Python backend.sigmoid方法代码示例

# 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 

# 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 

# 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 

# 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 

# 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) 

# 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] 

# 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 

# 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) 

# 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 

# 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 

# 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 

# 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 

# 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 

# 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) 

# 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)