本文整理汇总了Python中classifier.Classifier.__init__方法的典型用法代码示例。如果您正苦于以下问题：Python Classifier.__init__方法的具体用法？Python Classifier.__init__怎么用？Python Classifier.__init__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classifier.Classifier的用法示例。
在下文中一共展示了Classifier.__init__方法的7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: __init__
# 需要导入模块: from classifier import Classifier [as 别名]
# 或者: from classifier.Classifier import __init__ [as 别名]
def __init__(self, D, H, W, K, iternum):
Classifier.__init__(self, D, H, W, K, iternum)
self.L = 100 # size of hidden layer
""" Layer 1 Parameters """
# weight matrix: [M * L]
self.A1 = 0.01 * np.random.randn(self.M, self.L)
# bias: [1 * L]
self.b1 = np.zeros((1,self.L))
""" Layer 3 Parameters """
# weight matrix: [L * K]
self.A3 = 0.01 * np.random.randn(self.L, K)
# bias: [1 * K]
self.b3 = np.zeros((1,K))
""" Hyperparams """
# learning rate
self.rho = 1e-2
# momentum
self.mu = 0.9
# reg strencth
self.lam = 0.1
# velocity for A1: [M * L]
self.v1 = np.zeros((self.M, self.L))
# velocity for A3: [L * K]
self.v3 = np.zeros((self.L, K))
return示例2: __init__
# 需要导入模块: from classifier import Classifier [as 别名]
# 或者: from classifier.Classifier import __init__ [as 别名]
def __init__(self, fname, *args, **kargs):
Classifier.__init__(self, fname, *args, **kargs)
# sometimes a threshold value is trained during Bayesian
# classification to avoid classifying too many 'documents' as
# one kind or the other
self.thresholds = [1.0, 1.0]示例3: __init__
# 需要导入模块: from classifier import Classifier [as 别名]
# 或者: from classifier.Classifier import __init__ [as 别名]
def __init__(self, D, H, W, K, iternum):
Classifier.__init__(self, D, H, W, K, iternum)
""" Parameters """
# weight matrix: [M * K]
self.A = 0.01 * np.random.randn(self.M, K)
# bias: [1 * K]
self.b = np.zeros((1,K))
""" Hyperparams """
# learning rate
self.rho = 1e-5
# momentum
self.mu = 0.9
# reg strength
self.lam = 1e1
# velocity for A: [M * K]
self.v = np.zeros((self.M, K))
return示例4: __init__
# 需要导入模块: from classifier import Classifier [as 别名]
# 或者: from classifier.Classifier import __init__ [as 别名]
def __init__(self, rawfname, min_occurences=5, **kargs):
Classifier.__init__(self, rawfname, **kargs)
self.min_occurences = min_occurences
# Maintains all training examples
self.all_training_examples = []
# Each example contains only keys for features which occurred more
# than <min_occurences> times in the training set
self.shrunk_training_examples = []
# { feature -> num times <feature> was seen }
self.all_features = {}
self.model = None
self.filesubset = kargs.get('filesubset', 3000)
self.max_iter = kargs.get('max_iter', 4)示例5: __init__
# 需要导入模块: from classifier import Classifier [as 别名]
# 或者: from classifier.Classifier import __init__ [as 别名]
def __init__(self, D, H, W, K, iternum):
Classifier.__init__(self, D, H, W, K, iternum)
"""
Layer 1 Parameters (Conv 32 x 32 x 16)
K = 16, F = 5, S = 1, P = 2
weight matrix: [K1 * D * F1 * F1]
bias: [K1 * 1]
"""
K1, F1, self.S1, self.P1 = 16, 5, 1, 2
self.A1 = 0.01 * np.random.randn(K1, D, F1, F1)
self.b1 = np.zeros((K1, 1))
H1 = (H - F1 + 2*self.P1) / self.S1 + 1
W1 = (W - F1 + 2*self.P1) / self.S1 + 1
"""
Layer 3 Parameters (Pool 16 x 16 x 16)
K = 16, F = 2, S = 2
"""
K3, self.F3, self.S3 = K1, 2, 2
H3 = (H1 - self.F3) / self.S3 + 1
W3 = (W1 - self.F3) / self.S3 + 1
"""
Layer 4 Parameters (Conv 16 x 16 x 20)
K = 20, F = 5, S = 1, P = 2
weight matrix: [K4 * K3 * F4 * F4]
bias: [K4 * 1]
"""
K4, F4, self.S4, self.P4 = 20, 5, 1, 2
self.A4 = 0.01 * np.random.randn(K4, K3, F4, F4)
self.b4 = np.zeros((K4, 1))
H4 = (H3 - F4 + 2*self.P4) / self.S4 + 1
W4 = (W3 - F4 + 2*self.P4) / self.S4 + 1
"""
Layer 6 Parameters (Pool 8 x 8 x 20)
K = 20, F = 2, S = 2
"""
K6, self.F6, self.S6 = K4, 2, 2
H6 = (H4 - self.F6) / self.S6 + 1
W6 = (W4 - self.F6) / self.S6 + 1
"""
Layer 7 Parameters (Conv 8 x 8 x 20)
K = 20, F = 5, S = 1, P = 2
weight matrix: [K7 * K6 * F7 * F7]
bias: [K7 * 1]
"""
K7, F7, self.S7, self.P7 = 20, 5, 1, 2
self.A7 = 0.01 * np.random.randn(K7, K6, F7, F7)
self.b7 = np.zeros((K7, 1))
H7 = (H6 - F7 + 2*self.P7) / self.S7 + 1
W7 = (W6 - F7 + 2*self.P7) / self.S7 + 1
"""
Layer 9 Parameters (Pool 4 x 4 x 20)
K = 20, F = 2, S = 2
"""
K9, self.F9, self.S9 = K7, 2, 2
H9 = (H7 - self.F9) / self.S9 + 1
W9 = (W7 - self.F9) / self.S9 + 1
"""
Layer 10 Parameters (FC 1 x 1 x K)
weight matrix: [(K6 * H_6 * W_6) * K]
bias: [1 * K]
"""
self.A10 = 0.01 * np.random.randn(K9 * H9 * W9, K)
self.b10 = np.zeros((1, K))
""" Hyperparams """
# learning rate
self.rho = 1e-2
# momentum
self.mu = 0.9
# reg strength
self.lam = 0.1
# velocity for A1: [K1 * D * F1 * F1]
self.v1 = np.zeros((K1, D, F1, F1))
# velocity for A4: [K4 * K3 * F4 * F4]
self.v4 = np.zeros((K4, K3, F4, F4))
# velocity for A7: [K7 * K6 * F7 * F7]
self.v7 = np.zeros((K7, K6, F7, F7))
# velocity for A10: [(K9 * H9 * W9) * K]
self.v10 = np.zeros((K9 * H9 * W9, K))
return示例6: __init__
# 需要导入模块: from classifier import Classifier [as 别名]
# 或者: from classifier.Classifier import __init__ [as 别名]
def __init__(self, root_dir, input_text, config_dirs):
Classifier.__init__(self, input_text)
self.master_word_list = []
self.word_features = []
self.configs = utils.load_json_file(config_dirs)示例7: __init__
# 需要导入模块: from classifier import Classifier [as 别名]
# 或者: from classifier.Classifier import __init__ [as 别名]
def __init__(self, feature):
Classifier.__init__(self, feature)
self.threshold = None