'dimension':X_train_bin.shape[1],
'bias': None,
'value': None,
'layer_type': 'binary',
'layer_name': 'input'
},
1: {
'dimension':200,
'bias': None,
'value': None,
'layer_type': 'binary',
'layer_name': 'hidden'
}
}
learning_rate, weight_decay, momentum= step_iterator(0.01,0.001,-0.002), step_iterator(2e-5,2e-5,0), step_iterator(0.5,0.9,0.05)
batch_func = batch_func_generator(X_train_bin, batch_size = 100)
rbm = BinRBM(layers_dict = RBMlayers_dict, weight_list = None, random_state = None)
print 'Training starts'
rbm.fit(batch_func, PCD = False, error_function = 'recon',learning_rate = learning_rate, momentum = momentum,
weight_decay = weight_decay, k = 1, perst_size = 100, n_iter = 500, verbose = True)
combined = rbm.transform(X_train_bin)
combined_test = rbm.transform(X_test_bin)
parameters = {'alpha':[1e-1,1e-2,1e-3,1e-4,1e-5], 'n_iter': [10, 50 ,100]}
clf = GridSearchCV(SGDClassifier(loss="hinge", penalty="l2", n_iter = 100, random_state = 500), parameters)
clf.fit(combined,labels_train)
y_pred = clf.predict(combined_test)
print 'toplam: ', labels_test.shape[0], 'dogru: ', (y_pred == labels_test).sum()
print clf.best_estimator_
parameters = [{'kernel': ['rbf'], 'gamma': [1e-1,1e-2,1e-3, 1e-4],
'dimension':B_train.shape[1],
'bias': None,
'value': None,
'layer_type': 'linear',
'layer_name': 'input'
},
1: {
'dimension':500,
'bias': None,
'value': None,
'layer_type': 'binary',
'layer_name': 'hidden'
} }
learning_rate, weight_decay, momentum= step_iterator(1,1,0), step_iterator(0,0,0), step_iterator(0,0,0)
batch_func = batch_func_generator(R_train, batch_size = 50)
rbmR = GaussRBM(layers_dict = RBMRlayers_dict, weight_list = None, random_state = random_state)
print 'Training starts'
rbmR.fit(batch_func, PCD = False, error_function = 'recon',learning_rate = learning_rate, momentum = momentum,
weight_decay = weight_decay, k = 1, perst_size = 100, n_iter = 20, verbose = True)
# sparsity_cond = True, sparsity_target = 0.01, sparsity_lambda = 1e-6)
batch_func = batch_func_generator(G_train, batch_size = 50)
rbmG = GaussRBM(layers_dict = RBMGlayers_dict, weight_list = None, random_state = random_state)
print 'Training starts'
rbmG.fit(batch_func, PCD = False, error_function = 'recon',learning_rate = learning_rate, momentum = momentum,
weight_decay = weight_decay, k = 1, perst_size = 100, n_iter = 20, verbose = True)
# sparsity_cond = True, sparsity_target = 0.01, sparsity_lambda = 1e-6)
batch_func = batch_func_generator(B_train, batch_size = 50)
rbmB = GaussRBM(layers_dict = RBMBlayers_dict, weight_list = None, random_state = random_state)
print 'Training starts'
rbmB.fit(batch_func, PCD = False, error_function = 'recon',learning_rate = learning_rate, momentum = momentum,