Python

Python-mlp

待了解,可參考

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from sklearn.datasets import load_digits
import matplotlib.pyplot as plt
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import StandardScaler
digits = load_digits() #手寫數字資料庫
scaler = StandardScaler()
scaler.fit(digits.data)
X_scaled = scaler.transform(digits.data)
#多層感知機類神經網路(Multi-Layer Perceptron Neural Network, MLP)
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30), activation='logistic', max_iter = 1000)
mlp.fit(X_scaled,digits.target)
predicted = mlp.predict(X_scaled)
fig = plt.figure(figsize = (8,8))
fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)
for i in range(36):
    ax = fig.add_subplot(6, 6, i+1, xticks=[], yticks=[])
    ax.imshow(digits.images[i],cmap=plt.cm.binary,interpolation='nearest')
    ax.text(0, 7, str('{}-{}'.format(digits.target[i],predicted[i])), color="red", fontsize = 20)
res = []
for i, j in zip(digits.target,predicted):
    res.append(i==j)
sum(res) / len(digits.target)