a38a14f46715b7b0411d89c2e86854cf074c05a2,tslearn/shapelets.py,ShapeletModel,_set_model_layers,#ShapeletModel#Any#Any#Any#,310

Before Change

        shapelet_sizes = sorted(self.n_shapelets_per_size.keys())
        pool_layers = []
        for i, sz in enumerate(sorted(shapelet_sizes)):
            transformer_layer = Conv1D(filters=sz,
                                       kernel_size=sz,
                                       trainable=False,
                                       use_bias=False,
                                       name="false_conv_%d" % i)(inputs)
            shapelet_layer = LocalSquaredDistanceLayer(self.n_shapelets_per_size[sz],
                                                       name="shapelets_%d" % i)(transformer_layer)
            pool_layers.append(GlobalMinPooling1D(name="min_pooling_%d" % i)(shapelet_layer))
        if len(shapelet_sizes) > 1:

After Change

                self.model.get_layer("false_conv_%d_%d" % (i, di)).set_weights([numpy.eye(sz).reshape((sz, 1, sz))])

    def _set_model_layers(self, ts_sz, d, n_classes):
        inputs = [Input(shape=(ts_sz, 1), name="input_%d" % di) for di in range(d)]
        shapelet_sizes = sorted(self.n_shapelets_per_size.keys())
        pool_layers = []
        for i, sz in enumerate(sorted(shapelet_sizes)):
            transformer_layers = [Conv1D(filters=sz,
                                         kernel_size=sz,
                                         trainable=False,
                                         use_bias=False,
                                         name="false_conv_%d_%d" % (i, di))(inputs[di]) for di in range(d)]
            shapelet_layers = [LocalSquaredDistanceLayer(self.n_shapelets_per_size[sz],
                                                         name="shapelets_%d_%d" % (i, di))(transformer_layers[di])
                               for di in range(d)]
            if d == 1:
                summed_shapelet_layer = shapelet_layers[0]
            else:
                summed_shapelet_layer = add(shapelet_layers)
            pool_layers.append(GlobalMinPooling1D(name="min_pooling_%d" % i)(summed_shapelet_layer))
        if len(shapelet_sizes) > 1:
            concatenated_features = concatenate(pool_layers)
        else:

Instances