我们从Python开源项目中，提取了以下36个代码示例，用于说明如何使用torch.nn.init.calculate_gain()。
def _initialize_weights(self): init.orthogonal(self.conv1.weight, init.calculate_gain('relu')) init.orthogonal(self.conv2.weight, init.calculate_gain('relu')) init.orthogonal(self.conv3.weight, init.calculate_gain('relu')) init.orthogonal(self.conv4.weight)
def __init__(self, input_dim, dropout=0, softplus_boost=1.0): super(ProposalBeta, self).__init__() self.lin1 = nn.Linear(input_dim, input_dim) self.lin2 = nn.Linear(input_dim, 2) self.drop = nn.Dropout(dropout) self.softplus_boost = softplus_boost init.xavier_uniform(self.lin1.weight, gain=init.calculate_gain('relu')) init.xavier_uniform(self.lin2.weight)
def __init__(self, input_dim, output_dim, dropout=0, softplus_boost=1.0): super(ProposalMultivariateNormal, self).__init__() self.mean_lin1 = nn.Linear(input_dim, input_dim) self.mean_drop = nn.Dropout(dropout) self.mean_lin2 = nn.Linear(input_dim, output_dim) self.vars_lin1 = nn.Linear(input_dim, input_dim) self.vars_drop = nn.Dropout(dropout) self.vars_lin2 = nn.Linear(input_dim, output_dim) self.softplus_boost = softplus_boost init.xavier_uniform(self.mean_lin1.weight, gain=init.calculate_gain('relu')) init.xavier_uniform(self.mean_lin2.weight) init.xavier_uniform(self.vars_lin1.weight, gain=init.calculate_gain('relu')) init.xavier_uniform(self.vars_lin2.weight)
def test_calculate_gain_linear(self): for fn in ['linear', 'conv1d', 'conv2d', 'conv3d', 'conv_transpose2d', 'conv_transpose2d', 'conv_transpose3d']: gain = init.calculate_gain(fn) self.assertEqual(gain, 1)
def __init__(self, input_dim, output_dim, dropout=0, softmax_boost=1.0): super(ProposalUniformDiscrete, self).__init__() self.lin1 = nn.Linear(input_dim, input_dim) self.lin2 = nn.Linear(input_dim, output_dim) self.drop = nn.Dropout(dropout) self.softmax_boost = softmax_boost init.xavier_uniform(self.lin1.weight, gain=init.calculate_gain('relu')) init.xavier_uniform(self.lin2.weight)
def __init__(self, input_dim, dropout=0): super(ProposalNormal, self).__init__() self.lin1 = nn.Linear(input_dim, input_dim) self.lin2 = nn.Linear(input_dim, 2) self.drop = nn.Dropout(dropout) init.xavier_uniform(self.lin1.weight, gain=init.calculate_gain('relu')) init.xavier_uniform(self.lin2.weight)
def __init__(self, input_dim, dropout=0): super(ProposalLaplace, self).__init__() self.lin1 = nn.Linear(input_dim, input_dim) self.lin2 = nn.Linear(input_dim, 2) self.drop = nn.Dropout(dropout) init.xavier_uniform(self.lin1.weight, gain=init.calculate_gain('relu')) init.xavier_uniform(self.lin2.weight)
def __init__(self, input_dim, dropout=0, softmax_boost=1.0): super(ProposalFlip, self).__init__() self.lin1 = nn.Linear(input_dim, input_dim) self.lin2 = nn.Linear(input_dim, 1) self.drop = nn.Dropout(dropout) self.softmax_boost = softmax_boost init.xavier_uniform(self.lin1.weight, gain=init.calculate_gain('relu')) init.xavier_uniform(self.lin2.weight)
def __init__(self, input_dim, output_dim, dropout=0, softmax_boost=1.0): super(ProposalDiscrete, self).__init__() self.lin1 = nn.Linear(input_dim, input_dim) self.lin2 = nn.Linear(input_dim, output_dim) self.drop = nn.Dropout(dropout) self.softmax_boost = softmax_boost init.xavier_uniform(self.lin1.weight, gain=init.calculate_gain('relu'))
def __init__(self, input_dim, dropout=0, softplus_boost=1.0): super(ProposalUniformContinuous, self).__init__() self.lin1 = nn.Linear(input_dim, input_dim) self.lin2 = nn.Linear(input_dim, 2) self.drop = nn.Dropout(dropout) self.softplus_boost = softplus_boost init.xavier_uniform(self.lin1.weight, gain=init.calculate_gain('relu')) init.xavier_uniform(self.lin2.weight)
def __init__(self, input_dim, mixture_components=10, dropout=0): super(ProposalUniformContinuousAlt, self).__init__() self.mixture_components = mixture_components self.output_dim = 3 * mixture_components self.lin1 = nn.Linear(input_dim, input_dim) self.lin2 = nn.Linear(input_dim, self.output_dim) self.drop = nn.Dropout(dropout) init.xavier_uniform(self.lin1.weight, gain=init.calculate_gain('relu')) init.xavier_uniform(self.lin2.weight)
def __init__(self, input_dim, dropout=0, softplus_boost=1.0): super(ProposalGamma, self).__init__() self.lin1 = nn.Linear(input_dim, input_dim) self.lin2 = nn.Linear(input_dim, 2) self.drop = nn.Dropout(dropout) self.softplus_boost = softplus_boost init.xavier_uniform(self.lin1.weight, gain=init.calculate_gain('relu')) init.xavier_uniform(self.lin2.weight)
def test_calculate_gain_nonlinear(self): for fn in ['sigmoid', 'tanh', 'relu', 'leaky_relu']: gain = init.calculate_gain(fn) if fn == 'sigmoid': self.assertEqual(gain, 1) elif fn == 'tanh': # 5 / 3 self.assertEqual(gain, 1.6666666666666667) elif fn == 'relu': # sqrt(2) self.assertEqual(gain, 1.4142135623730951) elif fn == 'leaky_relu': # sqrt(2 / 1 + slope^2)) self.assertEqual(gain, 1.4141428569978354)
def test_calculate_gain_leaky_relu(self): for param in [None, 0, 0.01, 10]: gain = init.calculate_gain('leaky_relu', param) if param is None: # Default slope is 0.01 self.assertEqual(gain, 1.4141428569978354) elif param == 0: # No slope = same gain as normal ReLU self.assertEqual(gain, 1.4142135623730951) elif param == 0.01: self.assertEqual(gain, 1.4141428569978354) elif param == 10: self.assertEqual(gain, 0.14071950894605836)
def test_calculate_gain_leaky_relu_only_accepts_numbers(self): for param in [True, [1], {'a': 'b'}]: with self.assertRaises(ValueError): init.calculate_gain('leaky_relu', param)
def test_calculate_gain_only_accepts_valid_nonlinearities(self): for n in [2, 5, 25]: # Generate random strings of lengths that definitely aren't supported random_string = ''.join([random.choice(string.ascii_lowercase) for i in range(n)]) with self.assertRaises(ValueError): init.calculate_gain(random_string)
def initializationhelper(param, nltype): c = 0.1 torchinit.uniform(param.weight, a=-c, b=c) #torchinit.xavier_uniform(param.weight, gain=c*torchinit.calculate_gain(nltype)) c = 0.1 torchinit.uniform(param.bias, a=-c, b=c)
def reset_parameters(self): tanh_gain = weight_init.calculate_gain('tanh') linear_gain = weight_init.calculate_gain('linear') weight_init.xavier_uniform(self.W_s1.data, tanh_gain) weight_init.xavier_uniform(self.W_s2.data, linear_gain)
def reset_parameters(self): linear_gain = weight_init.calculate_gain('linear') weight_init.xavier_uniform(self.W_x.data, linear_gain) weight_init.xavier_uniform(self.W_y.data, linear_gain) weight_init.xavier_uniform(self.W_z.data, linear_gain)
def initWeight(self): for name, params in self.named_parameters(): # weight?xavier???? if 'weight' in name: init.xavier_uniform(params, init.calculate_gain('relu')) # bias?0???? else: init.constant(params, 0)
def _initialize_weights(self): init.orthogonal(self.conv1.weight, init.calculate_gain('relu')) init.orthogonal(self.conv2.weight, init.calculate_gain('relu')) init.orthogonal(self.conv3.weight, init.calculate_gain('relu')) init.orthogonal(self.conv4.weight) # Create the super-resolution model by using the above model definition.
