pre_l06_update1

L06/Old/ANN_example.py

-#%%
-
-import pandas as pd
-import matplotlib.pyplot as plt
-from sklearn.datasets import load_diabetes
-from sklearn.neural_network import MLPRegressor
-import numpy as np
-
-
-#%% data
-
-x = np.linspace(-10,10,1000)
-y = 2*np.tanh(2*x - 12) - 3*np.tanh(2*x - 4)
-
-plt.plot(x,y, '.')
-
-x = x.reshape(-1,1) # Scikit-algoritmer kræver (:,1)-format
-
-
-#%% fit model
-
-mlp = MLPRegressor(activation = 'tanh', # aktiveringsfunktion 
-                   hidden_layer_sizes = 2, # antal skjulte neuroner
-                   alpha = 1e-5, # regulariseringsparameter, her meget lille
-                   solver = 'lbfgs', # quasi-Newton solver
-                   max_iter=1000,
-                   verbose = True)
-mlp.fit(x,y)
-
-plt.plot(x,y)
-plt.plot(x, mlp.predict(x), 'rx', ms=1)
-
-mlp.coefs_ # w-parametre
-mlp.intercepts_ # = bias led
-
-#%% data 2 - Til øvelserne..
-
-x = np.linspace(-3,3,1000)
-y = np.sinc(x)
-
-plt.plot(x,y, '.')
-
-x = x.reshape(-1,1) # Scikit-algoritmer kræver (:,1)-format
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