本文整理汇总了Python中cntk.relu方法的典型用法代码示例。如果您正苦于以下问题：Python cntk.relu方法的具体用法？Python cntk.relu怎么用？Python cntk.relu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cntk的用法示例。
在下文中一共展示了cntk.relu方法的14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: create_basic_model
# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import relu [as 别名]
def create_basic_model(input, out_dims):
net = C.layers.Convolution(
(5, 5), 32, init=C.initializer.glorot_uniform(), activation=C.relu, pad=True
)(input)
net = C.layers.MaxPooling((3, 3), strides=(2, 2))(net)
net = C.layers.Convolution(
(5, 5), 32, init=C.initializer.glorot_uniform(), activation=C.relu, pad=True
)(net)
net = C.layers.MaxPooling((3, 3), strides=(2, 2))(net)
net = C.layers.Convolution(
(5, 5), 64, init=C.initializer.glorot_uniform(), activation=C.relu, pad=True
)(net)
net = C.layers.MaxPooling((3, 3), strides=(2, 2))(net)
net = C.layers.Dense(64, init=C.initializer.glorot_uniform())(net)
net = C.layers.Dense(out_dims, init=C.initializer.glorot_uniform(), activation=None)(net)
return net 示例2: create_vgg9_model
# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import relu [as 别名]
def create_vgg9_model(input, out_dims):
with C.layers.default_options(activation=C.relu):
model = C.layers.Sequential([
C.layers.For(range(3), lambda i: [
C.layers.Convolution(
(3, 3), [64, 96, 128][i], init=C.initializer.glorot_uniform(), pad=True
),
C.layers.Convolution(
(3, 3), [64, 96, 128][i], init=C.initializer.glorot_uniform(), pad=True
),
C.layers.MaxPooling((3, 3), strides=(2, 2))
]),
C.layers.For(range(2), lambda: [
C.layers.Dense(1024, init=C.initializer.glorot_uniform())
]),
C.layers.Dense(out_dims, init=C.initializer.glorot_uniform(), activation=None)
])
return model(input) 示例3: relu
# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import relu [as 别名]
def relu(x, alpha=0., max_value=None, threshold=0.):
if alpha != 0.:
if threshold != 0.:
negative_part = C.relu(-x + threshold)
else:
negative_part = C.relu(-x)
if threshold != 0.:
x = x * C.greater(x, threshold)
else:
x = C.relu(x)
if max_value is not None:
x = C.clip(x, 0.0, max_value)
if alpha != 0.:
x -= alpha * negative_part
return x 示例4: create_model
# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import relu [as 别名]
def create_model(features):
'''
This function creates the architecture model.
:param features: The input features.
:return: The output of the network which its dimentionality is num_classes.
'''
with C.layers.default_options(init = C.layers.glorot_uniform(), activation = C.ops.relu):
# Hidden input dimention
hidden_dim = 64
# Encoder
encoder_out = C.layers.Dense(hidden_dim, activation=C.relu)(features)
encoder_out = C.layers.Dense(int(hidden_dim / 2.0), activation=C.relu)(encoder_out)
# Decoder
decoder_out = C.layers.Dense(int(hidden_dim / 2.0), activation=C.relu)(encoder_out)
decoder_out = C.layers.Dense(hidden_dim, activation=C.relu)(decoder_out)
decoder_out = C.layers.Dense(feature_dim, activation=C.sigmoid)(decoder_out)
return decoder_out
# Initializing the model with normalized input. 示例5: create_terse_model
# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import relu [as 别名]
def create_terse_model(input, out_dims):
with C.layers.default_options(activation=C.relu):
model = C.layers.Sequential([
C.layers.For(range(3), lambda i: [
C.layers.Convolution(
(5, 5), [32, 32, 64][i], init=C.initializer.glorot_uniform(), pad=True
),
C.layers.MaxPooling((3, 3), strides=(2, 2))
]),
C.layers.Dense(64, init=C.initializer.glorot_uniform()),
C.layers.Dense(out_dims, init=C.initializer.glorot_uniform(), activation=None)
])
return model(input) 示例6: create_dropout_model
# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import relu [as 别名]
def create_dropout_model(input, out_dims):
with C.layers.default_options(activation=C.relu):
model = C.layers.Sequential([
C.layers.For(range(3), lambda i: [
C.layers.Convolution(
(5, 5), [32, 32, 64][i], init=C.initializer.glorot_uniform(), pad=True
),
C.layers.MaxPooling((3, 3), strides=(2, 2))
]),
C.layers.Dense(64, init=C.initializer.glorot_uniform()),
C.layers.Dropout(0.25),
C.layers.Dense(out_dims, init=C.initializer.glorot_uniform(), activation=None)
])
return model(input) 示例7: convolution_bn
# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import relu [as 别名]
def convolution_bn(input, filter_size, num_filters, strides=(1, 1),
init=C.he_normal(), activation=C.relu):
if activation is None:
activation = lambda x: x
r = C.layers.Convolution(
filter_size, num_filters,
strides=strides, init=init,
activation=None, pad=True, bias=False
)(input)
# r = C.layers.BatchNormalization(map_rank=1)(r)
return activation(r) 示例8: resnet_basic_inc
# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import relu [as 别名]
def resnet_basic_inc(input, num_filters):
c1 = convolution_bn(input, (3, 3), num_filters, strides=(2, 2))
c2 = convolution_bn(c1, (3, 3), num_filters, activation=None)
s = convolution_bn(input, (1, 1), num_filters, strides=(2, 2), activation=None)
return C.relu(c2 + s) 示例9: test_relu
# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import relu [as 别名]
def test_relu():
assert_cntk_ngraph_array_equal(C.relu([-2, -1., 0., 1., 2.]))
assert_cntk_ngraph_array_equal(C.relu([0.]))
assert_cntk_ngraph_array_equal(C.relu([-0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1]))
assert_cntk_ngraph_array_equal(C.relu([[1, 2, 3], [4, 5, 6]]))
assert_cntk_ngraph_array_equal(C.relu([[-3, -2, -1], [1, 2, 3]])) 示例10: bn_relu
# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import relu [as 别名]
def bn_relu(input, name=""):
return bn(input, activation=C.relu, name=name) 示例11: conv_bn_relu
# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import relu [as 别名]
def conv_bn_relu(input, filter_shape, num_filters, strides=(1,1), init=C.he_normal(), name=""):
return conv_bn(input, filter_shape, num_filters, strides, init, activation=C.relu, name=name) 示例12: conv_bn_relu_nopad
# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import relu [as 别名]
def conv_bn_relu_nopad(input, filter_shape, num_filters, strides=(1,1), init=C.he_normal(), name=""):
return conv_bn_nopad(input, filter_shape, num_filters, strides, init, activation=C.relu, name=name) 示例13: relu
# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import relu [as 别名]
def relu(x, alpha=0., max_value=None):
if alpha != 0.:
negative_part = C.relu(-x)
x = C.relu(x)
if max_value is not None:
x = C.clip(x, 0.0, max_value)
if alpha != 0.:
x -= alpha * negative_part
return x 示例14: D
# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import relu [as 别名]
def D(x_img, x_code):
'''
Detector network architecture
Args:
x_img: cntk.input_variable represent images to network
x_code: cntk.input_variable represent conditional code to network
'''
def bn_with_leaky_relu(x, leak=0.2):
h = C.layers.BatchNormalization(map_rank=1)(x)
r = C.param_relu(C.constant((np.ones(h.shape) * leak).astype(np.float32)), h)
return r
with C.layers.default_options(init=C.normal(scale=0.02)):
h0 = C.layers.Convolution2D(dkernel, 1, strides=dstride)(x_img)
h0 = bn_with_leaky_relu(h0, leak=0.2)
print('h0 shape :', h0.shape)
h1 = C.layers.Convolution2D(dkernel, 64, strides=dstride)(h0)
h1 = bn_with_leaky_relu(h1, leak=0.2)
print('h1 shape :', h1.shape)
h2 = C.layers.Dense(256, activation=None)(h1)
h2 = bn_with_leaky_relu(h2, leak=0.2)
print('h2 shape :', h2.shape)
h2_aug = C.splice(h2, x_code)
h3 = C.layers.Dense(256, activation=C.relu)(h2_aug)
h4 = C.layers.Dense(1, activation=C.sigmoid, name='D_out')(h3)
print('h3 shape :', h4.shape)
return h4