def _construct_layers(self): self.input_layer = Input(shape=(self.n_objects_fit_, self.n_object_features_fit_)) # Todo: Variable sized input # X = Input(shape=(None, n_features)) logger.info("n_hidden {}, n_units {}".format(self.n_hidden, self.n_units)) hidden_dense_kwargs = { "kernel_regularizer": self.kernel_regularizer_, "kernel_initializer": self.kernel_initializer, "activation": self.activation, } hidden_dense_kwargs.update( self._get_prefix_attributes("hidden_dense_layer__")) if self.batch_normalization: if self.add_zeroth_order_model: self.hidden_layers_zeroth = [ NormalizedDense( self.n_units, name="hidden_zeroth_{}".format(x), **hidden_dense_kwargs, ) for x in range(self.n_hidden) ] self.hidden_layers = [ NormalizedDense(self.n_units, name="hidden_{}".format(x), **hidden_dense_kwargs) for x in range(self.n_hidden) ] else: if self.add_zeroth_order_model: self.hidden_layers_zeroth = [ Dense( self.n_units, name="hidden_zeroth_{}".format(x), **hidden_dense_kwargs, ) for x in range(self.n_hidden) ] self.hidden_layers = [ Dense(self.n_units, name="hidden_{}".format(x), **hidden_dense_kwargs) for x in range(self.n_hidden) ] assert len(self.hidden_layers) == self.n_hidden self.output_node = Dense(1, activation="sigmoid", kernel_regularizer=self.kernel_regularizer_) if self.add_zeroth_order_model: self.output_node_zeroth = Dense( 1, activation="sigmoid", kernel_regularizer=self.kernel_regularizer_)
def _construct_layers(self, **kwargs): self.input_layer = Input(shape=(self.n_objects, self.n_object_features)) # Todo: Variable sized input # X = Input(shape=(None, n_features)) if self.batch_normalization: if self._use_zeroth_model: self.hidden_layers_zeroth = [ NormalizedDense(self.n_units, name="hidden_zeroth_{}".format(x), *kwargs) for x in range(self.n_hidden) ] self.hidden_layers = [ NormalizedDense(self.n_units, name="hidden_{}".format(x), **kwargs) for x in range(self.n_hidden) ] else: if self._use_zeroth_model: self.hidden_layers_zeroth = [ Dense(self.n_units, name="hidden_zeroth_{}".format(x), **kwargs) for x in range(self.n_hidden) ] self.hidden_layers = [ Dense(self.n_units, name="hidden_{}".format(x), **kwargs) for x in range(self.n_hidden) ] assert len(self.hidden_layers) == self.n_hidden self.output_node = Dense( 1, activation="linear", kernel_regularizer=self.kernel_regularizer, name="score", ) if self._use_zeroth_model: self.output_node_zeroth = Dense( 1, activation="linear", kernel_regularizer=self.kernel_regularizer, name="zero_score", ) self.weighted_sum = Dense( 1, activation="sigmoid", kernel_regularizer=self.kernel_regularizer, name="weighted_sum", )
def _construct_layers(self): logger.info("n_hidden {}, n_units {}".format(self.n_hidden, self.n_units)) self.x1 = Input(shape=(self.n_object_features_fit_, )) self.x2 = Input(shape=(self.n_object_features_fit_, )) self.output_node = Dense(1, activation="sigmoid", kernel_regularizer=self.kernel_regularizer_) self.output_layer_score = Dense(1, activation="linear") hidden_dense_kwargs = { "kernel_regularizer": self.kernel_regularizer_, "kernel_initializer": self.kernel_initializer, "activation": self.activation, } hidden_dense_kwargs.update( self._get_prefix_attributes("hidden_dense_layer__")) if self.batch_normalization: self.hidden_layers = [ NormalizedDense(self.n_units, name="hidden_{}".format(x), **hidden_dense_kwargs) for x in range(self.n_hidden) ] else: self.hidden_layers = [ Dense(self.n_units, name="hidden_{}".format(x), **hidden_dense_kwargs) for x in range(self.n_hidden) ] assert len(self.hidden_layers) == self.n_hidden
def _construct_layers(self, n_hidden=2, n_units=16, **kwargs): self.input_layer = Input(shape=(self.n_objects, self.n_features)) # Todo: Variable sized input # X = Input(shape=(None, n_features)) if self.batch_normalization: if self._use_zeroth_model: self.hidden_layers_zeroth = [ NormalizedDense(n_units, name="hidden_zeroth_{}".format(x), kernel_regularizer=self.kernel_regularizer, activation=self.non_linearities) for x in range(n_hidden) ] self.hidden_layers = [ NormalizedDense(n_units, name="hidden_{}".format(x), kernel_regularizer=self.kernel_regularizer, activation=self.non_linearities) for x in range(n_hidden) ] else: if self._use_zeroth_model: self.hidden_layers_zeroth = [ Dense(n_units, name="hidden_zeroth_{}".format(x), kernel_regularizer=self.kernel_regularizer, activation=self.non_linearities) for x in range(n_hidden) ] self.hidden_layers = [ Dense(n_units, name="hidden_{}".format(x), kernel_regularizer=self.kernel_regularizer, activation=self.non_linearities) for x in range(n_hidden) ] assert len(self.hidden_layers) == n_hidden self.output_node = Dense(1, activation='sigmoid', kernel_regularizer=self.kernel_regularizer) if self._use_zeroth_model: self.output_node_zeroth = Dense( 1, activation='sigmoid', kernel_regularizer=self.kernel_regularizer)
def _construct_layers(self, **kwargs): self.output_node = Dense(1, activation='sigmoid', kernel_regularizer=self.kernel_regularizer) self.x1 = Input(shape=(self.n_object_features,)) self.x2 = Input(shape=(self.n_object_features,)) if self.batch_normalization: self.hidden_layers = [NormalizedDense(self.n_units, name="hidden_{}".format(x), **kwargs) for x in range(self.n_hidden)] else: self.hidden_layers = [Dense(self.n_units, name="hidden_{}".format(x), **kwargs) for x in range(self.n_hidden)] assert len(self.hidden_layers) == self.n_hidden
def _construct_layers(self, **kwargs): self.input_layer = Input(shape=(self.n_top, self.n_object_features)) self.output_node = Dense(1, activation="linear", kernel_regularizer=self.kernel_regularizer) if self.batch_normalization: self.hidden_layers = [ NormalizedDense(self.n_units, name="hidden_{}".format(x), kernel_regularizer=self.kernel_regularizer, kernel_initializer=self.kernel_initializer, activation=self.non_linearities, **kwargs) for x in range(self.n_hidden) ] else: self.hidden_layers = [ Dense(self.n_units, name="hidden_{}".format(x), kernel_regularizer=self.kernel_regularizer, kernel_initializer=self.kernel_initializer, activation=self.non_linearities, **kwargs) for x in range(self.n_hidden) ] assert len(self.hidden_layers) == self.n_hidden