本文整理汇总了Python中sklearn.neural_network.BernoulliRBM._sample_hiddens方法的典型用法代码示例。如果您正苦于以下问题：Python BernoulliRBM._sample_hiddens方法的具体用法？Python BernoulliRBM._sample_hiddens怎么用？Python BernoulliRBM._sample_hiddens使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.neural_network.BernoulliRBM的用法示例。
在下文中一共展示了BernoulliRBM._sample_hiddens方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_sample_hiddens
# 需要导入模块: from sklearn.neural_network import BernoulliRBM [as 别名]
# 或者: from sklearn.neural_network.BernoulliRBM import _sample_hiddens [as 别名]
def test_sample_hiddens():
rng = np.random.RandomState(0)
X = Xdigits[:100]
rbm1 = BernoulliRBM(n_components=2, batch_size=5, n_iter=5, random_state=42)
rbm1.fit(X)
h = rbm1._mean_hiddens(X[0])
hs = np.mean([rbm1._sample_hiddens(X[0], rng) for i in range(100)], 0)
assert_almost_equal(h, hs, decimal=1)示例2: test_gibbs
# 需要导入模块: from sklearn.neural_network import BernoulliRBM [as 别名]
# 或者: from sklearn.neural_network.BernoulliRBM import _sample_hiddens [as 别名]
def test_gibbs():
rng = np.random.RandomState(42)
X = Xdigits[:100]
rbm1 = BernoulliRBM(n_components=2, batch_size=5,
n_iter=5, random_state=rng)
rbm1.fit(X)
Xt1 = np.mean([rbm1.gibbs(X[0]) for i in range(100)], 0)
Xt2 = np.mean([rbm1._sample_visibles(rbm1._sample_hiddens(X[0], rng), rng)
for i in range(1000)], 0)
assert_almost_equal(Xt1, Xt2, decimal=1)示例3: print
# 需要导入模块: from sklearn.neural_network import BernoulliRBM [as 别名]
# 或者: from sklearn.neural_network.BernoulliRBM import _sample_hiddens [as 别名]
print("Debut training RBM1")
print(X_train.shape)
t0 = time.clock()
rbm_layer_1.fit(X_train)
print(time.clock() - t0)
# creation d'une base de train a partir d'echantillonnage
# de variable cachees du premier rbm
n_sample_second_layer_training = int(X.shape[0])
H1_train = np.zeros(shape=(n_sample_second_layer_training, rbm_layer_1.n_components))
H1_label_train = np.zeros(shape = (n_sample_second_layer_training, 1))
comp = 0
while (comp < n_sample_second_layer_training):
rng = check_random_state(rbm_layer_1.random_state)
randTemp = rd.randint(0, X.shape[0] - 1)
H1_train[comp] = rbm_layer_1._sample_hiddens(X[randTemp], rng)
H1_label_train[comp] = Y[randTemp]
comp = comp + 1
#-------------------- Training du second rbm --------------------
# grid_search pour determiner parametres optimaux du deuxieme RBM.
grid_search_test = False
if grid_search_test:
# Models we will use
logistic = linear_model.LogisticRegression() # pour comparaison avec RBM + regression logistique
rbm = BernoulliRBM(random_state=0, verbose=True)
classifier = Pipeline(steps=[('rbm_layer_2', rbm), ('logistic', logistic)])
parameters = {'rbm_layer_2__learning_rate': np.linspace(0.04, 0.05, num=10)}