本文整理汇总了Python中keras.backend.sigmoid方法的典型用法代码示例。如果您正苦于以下问题:Python backend.sigmoid方法的具体用法?Python backend.sigmoid怎么用?Python backend.sigmoid使用的例子?那么, 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keras.backend
的用法示例。
在下文中一共展示了backend.sigmoid方法的15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: weather_l2
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import sigmoid [as 别名]
def weather_l2(hidden_nums=100,l2=0.01):
input_img = Input(shape=(37,))
hn = Dense(hidden_nums, activation='relu')(input_img)
hn = Dense(hidden_nums, activation='relu',
kernel_regularizer=regularizers.l2(l2))(hn)
out_u = Dense(37, activation='sigmoid',
name='ae_part')(hn)
out_sig = Dense(37, activation='linear',
name='pred_part')(hn)
out_both = concatenate([out_u, out_sig], axis=1, name = 'concatenate')
#weather_model = Model(input_img, outputs=[out_ae, out_pred])
mve_model = Model(input_img, outputs=[out_both])
mve_model.compile(optimizer='adam', loss=mve_loss, loss_weights=[1.])
return mve_model
示例2: CausalCNN
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import sigmoid [as 别名]
def CausalCNN(n_filters, lr, decay, loss,
seq_len, input_features,
strides_len, kernel_size,
dilation_rates):
inputs = Input(shape=(seq_len, input_features), name='input_layer')
x=inputs
for dilation_rate in dilation_rates:
x = Conv1D(filters=n_filters,
kernel_size=kernel_size,
padding='causal',
dilation_rate=dilation_rate,
activation='linear')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
#x = Dense(7, activation='relu', name='dense_layer')(x)
outputs = Dense(3, activation='sigmoid', name='output_layer')(x)
causalcnn = Model(inputs, outputs=[outputs])
return causalcnn
示例3: weather_ae
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import sigmoid [as 别名]
def weather_ae(layers, lr, decay, loss,
input_len, input_features):
inputs = Input(shape=(input_len, input_features), name='input_layer')
for i, hidden_nums in enumerate(layers):
if i==0:
hn = Dense(hidden_nums, activation='relu')(inputs)
else:
hn = Dense(hidden_nums, activation='relu')(hn)
outputs = Dense(3, activation='sigmoid', name='output_layer')(hn)
weather_model = Model(inputs, outputs=[outputs])
return weather_model
示例4: get_model
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import sigmoid [as 别名]
def get_model(num_users, num_items, latent_dim, regs=[0,0]):
# Input variables
user_input = Input(shape=(1,), dtype='int32', name = 'user_input')
item_input = Input(shape=(1,), dtype='int32', name = 'item_input')
MF_Embedding_User = Embedding(input_dim = num_users, output_dim = latent_dim, name = 'user_embedding',
init = init_normal, W_regularizer = l2(regs[0]), input_length=1)
MF_Embedding_Item = Embedding(input_dim = num_items, output_dim = latent_dim, name = 'item_embedding',
init = init_normal, W_regularizer = l2(regs[1]), input_length=1)
# Crucial to flatten an embedding vector!
user_latent = Flatten()(MF_Embedding_User(user_input))
item_latent = Flatten()(MF_Embedding_Item(item_input))
# Element-wise product of user and item embeddings
predict_vector = merge([user_latent, item_latent], mode = 'mul')
# Final prediction layer
#prediction = Lambda(lambda x: K.sigmoid(K.sum(x)), output_shape=(1,))(predict_vector)
prediction = Dense(1, activation='sigmoid', init='lecun_uniform', name = 'prediction')(predict_vector)
model = Model(input=[user_input, item_input],
output=prediction)
return model
示例5: __init__
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import sigmoid [as 别名]
def __init__(self, halt_epsilon=0.01, time_penalty=0.01, **kwargs):
"""
:param halt_epsilon: a small constant that allows computation to halt
after a single update (sigmoid never reaches exactly 1.0)
:param time_penalty: parameter that weights the relative cost
of computation versus error. The larger it is, the less
computational steps the network will try to make and vice versa.
The default value of 0.01 works well for Transformer.
:param kwargs: Any standard parameters for a layer in Keras (like name)
"""
self.halt_epsilon = halt_epsilon
self.time_penalty = time_penalty
self.ponder_cost = None
self.weighted_output = None
self.zeros_like_input = None
self.zeros_like_halting = None
self.ones_like_halting = None
self.halt_budget = None
self.remainder = None
self.active_steps = None
super().__init__(**kwargs)
示例6: step
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import sigmoid [as 别名]
def step(self, inputs, states):
h_tm1 = states[0] # previous memory
#B_U = states[1] # dropout matrices for recurrent units
#B_W = states[2]
h_tm1a = K.dot(h_tm1, self.Wa)
eij = K.dot(K.tanh(h_tm1a + K.dot(inputs[:, :self.h_dim], self.Ua)), self.Va)
eijs = K.repeat_elements(eij, self.h_dim, axis=1)
#alphaij = K.softmax(eijs) # batchsize * lenh h batchsize * lenh * ndim
#ci = K.permute_dimensions(K.permute_dimensions(self.h, [2,0,1]) * alphaij, [1,2,0])
#cisum = K.sum(ci, axis=1)
cisum = eijs*inputs[:, :self.h_dim]
#print(K.shape(cisum), cisum.shape, ci.shape, self.h.shape, alphaij.shape, x.shape)
zr = K.sigmoid(K.dot(inputs[:, self.h_dim:], self.Wzr) + K.dot(h_tm1, self.Uzr) + K.dot(cisum, self.Czr))
zi = zr[:, :self.units]
ri = zr[:, self.units: 2 * self.units]
si_ = K.tanh(K.dot(inputs[:, self.h_dim:], self.W) + K.dot(ri*h_tm1, self.U) + K.dot(cisum, self.C))
si = (1-zi) * h_tm1 + zi * si_
return si, [si] #h_tm1, [h_tm1]
示例7: call
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import sigmoid [as 别名]
def call(self, inputs):
if self.data_format == 'channels_first':
sq = K.mean(inputs, [2, 3])
else:
sq = K.mean(inputs, [1, 2])
ex = K.dot(sq, self.kernel1)
if self.use_bias:
ex = K.bias_add(ex, self.bias1)
ex= K.relu(ex)
ex = K.dot(ex, self.kernel2)
if self.use_bias:
ex = K.bias_add(ex, self.bias2)
ex= K.sigmoid(ex)
if self.data_format == 'channels_first':
ex = K.expand_dims(ex, -1)
ex = K.expand_dims(ex, -1)
else:
ex = K.expand_dims(ex, 1)
ex = K.expand_dims(ex, 1)
return inputs * ex
示例8: call
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import sigmoid [as 别名]
def call(self, x):
# input shape: (nb_samples, time (padded with zeros), input_dim)
# note that the .build() method of subclasses MUST define
# self.input_spec with a complete input shape.
input_shape = self.input_spec[0].shape
if self.window_size > 1:
x = K.temporal_padding(x, (self.window_size-1, 0))
x = K.expand_dims(x, 2) # add a dummy dimension
# z, g
output = K.conv2d(x, self.kernel, strides=self.strides,
padding='valid',
data_format='channels_last')
output = K.squeeze(output, 2) # remove the dummy dimension
if self.use_bias:
output = K.bias_add(output, self.bias, data_format='channels_last')
z = output[:, :, :self.output_dim]
g = output[:, :, self.output_dim:]
return self.activation(z) * K.sigmoid(g)
示例9: step
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import sigmoid [as 别名]
def step(self, inputs, states):
vP_t = inputs
hP_tm1 = states[0]
_ = states[1:3] # ignore internal dropout/masks
vP, WP_v, WPP_v, v, W_g2 = states[3:8]
vP_mask, = states[8:]
WP_v_Dot = K.dot(vP, WP_v)
WPP_v_Dot = K.dot(K.expand_dims(vP_t, axis=1), WPP_v)
s_t_hat = K.tanh(WPP_v_Dot + WP_v_Dot)
s_t = K.dot(s_t_hat, v)
s_t = K.batch_flatten(s_t)
a_t = softmax(s_t, mask=vP_mask, axis=1)
c_t = K.batch_dot(a_t, vP, axes=[1, 1])
GRU_inputs = K.concatenate([vP_t, c_t])
g = K.sigmoid(K.dot(GRU_inputs, W_g2))
GRU_inputs = g * GRU_inputs
hP_t, s = super(SelfAttnGRU, self).step(GRU_inputs, states)
return hP_t, s
示例10: step
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import sigmoid [as 别名]
def step(self, inputs, states):
uP_t = inputs
vP_tm1 = states[0]
_ = states[1:3] # ignore internal dropout/masks
uQ, WQ_u, WP_v, WP_u, v, W_g1 = states[3:9]
uQ_mask, = states[9:10]
WQ_u_Dot = K.dot(uQ, WQ_u) #WQ_u
WP_v_Dot = K.dot(K.expand_dims(vP_tm1, axis=1), WP_v) #WP_v
WP_u_Dot = K.dot(K.expand_dims(uP_t, axis=1), WP_u) # WP_u
s_t_hat = K.tanh(WQ_u_Dot + WP_v_Dot + WP_u_Dot)
s_t = K.dot(s_t_hat, v) # v
s_t = K.batch_flatten(s_t)
a_t = softmax(s_t, mask=uQ_mask, axis=1)
c_t = K.batch_dot(a_t, uQ, axes=[1, 1])
GRU_inputs = K.concatenate([uP_t, c_t])
g = K.sigmoid(K.dot(GRU_inputs, W_g1)) # W_g1
GRU_inputs = g * GRU_inputs
vP_t, s = super(QuestionAttnGRU, self).step(GRU_inputs, states)
return vP_t, s
示例11: weather_mve
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import sigmoid [as 别名]
def weather_mve(hidden_nums=100):
input_img = Input(shape=(37,))
hn = Dense(hidden_nums, activation='relu')(input_img)
hn = Dense(hidden_nums, activation='relu')(hn)
out_u = Dense(37, activation='sigmoid', name='ae_part')(hn)
out_sig = Dense(37, activation='linear', name='pred_part')(hn)
out_both = concatenate([out_u, out_sig], axis=1, name = 'concatenate')
#weather_model = Model(input_img, outputs=[out_ae, out_pred])
mve_model = Model(input_img, outputs=[out_both])
mve_model.compile(optimizer='adam', loss=mve_loss, loss_weights=[1.])
return mve_model
示例12: weather_mse
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import sigmoid [as 别名]
def weather_mse():
input_img = Input(shape=(37,))
hn = Dense(100, activation='relu')(input_img)
hn = Dense(100, activation='relu')(hn)
out_pred = Dense(37, activation='sigmoid', name='pred_part')(hn)
weather_model = Model(input_img, outputs=[out_pred])
weather_model.compile(optimizer='adam', loss='mse',loss_weights=[1.])
return weather_model
示例13: weather_fusion
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import sigmoid [as 别名]
def weather_fusion():
input_img = Input(shape=(37,))
hn = Dense(100, activation='relu')(input_img)
hn = Dense(100, activation='relu')(hn)
#out_ae = Dense(37, activation='sigmoid', name='ae_part')(hn)
out_pred = Dense(37, activation='sigmoid', name='pred_part')(hn)
weather_model = Model(input_img, outputs=[out_ae, out_pred])
weather_model.compile(optimizer='adam', loss='mse',loss_weights=[1.5, 1.])
return weather_model
示例14: swish
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import sigmoid [as 别名]
def swish(x):
return (K.sigmoid(x) * x)
示例15: pair_loss
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import sigmoid [as 别名]
def pair_loss(y_true, y_pred):
y_true = tf.cast(y_true, tf.int32)
parts = tf.dynamic_partition(y_pred, y_true, 2)
y_pos = parts[1]
y_neg = parts[0]
y_pos = tf.expand_dims(y_pos, 0)
y_neg = tf.expand_dims(y_neg, -1)
out = K.sigmoid(y_neg - y_pos)
return K.mean(out)