc: 1.0000
Epoch 42/100
10/10 [==============================] - ETA: 0s - loss: 0.7270 - acc: 1.0000 - val_loss: 0.6988 - val_acc: 1.0000
Epoch 43/100
10/10 [==============================] - ETA: 0s - loss: 0.7018 - acc: 1.0000 - val_loss: 0.6744 - val_acc: 1.0000
Epoch 44/100
10/10 [==============================] - ETA: 0s - loss: 0.6773 - acc: 1.0000 - val_loss: 0.6511 - val_acc: 1.0000
Epoch 45/100
10/10 [==============================] - ETA: 0s - loss: 0.6542 - acc: 1.0000 - val_loss: 0.6283 - val_acc: 1.0000
Epoch 46/100
10/10 [==============================] - ETA: 0s - loss: 0.6312 - acc: 1.0000 - val_loss: 0.6065 - val_acc: 1.0000
Epoch 47/100
10/10 [==============================] - ETA: 0s - loss: 0.6097 - acc: 1.0000 - val_loss: 0.5852 - val_acc: 1.0000
Epoch 48/100
10/10 [==============================] - ETA: 0s - loss: 0.5882 - acc: 1.0000 - val_loss: 0.5647 - val_acc: 1.0000
Epoch 49/100
10/10 [==============================] - ETA: 0s - loss: 0.5677 - acc: 1.0000 - val_loss: 0.5451 - val_acc: 1.0000
Epoch 50/100
10/10 [==============================] - ETA: 0s - loss: 0.5482 - acc: 1.0000 - val_loss: 0.5261 - val_acc: 1.0000
Epoch 51/100
10/10 [==============================] - ETA: 0s - loss: 0.5291 - acc: 1.0000 - val_loss: 0.5078 - val_acc: 1.0000
Epoch 52/100
10/10 [==============================] - ETA: 0s - loss: 0.5108 - acc: 1.0000 - val_loss: 0.4900 - val_acc: 1.0000
Epoch 53/100
10/10 [==============================] - ETA: 0s - loss: 0.4928 - acc: 1.0000 - val_loss: 0.4729 - val_acc: 1.0000
Epoch 54/100
10/10 [==============================] - ETA: 0s - loss: 0.4759 - acc: 1.0000 - val_loss: 0.4567 - val_acc: 1.0000
Epoch 55/100
10/10 [==============================] - ETA: 0s - loss: 0.4596 - acc: 1.0000 - val_loss: 0.4409 - val_acc: 1.0000
Epoch 56/100
10/10 [==============================] - ETA: 0s - loss: 0.4440 - acc: 1.0000 - val_loss: 0.4258 - val_acc: 1.0000
Epoch 57/100
10/10 [==============================] - ETA: 0s - loss: 0.4286 - acc: 1.0000 - val_loss: 0.4113 - val_acc: 1.0000
Epoch 58/100
10/10 [==============================] - ETA: 0s - loss: 0.4143 - acc: 1.0000 - val_loss: 0.3971 - val_acc: 1.0000
Epoch 59/100
10/10 [==============================] - ETA: 0s - loss: 0.4001 - acc: 1.0000 - val_loss: 0.3835 - val_acc: 1.0000
Epoch 60/100
10/10 [==============================] - ETA: 0s - loss: 0.3864 - acc: 1.0000 - val_loss: 0.3706 - val_acc: 1.0000
Epoch 61/100
10/10 [==============================] - ETA: 0s - loss: 0.3735 - acc: 1.0000 - val_loss: 0.3580 - val_acc: 1.0000
Epoch 62/100
10/10 [==============================] - ETA: 0s - loss: 0.3611 - acc: 1.0000 - val_loss: 0.3459 - val_acc: 1.0000
Epoch 63/100
10/10 [==============================] - ETA: 0s - loss: 0.3487 - acc: 1.0000 - val_loss: 0.3343 - val_acc: 1.0000
Epoch 64/100
10/10 [==============================] - ETA: 0s - loss: 0.3370 - acc: 1.0000 - val_loss: 0.3232 - val_acc: 1.0000
Epoch 65/100
10/10 [==============================] - ETA: 0s - loss: 0.3261 - acc: 1.0000 - val_loss: 0.3125 - val_acc: 1.0000
Epoch 66/100
10/10 [==============================] - ETA: 0s - loss: 0.3152 - acc: 1.0000 - val_loss: 0.3023 - val_acc: 1.0000
Epoch 67/100
10/10 [==============================] - ETA: 0s - loss: 0.3049 - acc: 1.0000 - val_loss: 0.2925 - val_acc: 1.0000
Epoch 68/100
10/10 [==============================] - ETA: 0s - loss: 0.2953 - acc: 1.0000 - val_loss: 0.2828 - val_acc: 1.0000
Epoch 69/100
10/10 [==============================] - ETA: 0s - loss: 0.2854 - acc: 1.0000 - val_loss: 0.2736 - val_acc: 1.0000
Epoch 70/100
10/10 [==============================] - ETA: 0s - loss: 0.2762 - acc: 1.0000 - val_loss: 0.2648 - val_acc: 1.0000
Epoch 71/100
10/10 [==============================] - ETA: 0s - loss: 0.2675 - acc: 1.0000 - val_loss: 0.2563 - val_acc: 1.0000
Epoch 72/100
10/10 [==============================] - ETA: 0s - loss: 0.2588 - acc: 1.0000 - val_loss: 0.2481 - val_acc: 1.0000
Epoch 73/100
10/10 [==============================] - ETA: 0s - loss: 0.2506 - acc: 1.0000 - val_loss: 0.2403 - val_acc: 1.0000
Epoch 74/100
10/10 [==============================] - ETA: 0s - loss: 0.2429 - acc: 1.0000 - val_loss: 0.2327 - val_acc: 1.0000
Epoch 75/100
10/10 [==============================] - ETA: 0s - loss: 0.2351 - acc: 1.0000 - val_loss: 0.2254 - val_acc: 1.0000
Epoch 76/100
10/10 [==============================] - ETA: 0s - loss: 0.2279 - acc: 1.0000 - val_loss: 0.2184 - val_acc: 1.0000
Epoch 77/100
10/10 [==============================] - ETA: 0s - loss: 0.2209 - acc: 1.0000 - val_loss: 0.2117 - val_acc: 1.0000
Epoch 78/100
10/10 [==============================] - ETA: 0s - loss: 0.2140 - acc: 1.0000 - val_loss: 0.2053 - val_acc: 1.0000
Epoch 79/100
10/10 [==============================] - ETA: 0s - loss: 0.2078 - acc: 1.0000 - val_loss: 0.1989 - val_acc: 1.0000
Epoch 80/100
10/10 [==============================] - ETA: 0s - loss: 0.2011 - acc: 1.0000 - val_loss: 0.1929 - val_acc: 1.0000
Epoch 81/100
10/10 [==============================] - ETA: 0s - loss: 0.1954 - acc: 1.0000 - val_loss: 0.1872 - val_acc: 1.0000
Epoch 82/100
10/10 [==============================] - ETA: 0s - loss: 0.1894 - acc: 1.0000 - val_loss: 0.1817 - val_acc: 1.0000
Epoch 83/100
10/10 [==============================] - ETA: 0s - loss: 0.1841 - acc: 1.0000 - val_loss: 0.1763 - val_acc: 1.0000
Epoch 84/100
10/10 [==============================] - ETA: 0s - loss: 0.1784 - acc: 1.0000 - val_loss: 0.1711 - val_acc: 1.0000
Epoch 85/100
10/10 [==============================] - ETA: 0s - loss: 0.1732 - acc: 1.0000 - val_loss: 0.1662 - val_acc: 1.0000
Epoch 86/100
10/10 [==============================] - ETA: 0s - loss: 0.1684 - acc: 1.0000 - val_loss: 0.1614 - val_acc: 1.0000
Epoch 87/100
10/10 [==============================] - ETA: 0s - loss: 0.1636 - acc: 1.0000 - val_loss: 0.1568 - val_acc: 1.0000
Epoch 88/100
10/10 [==============================] - ETA: 0s - loss: 0.1590 - acc: 1.0000 - val_loss: 0.1523 - val_acc: 1.0000
Epoch 89/100
10/10 [==============================] - ETA: 0s - loss: 0.1542 - acc: 1.0000 - val_loss: 0.1480 - val_acc: 1.0000
Epoch 90/100
10/10 [==============================] - ETA: 0s - loss: 0.1501 - acc: 1.0000 - val_loss: 0.1438 - val_acc: 1.0000
Epoch 91/100
10/10 [==============================] - ETA: 0s - loss: 0.1457 - acc: 1.0000 - val_loss: 0.1399 - val_acc: 1.0000
Epoch 92/100
10/10 [==============================] - ETA: 0s - loss: 0.1418 - acc: 1.0000 - val_loss: 0.1360 - val_acc: 1.0000
Epoch 93/100
10/10 [==============================] - ETA: 0s - loss: 0.1379 - acc: 1.0000 - val_loss: 0.1323 - val_acc: 1.0000
Epoch 94/100
10/10 [==============================] - ETA: 0s - loss: 0.1343 - acc: 1.0000 - val_loss: 0.1287 - val_acc: 1.0000
Epoch 95/100
10/10 [==============================] - ETA: 0s - loss: 0.1306 - acc: 1.0000 - val_loss: 0.1252 - val_acc: 1.0000
Epoch 96/100
10/10 [==============================] - ETA: 0s - loss: 0.1271 - acc: 1.0000 - val_loss: 0.1218 - val_acc: 1.0000
Epoch 97/100
10/10 [==============================] - ETA: 0s - loss: 0.1237 - acc: 1.0000 - val_loss: 0.1185 - val_acc: 1.0000
Epoch 98/100
10/10 [==============================] - ETA: 0s - loss: 0.1204 - acc: 1.0000 - val_loss: 0.1154 - val_acc: 1.0000
Epoch 99/100
10/10 [==============================] - ETA: 0s - loss: 0.1172 - acc: 1.0000 - val_loss: 0.1124 - val_acc: 1.0000
Epoch 100/100
10/10 [==============================] - ETA: 0s - loss: 0.1141 - acc: 1.0000 - val_loss: 0.1095 - val_acc: 1.0000
Test loss: 0.109465420246
Test accuracy: 1.0
```
## 輸出的圖片
plot_model 的執行，需要安裝 pydot 和 graphviz
```
pip3 install pydot
```
## plot_model 的環境安裝
在 windows 上，graphviz 必須手動安裝，安裝檔可以到他們官網下載。
[http://www.graphviz.org/Download_windows.php](http://www.graphviz.org/Download_windows.php)
安裝好之後必須在環境變數 path 加上 C:\Program Files (x86)\Graphviz2.30\bin 才能使用。
cmd 下輸入指令：
```
dot -version
```
若顯示以下訊息，表示正確安裝。
```
dot - graphviz version 2.30.1 (20130214.1330)
libdir = "C:\Program Files (x86)\Graphviz2.30\bin"
Activated plugin library: gvplugin_pango.dll
Using textlayout: textlayout:cairo
Activated plugin library: gvplugin_dot_layout.dll
Using layout: dot:dot_layout
Activated plugin library: gvplugin_core.dll
Using render: dot:core
Using device: dot:dot:core
The plugin configuration file:
C:\Program Files (x86)\Graphviz2.30\bin\config6
was successfully loaded.
render : cairo dot fig gd gdiplus map pic pov ps svg tk vml vrml xdot
layout : circo dot fdp neato nop nop1 nop2 osage patchwork sfdp twopi
textlayout : textlayout
device : bmp canon cmap cmapx cmapx_np dot emf emfplus eps fig gd gd2 gif gv imap imap_np ismap jpe jpeg jpg metafile pdf p
ic plain plain-ext png pov ps ps2 svg svgz tif tiff tk vml vmlz vrml wbmp xdot
loadimage : (lib) bmp eps gd gd2 gif jpe jpeg jpg png ps svg
```
## 參考文件
plot_model 在 windows 上的環境建立：[https://zhuanlan.zhihu.com/p/28158957](https://zhuanlan.zhihu.com/p/28158957)
graphviz 的使用：[https://www.openfoundry.org/tw/foss-programs/8820-graphviz-](https://www.openfoundry.org/tw/foss-programs/8820-graphviz-)
plot_model 的執行，需要安裝 pydot 和 graphviz
```
pip3 install pydot
```
## plot_model 的環境安裝
在 windows 上，graphviz 必須手動安裝，安裝檔可以到他們官網下載。
[http://www.graphviz.org/Download_windows.php](http://www.graphviz.org/Download_windows.php)
安裝好之後必須在環境變數 path 加上 C:\Program Files (x86)\Graphviz2.30\bin 才能使用。
cmd 下輸入指令：
```
dot -version
```
若顯示以下訊息，表示正確安裝。
```
dot - graphviz version 2.30.1 (20130214.1330)
libdir = "C:\Program Files (x86)\Graphviz2.30\bin"
Activated plugin library: gvplugin_pango.dll
Using textlayout: textlayout:cairo
Activated plugin library: gvplugin_dot_layout.dll
Using layout: dot:dot_layout
Activated plugin library: gvplugin_core.dll
Using render: dot:core
Using device: dot:dot:core
The plugin configuration file:
C:\Program Files (x86)\Graphviz2.30\bin\config6
was successfully loaded.
render : cairo dot fig gd gdiplus map pic pov ps svg tk vml vrml xdot
layout : circo dot fdp neato nop nop1 nop2 osage patchwork sfdp twopi
textlayout : textlayout
device : bmp canon cmap cmapx cmapx_np dot emf emfplus eps fig gd gd2 gif gv imap imap_np ismap jpe jpeg jpg metafile pdf p
ic plain plain-ext png pov ps ps2 svg svgz tif tiff tk vml vmlz vrml wbmp xdot
loadimage : (lib) bmp eps gd gd2 gif jpe jpeg jpg png ps svg
```
## 參考文件
plot_model 在 windows 上的環境建立：[https://zhuanlan.zhihu.com/p/28158957](https://zhuanlan.zhihu.com/p/28158957)
graphviz 的使用：[https://www.openfoundry.org/tw/foss-programs/8820-graphviz-](https://www.openfoundry.org/tw/foss-programs/8820-graphviz-)
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