本文整理匯總了Python中cntk.sigmoid方法的典型用法代碼示例。如果您正苦於以下問題：Python cntk.sigmoid方法的具體用法？Python cntk.sigmoid怎麽用？Python cntk.sigmoid使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類cntk的用法示例。
在下文中一共展示了cntk.sigmoid方法的7個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: create_model
# 需要導入模塊: import cntk [as 別名]
# 或者: from cntk import sigmoid [as 別名]
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2: test_sigmoid
# 需要導入模塊: import cntk [as 別名]
# 或者: from cntk import sigmoid [as 別名]
def test_sigmoid():
assert_cntk_ngraph_isclose(C.sigmoid([-2, -1., 0., 1., 2.]))
assert_cntk_ngraph_isclose(C.sigmoid([0.]))
assert_cntk_ngraph_isclose(C.exp([-0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1, 0.])) 示例3: sigmoid
# 需要導入模塊: import cntk [as 別名]
# 或者: from cntk import sigmoid [as 別名]
def sigmoid(x):
return C.sigmoid(x) 示例4: binary_crossentropy
# 需要導入模塊: import cntk [as 別名]
# 或者: from cntk import sigmoid [as 別名]
def binary_crossentropy(target, output, from_logits=False):
if from_logits:
output = C.sigmoid(output)
output = C.clip(output, epsilon(), 1.0 - epsilon())
output = -target * C.log(output) - (1.0 - target) * C.log(1.0 - output)
return output 示例5: create_model
# 需要導入模塊: import cntk [as 別名]
# 或者: from cntk import sigmoid [as 別名]
def create_model(input, net_type="gru", encoder_type=1, model_file=None, e3cloning=False):
if encoder_type == 1:
h = audio_encoder(input)
if net_type.lower() is not "cnn":
h = flatten(h)
elif encoder_type == 2:
h = audio_encoder_2(input)
# pooling
h = C.layers.GlobalAveragePooling(name="avgpool")(h)
h = C.squeeze(h)
elif encoder_type == 3:
h = audio_encoder_3(input, model_file, e3cloning)
if net_type.lower() is not "cnn":
h = flatten(h)
else:
raise ValueError("encoder type {:d} not supported".format(encoder_type))
if net_type.lower() == "cnn":
h = C.layers.Dense(1024, init=C.he_normal(), activation=C.tanh)(h)
elif net_type.lower() == "gru":
h = C.layers.Recurrence(step_function=C.layers.GRU(256), go_backwards=False, name="rnn")(h)
elif net_type.lower() == "lstm":
h = C.layers.Recurrence(step_function=C.layers.LSTM(256), go_backwards=False, name="rnn")(h)
elif net_type.lower() == "bigru":
# bi-directional GRU
h = bi_recurrence(h, C.layers.GRU(128), C.layers.GRU(128), name="bigru")
elif net_type.lower() == "bilstm":
# bi-directional LSTM
h = bi_recurrence(h, C.layers.LSTM(128), C.layers.LSTM(128), name="bilstm")
h = C.layers.Dropout(0.2)(h)
# output
y = C.layers.Dense(label_dim, activation=C.sigmoid, init=C.he_normal(), name="output")(h)
return y
#--------------------------------------
# loss functions
#-------------------------------------- 示例6: D
# 需要導入模塊: import cntk [as 別名]
# 或者: from cntk import sigmoid [as 別名]
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 示例7: binary_crossentropy
# 需要導入模塊: import cntk [as 別名]
# 或者: from cntk import sigmoid [as 別名]
def binary_crossentropy(output, target, from_logits=False):
if from_logits:
output = C.sigmoid(output)
output = C.clip(output, _EPSILON, 1.0 - _EPSILON)
output = -target * C.log(output) - (1.0 - target) * C.log(1.0 - output)
return output