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Python backend.transpose方法代码示例

本文整理汇总了Python中tensorflow.keras.backend.transpose方法的典型用法代码示例。如果您正苦于以下问题:Python backend.transpose方法的具体用法?Python backend.transpose怎么用?Python backend.transpose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tensorflow.keras.backend的用法示例。


在下文中一共展示了backend.transpose方法的6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: call

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import transpose [as 别名]
def call(self, inputs):
        if self.trainable_kernel:
            output = K.dot(K.dot(inputs, self.kernel), K.transpose(inputs))
        else:
            output = K.dot(inputs, K.transpose(inputs))
        if self.activation is not None:
            output = self.activation(output)
        return output 
开发者ID:danielegrattarola,项目名称:spektral,代码行数:10,代码来源:base.py

示例2: build

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import transpose [as 别名]
def build(self, input_shape):



        # Create a trainable weight variable for this layer.
        self.kernel = self.add_weight(name='mult-kernel',
                                    shape=(np.prod(self.orig_input_shape),
                                           self.output_len),
                                    initializer=self.kernel_initializer,
                                    trainable=True)

        M = K.reshape(self.kernel, [-1, self.output_len])  # D x d
        mt = K.transpose(M) # d x D
        mtm_inv = tf.matrix_inverse(K.dot(mt, M))  # d x d
        self.W = K.dot(mtm_inv, mt) # d x D

        if self.use_bias:
            self.bias = self.add_weight(name='bias-kernel',
                                        shape=(self.output_len, ),
                                        initializer=self.bias_initializer,
                                        trainable=True)

        # self.sigma_sq = self.add_weight(name='bias-kernel',
        #                                 shape=(1, ),
        #                                 initializer=self.initializer,
        #                                 trainable=True)

        super(SpatiallySparse_Dense, self).build(input_shape)  # Be sure to call this somewhere! 
开发者ID:adalca,项目名称:neuron,代码行数:30,代码来源:layers.py

示例3: call

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import transpose [as 别名]
def call(self, inputs, **kwargs):
        """ student t-distribution, as same as used in t-SNE algorithm.
                 q_ij = 1/(1+dist(x_i, u_j)^2), then normalize it.
        Arguments:
            inputs: the variable containing data, shape=(n_samples, n_features)
        Return:
            q: student's t-distribution, or soft labels for each sample. shape=(n_samples, n_clusters)
        """
        q = 1.0 / (1.0 + (K.sum(K.square(K.expand_dims(inputs, axis=1) - self.clusters), axis=2) / self.alpha))
        q **= (self.alpha + 1.0) / 2.0
        q = K.transpose(K.transpose(q) / K.sum(q, axis=1))
        return q 
开发者ID:XifengGuo,项目名称:DEC-DA,代码行数:14,代码来源:FcDEC.py

示例4: mi_loss

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import transpose [as 别名]
def mi_loss(self, y_true, y_pred):
        """Mutual information loss computed from the joint
           distribution matrix and the marginals

        Arguments:
            y_true (tensor): Not used since this is
                unsupervised learning
            y_pred (tensor): stack of softmax predictions for
                the Siamese latent vectors (Z and Zbar)
        """
        size = self.args.batch_size
        n_labels = y_pred.shape[-1]
        # lower half is Z
        Z = y_pred[0: size, :]
        Z = K.expand_dims(Z, axis=2)
        # upper half is Zbar
        Zbar = y_pred[size: y_pred.shape[0], :]
        Zbar = K.expand_dims(Zbar, axis=1)
        # compute joint distribution (Eq 10.3.2 & .3)
        P = K.batch_dot(Z, Zbar)
        P = K.sum(P, axis=0)
        # enforce symmetric joint distribution (Eq 10.3.4)
        P = (P + K.transpose(P)) / 2.0
        # normalization of total probability to 1.0
        P = P / K.sum(P)
        # marginal distributions (Eq 10.3.5 & .6)
        Pi = K.expand_dims(K.sum(P, axis=1), axis=1)
        Pj = K.expand_dims(K.sum(P, axis=0), axis=0)
        Pi = K.repeat_elements(Pi, rep=n_labels, axis=1)
        Pj = K.repeat_elements(Pj, rep=n_labels, axis=0)
        P = K.clip(P, K.epsilon(), np.finfo(float).max)
        Pi = K.clip(Pi, K.epsilon(), np.finfo(float).max)
        Pj = K.clip(Pj, K.epsilon(), np.finfo(float).max)
        # negative MI loss (Eq 10.3.7)
        neg_mi = K.sum((P * (K.log(Pi) + K.log(Pj) - K.log(P))))
        # each head contribute 1/n_heads to the total loss
        return neg_mi/self.args.heads 
开发者ID:PacktPublishing,项目名称:Advanced-Deep-Learning-with-Keras,代码行数:39,代码来源:iic-13.5.1.py

示例5: call

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import transpose [as 别名]
def call(self, x, **kwargs):
        # (x - y)^2 = x^2 + y^2 - 2 * x * y
        x_sq = K.expand_dims(K.sum(x ** 2, axis=2), axis=-1)
        y_sq = K.reshape(K.sum(self.kernel ** 2, axis=1),
                         (1, 1, self.n_shapelets))
        xy = K.dot(x, K.transpose(self.kernel))
        return (x_sq + y_sq - 2 * xy) / K.int_shape(self.kernel)[1] 
开发者ID:tslearn-team,项目名称:tslearn,代码行数:9,代码来源:shapelets.py

示例6: _pairwise_distances

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import transpose [as 别名]
def _pairwise_distances(self, inputs: List[Tensor]) -> Tensor:
        emb_c, emb_r = inputs
        bs = K.shape(emb_c)[0]
        embeddings = K.concatenate([emb_c, emb_r], 0)
        dot_product = K.dot(embeddings, K.transpose(embeddings))
        square_norm = K.batch_dot(embeddings, embeddings, axes=1)
        distances = K.transpose(square_norm) - 2.0 * dot_product + square_norm
        distances = distances[0:bs, bs:bs+bs]
        distances = K.clip(distances, 0.0, None)
        mask = K.cast(K.equal(distances, 0.0), K.dtype(distances))
        distances = distances + mask * 1e-16
        distances = K.sqrt(distances)
        distances = distances * (1.0 - mask)
        return distances 
开发者ID:deepmipt,项目名称:DeepPavlov,代码行数:16,代码来源:bilstm_siamese_network.py


注:本文中的tensorflow.keras.backend.transpose方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。