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Python backend.local_conv1d方法代碼示例

本文整理匯總了Python中keras.backend.local_conv1d方法的典型用法代碼示例。如果您正苦於以下問題:Python backend.local_conv1d方法的具體用法?Python backend.local_conv1d怎麽用?Python backend.local_conv1d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keras.backend的用法示例。


在下文中一共展示了backend.local_conv1d方法的5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: call

# 需要導入模塊: from keras import backend [as 別名]
# 或者: from keras.backend import local_conv1d [as 別名]
def call(self, u_vecs):
        if self.share_weights:
            u_hat_vecs = K.conv1d(u_vecs, self.W)
        else:
            u_hat_vecs = K.local_conv1d(u_vecs, self.W, [1], [1])

        batch_size = K.shape(u_vecs)[0]
        input_num_capsule = K.shape(u_vecs)[1]
        u_hat_vecs = K.reshape(u_hat_vecs, (batch_size, input_num_capsule,
                                            self.num_capsule, self.dim_capsule))
        u_hat_vecs = K.permute_dimensions(u_hat_vecs, (0, 2, 1, 3))

        b = K.zeros_like(u_hat_vecs[:, :, :, 0])  # shape = [None, num_capsule, input_num_capsule]
        for i in range(self.routings):
            b = K.permute_dimensions(b, (0, 2, 1))  # shape = [None, input_num_capsule, num_capsule]
            c = K.softmax(b)
            c = K.permute_dimensions(c, (0, 2, 1))
            b = K.permute_dimensions(b, (0, 2, 1))
            outputs = self.activation(K.batch_dot(c, u_hat_vecs, [2, 2]))
            if i < self.routings - 1:
                b = K.batch_dot(outputs, u_hat_vecs, [2, 3])

        return outputs 
開發者ID:WeavingWong,項目名稱:DigiX_HuaWei_Population_Age_Attribution_Predict,代碼行數:25,代碼來源:models.py

示例2: call

# 需要導入模塊: from keras import backend [as 別名]
# 或者: from keras.backend import local_conv1d [as 別名]
def call(self, u_vecs):
        if self.share_weights:
            u_hat_vecs = K.conv1d(u_vecs, self.W)
        else:
            u_hat_vecs = K.local_conv1d(u_vecs, self.W, [1], [1])

        batch_size = K.shape(u_vecs)[0]
        input_num_capsule = K.shape(u_vecs)[1]
        u_hat_vecs = K.reshape(u_hat_vecs, (batch_size, input_num_capsule,
                                            self.num_capsule, self.dim_capsule))
        u_hat_vecs = K.permute_dimensions(u_hat_vecs, (0, 2, 1, 3))
        # final u_hat_vecs.shape = [None, num_capsule, input_num_capsule, dim_capsule]

        b = K.zeros_like(u_hat_vecs[:, :, :, 0])  # shape = [None, num_capsule, input_num_capsule]
        outputs = None
        for i in range(self.routings):
            b = K.permute_dimensions(b, (0, 2, 1))  # shape = [None, input_num_capsule, num_capsule]
            c = K.softmax(b)
            c = K.permute_dimensions(c, (0, 2, 1))
            b = K.permute_dimensions(b, (0, 2, 1))
            outputs = self.activation(K.batch_dot(c, u_hat_vecs, [2, 2]))
            if i < self.routings - 1:
                b = K.batch_dot(outputs, u_hat_vecs, [2, 3])

        return outputs 
開發者ID:yongzhuo,項目名稱:Keras-TextClassification,代碼行數:27,代碼來源:capsule.py

示例3: call

# 需要導入模塊: from keras import backend [as 別名]
# 或者: from keras.backend import local_conv1d [as 別名]
def call(self, u_vecs):
        if self.share_weights:
            u_hat_vecs = K.conv1d(u_vecs, self.W)
        else:
            u_hat_vecs = K.local_conv1d(u_vecs, self.W, [1], [1])

        batch_size = K.shape(u_vecs)[0]
        input_num_capsule = K.shape(u_vecs)[1]
        u_hat_vecs = K.reshape(u_hat_vecs, (batch_size, input_num_capsule,
                                            self.num_capsule, self.dim_capsule))    # noqa
        u_hat_vecs = K.permute_dimensions(u_hat_vecs, (0, 2, 1, 3))
        # final u_hat_vecs.shape = [None, num_capsule, input_num_capsule, dim_capsule]  # noqa

        b = K.zeros_like(u_hat_vecs[:, :, :, 0])  # shape = [None, num_capsule, input_num_capsule]  # noqa
        for i in range(self.routings):
            b = K.permute_dimensions(b, (0, 2, 1))  # shape = [None, input_num_capsule, num_capsule]    # noqa
            c = K.softmax(b)
            c = K.permute_dimensions(c, (0, 2, 1))
            b = K.permute_dimensions(b, (0, 2, 1))
            outputs = self.activation(tf.keras.backend.batch_dot(c, u_hat_vecs, [2, 2]))    # noqa
            if i < self.routings - 1:
                b = tf.keras.backend.batch_dot(outputs, u_hat_vecs, [2, 3])
        return outputs 
開發者ID:KevinLiao159,項目名稱:Quora,代碼行數:25,代碼來源:neural_networks.py

示例4: call

# 需要導入模塊: from keras import backend [as 別名]
# 或者: from keras.backend import local_conv1d [as 別名]
def call(self, inputs):
        if self.share_weights:
            u_hat_vectors = K.conv1d(inputs, self.W)
        else:
            u_hat_vectors = K.local_conv1d(inputs, self.W, [1], [1])

        # u_hat_vectors : The spatially transformed input vectors (with local_conv_1d)

        batch_size = K.shape(inputs)[0]
        input_num_capsule = K.shape(inputs)[1]
        u_hat_vectors = K.reshape(u_hat_vectors, (batch_size,
                                                  input_num_capsule,
                                                  self.num_capsule,
                                                  self.dim_capsule))

        u_hat_vectors = K.permute_dimensions(u_hat_vectors, (0, 2, 1, 3))
        routing_weights = K.zeros_like(u_hat_vectors[:, :, :, 0])

        for i in range(self.routings):
            capsule_weights = K.softmax(routing_weights, 1)
            outputs = K.batch_dot(capsule_weights, u_hat_vectors, [2, 2])
            if K.ndim(outputs) == 4:
                outputs = K.sum(outputs, axis=1)
            if i < self.routings - 1:
                outputs = K.l2_normalize(outputs, -1)
                routing_weights = K.batch_dot(outputs, u_hat_vectors, [2, 3])
                if K.ndim(routing_weights) == 4:
                    routing_weights = K.sum(routing_weights, axis=1)

        return self.activation(outputs) 
開發者ID:keras-team,項目名稱:keras-contrib,代碼行數:32,代碼來源:capsule.py

示例5: call

# 需要導入模塊: from keras import backend [as 別名]
# 或者: from keras.backend import local_conv1d [as 別名]
def call(self, inputs):
        """Following the routing algorithm from Hinton's paper,
        but replace b = b + <u,v> with b = <u,v>.

        This change can improve the feature representation of Capsule.

        However, you can replace
            b = K.batch_dot(outputs, hat_inputs, [2, 3])
        with
            b += K.batch_dot(outputs, hat_inputs, [2, 3])
        to realize a standard routing.
        """

        if self.share_weights:
            hat_inputs = K.conv1d(inputs, self.kernel)
        else:
            hat_inputs = K.local_conv1d(inputs, self.kernel, [1], [1])

        batch_size = K.shape(inputs)[0]
        input_num_capsule = K.shape(inputs)[1]
        hat_inputs = K.reshape(hat_inputs,
                               (batch_size, input_num_capsule,
                                self.num_capsule, self.dim_capsule))
        hat_inputs = K.permute_dimensions(hat_inputs, (0, 2, 1, 3))

        b = K.zeros_like(hat_inputs[:, :, :, 0])
        for i in range(self.routings):
            c = softmax(b, 1)
            if K.backend() == 'theano':
                o = K.sum(o, axis=1)
            o = self.activation(K.batch_dot(c, hat_inputs, [2, 2]))
            if i < self.routings - 1:
                b = K.batch_dot(o, hat_inputs, [2, 3])
                if K.backend() == 'theano':
                    o = K.sum(o, axis=1)

        return o 
開發者ID:hello-sea,項目名稱:DeepLearning_Wavelet-LSTM,代碼行數:39,代碼來源:cifar10_cnn_capsule.py


注:本文中的keras.backend.local_conv1d方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台,相關代碼片段篩選自各路編程大神貢獻的開源項目,源碼版權歸原作者所有,傳播和使用請參考對應項目的License;未經允許,請勿轉載。