def call(self, inputs): x1 = inputs[0] x2 = inputs[1] if isinstance(self.axes, int): if self.axes < 0: axes = [self.axes % K.ndim(x1), self.axes % K.ndim(x2)] else: axes = [self.axes] * 2 else: axes = [] for i in range(len(self.axes)): if self.axes[i] < 0: axes.append(self.axes[i] % K.ndim(inputs[i])) else: axes.append(self.axes[i]) if self.normalize: x1 = K.l2_normalize(x1, axis=axes[0]) x2 = K.l2_normalize(x2, axis=axes[1]) output = K.batch_dot(x1, x2, axes) return output
def _merge_function(self, inputs): if len(inputs) != 2: raise ValueError('A `Dot` layer should be called ' 'on exactly 2 inputs') x1 = inputs[0] x2 = inputs[1] if isinstance(self.axes, int): if self.axes < 0: axes = [self.axes % K.ndim(x1), self.axes % K.ndim(x2)] else: axes = [self.axes] * 2 else: axes = [] for i in range(len(self.axes)): if self.axes[i] < 0: axes.append(self.axes[i] % K.ndim(inputs[i])) else: axes.append(self.axes[i]) if self.normalize: x1 = K.l2_normalize(x1, axis=axes[0]) x2 = K.l2_normalize(x2, axis=axes[1]) output = K.batch_dot(x1, x2, axes) return output
def cosine_proximity(y_true, y_pred): y_true = K.l2_normalize(y_true, axis=-1) y_pred = K.l2_normalize(y_pred, axis=-1) return -K.sum(y_true * y_pred, axis=-1)