技術標籤：神經網路python深度學習

Win 10 解決執行Keras中的plot_model()函式時的報錯，ImportError: 'Failed to import pydot. You must `pip install pydot` and install graphviz (https://graphviz.gitlab.io/download/), ', 'for `pydotprint` to work.'

Keras.__version__ == 2.4.3

plot_model()是將Keras中的神經網路的模型進行視覺化處理的函式。當win 10 中執行時，會出現以下報錯：ImportError: 'Failed to import pydot. You must `pip install pydot` and install graphviz

看了其他許多部落格後，都無法解決。在這些部落格的基礎上，和檢視原始碼後，我總結了以下的解決方法：

START:

STEP 1. 下載並安裝graphviz 2.38。注意：設定安裝路徑為"C:\ProgramFiles\att\Graphviz\bin"，並將該路徑新增到系統的Path環境變數中。軟體下載連結為 https://softpedia-secure-download.com/dl/b07f24905c8ad73c39308d61e31f96dc/5fe6c2c9/100124651/software/other_tools/graphviz-2.38.msi

STEP 2. 在cmd命令列中執行pip install pydot_ng，來安裝pydot_ng包。

END.

測試程式碼借鑑 https://blog.csdn.net/qq_34564612/article/details/78851001?utm_medium=distribute.pc_relevant.none-task-blog-searchFromBaidu-2.control&depth_1-utm_source=distribute.pc_relevant.none-task-blog-searchFromBaidu-2.control 中的程式碼。

from keras.models import Model
from keras.layers import LSTM, Activation, Input
import numpy as np
from keras.utils.vis_utils import plot_model

data_dim = 1
timesteps = 12
num_classes = 4

inputs = Input(shape=(12,1))
lstm1 = LSTM(32, return_sequences=True)(inputs)
lstm2 = LSTM(4 , return_sequences=True)(lstm1)
outputs = Activation('softmax')(lstm2)
model = Model(inputs=inputs,outputs=outputs)
model.compile(loss='categorical_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

x_train = np.random.random((1000, timesteps, data_dim))
y_train = np.random.random((1000, timesteps, num_classes))

x_val = np.random.random((100, timesteps, data_dim))
y_val = np.random.random((100, timesteps, num_classes))

model.fit(x_train, y_train,
          batch_size=64, epochs=5,
          validation_data=(x_val, y_val))
#模型視覺化
plot_model(model, to_file='model.png')
x = np.arange(12).reshape(1,12,1)
a = model.predict(x,batch_size=64)
print a
 

來自 <https://blog.csdn.net/qq_34564612/article/details/78851001?utm_medium=distribute.pc_relevant.none-task-blog-searchFromBaidu-2.control&depth_1-utm_source=distribute.pc_relevant.none-task-blog-searchFromBaidu-2.control>

Result: