def get_cross_logits( features, feature_columns, shared_feature_vectors, units, is_training, extra_options): with tf.variable_scope('cross'): _check_cross_args(extra_options) use_shared_embedding = extra_options['cross_use_shared_embedding'] use_project = extra_options['cross_use_project'] project_size = extra_options['cross_project_size'] num_layers = extra_options['cross_num_layers'] if not use_shared_embedding: feature_vectors = get_feature_vectors(features, feature_columns) else: feature_vectors = shared_feature_vectors if use_project: feature_vectors = project(feature_vectors, project_size) x = tf.concat(feature_vectors, axis=1) # [B, T] y = _cross_net(x, num_layers) with tf.variable_scope('logits') as logits_scope: logits = fc(y, units=units, name=logits_scope) add_hidden_layer_summary(logits, logits_scope.name) return logits
def get_ifm_logits(features, feature_columns, shared_feature_vectors, units, is_training, extra_options): with tf.variable_scope('ifm'): _check_ifm_args(extra_options) use_shared_embedding = extra_options['ifm_use_shared_embedding'] use_project = extra_options['ifm_use_project'] project_size = extra_options['ifm_project_size'] hidden_unit = extra_options['ifm_hidden_unit'] field_dim = extra_options['ifm_field_dim'] if not use_shared_embedding: feature_vectors = get_feature_vectors(features, feature_columns) else: feature_vectors = shared_feature_vectors if use_project: feature_vectors = project(feature_vectors, project_size) y = _ifm(feature_vectors, hidden_unit, field_dim, reduce_sum=True) with tf.variable_scope('logits') as logits_scope: logits = y add_hidden_layer_summary(logits, logits_scope.name) return logits
def get_cin_logits(features, feature_columns, shared_feature_vectors, units, is_training, extra_options): with tf.variable_scope('cin'): _check_cin_args(extra_options) use_shared_embedding = extra_options['cin_use_shared_embedding'] use_project = extra_options['cin_use_project'] project_size = extra_options['cin_project_size'] hidden_feature_maps = extra_options['cin_hidden_feature_maps'] split_half = extra_options['cin_split_half'] if not use_shared_embedding: feature_vectors = get_feature_vectors(features, feature_columns) else: feature_vectors = shared_feature_vectors if use_project: feature_vectors = project(feature_vectors, project_size) check_feature_dims(feature_vectors) x = tf.stack(feature_vectors, axis=1) # [B, N, D] y = _cin_layer(x, hidden_feature_maps, split_half, reduce_sum=False) # [B, F] with tf.variable_scope('logits') as logits_scope: logits = fc(y, units=units, name=logits_scope) add_hidden_layer_summary(logits, logits_scope.name) return logits
def get_fm_logits(features, feature_columns, shared_feature_vectors, units, is_training, extra_options): assert units == 1, "FM units must be 1" with tf.variable_scope('fm'): _check_fm_args(extra_options) use_shared_embedding = extra_options['fm_use_shared_embedding'] use_project = extra_options['fm_use_project'] project_size = extra_options['fm_project_size'] if not use_shared_embedding: feature_vectors = get_feature_vectors(features, feature_columns) else: feature_vectors = shared_feature_vectors if use_project: feature_vectors = project(feature_vectors, project_size) y = _fm(feature_vectors, reduce_sum=True) # [B, 1] with tf.variable_scope('logits') as logits_scope: logits = y add_hidden_layer_summary(logits, logits_scope.name) return logits
def get_fgcnn_feature_vectors(features, feature_columns, feature_vectors, options, name='fgcnn'): """ """ with tf.variable_scope(name): _check_fgcnn_args(options) use_shared_embedding = options['fgcnn_use_shared_embedding'] use_project = options['fgcnn_use_project'] project_dim = options['fgcnn_project_dim'] filter_nums = options['fgcnn_filter_nums'] kernel_sizes = options['fgcnn_kernel_sizes'] pooling_sizes = options['fgcnn_pooling_sizes'] new_map_sizes = options['fgcnn_new_map_sizes'] x = feature_vectors if not use_shared_embedding: x = get_feature_vectors(features, feature_columns, name + '_feature_vectors') if use_project: x = project(feature_vectors, project_dim) new_feature_vectors = _fgcnn(x, filter_nums=filter_nums, kernel_sizes=kernel_sizes, pooling_sizes=pooling_sizes, new_map_sizes=new_map_sizes) return new_feature_vectors
def get_wkfm_logits( features, feature_columns, shared_feature_vectors, units, is_training, extra_options): with tf.variable_scope('wkfm'): _check_wkfm_args(extra_options) use_shared_embedding = extra_options['wkfm_use_shared_embedding'] use_project = extra_options['wkfm_use_project'] project_size = extra_options['wkfm_project_size'] if not use_shared_embedding: feature_vectors = get_feature_vectors(features, feature_columns) else: feature_vectors = shared_feature_vectors if use_project: feature_vectors = project(feature_vectors, project_size) y = _wkfm(feature_vectors, reduce_sum=True) # [B, 1] with tf.variable_scope('logits') as logits_scope: # fc just for adding a bias logits = fc(y, units=units, name=logits_scope) add_hidden_layer_summary(logits, logits_scope.name) return logits
def get_ipnn_logits( features, feature_columns, shared_feature_vectors, units, is_training, extra_options): with tf.variable_scope('ipnn'): _check_ipnn_args(extra_options) use_shared_embedding = extra_options['ipnn_use_shared_embedding'] use_project = extra_options['ipnn_use_project'] project_size = extra_options['ipnn_project_size'] hidden_units = extra_options['ipnn_hidden_units'] activation_fn = extra_options['ipnn_activation_fn'] dropout = extra_options['ipnn_dropout'] batch_norm = extra_options['ipnn_batch_norm'] layer_norm = extra_options['ipnn_layer_norm'] use_resnet = extra_options['ipnn_use_resnet'] use_densenet = extra_options['ipnn_use_densenet'] unordered_inner_product = extra_options['ipnn_unordered_inner_product'] concat_project = extra_options['ipnn_concat_project'] leaky_relu_alpha = extra_options['leaky_relu_alpha'] swish_beta = extra_options['swish_beta'] activation_fn = get_activation_fn(activation_fn=activation_fn, leaky_relu_alpha=leaky_relu_alpha, swish_beta=swish_beta) if not use_shared_embedding: feature_vectors = get_feature_vectors(features, feature_columns) else: feature_vectors = shared_feature_vectors project_feature_vectors = None if use_project: project_feature_vectors = project(feature_vectors, project_size) y = _ipnn(feature_vectors=feature_vectors, project_feature_vectors=project_feature_vectors, use_project=use_project, units=units, hidden_units=hidden_units, activation_fn=activation_fn, dropout=dropout, batch_norm=batch_norm, layer_norm=layer_norm, use_resnet=use_resnet, use_densenet=use_densenet, is_training=is_training, unordered_inner_product=unordered_inner_product, concat_project=concat_project) with tf.variable_scope('logits') as logits_scope: logits = fc(y, units=units, name=logits_scope) add_hidden_layer_summary(logits, logits_scope.name) return logits
def get_ccpm_logits(features, feature_columns, shared_feature_vectors, units, is_training, extra_options): with tf.variable_scope('ccpm'): _check_ccpm_args(extra_options) use_shared_embedding = extra_options['ccpm_use_shared_embedding'] use_project = extra_options['ccpm_use_project'] project_size = extra_options['ccpm_project_size'] hidden_units = extra_options['ccpm_hidden_units'] activation_fn = extra_options['ccpm_activation_fn'] dropout = extra_options['ccpm_dropout'] batch_norm = extra_options['ccpm_batch_norm'] layer_norm = extra_options['ccpm_layer_norm'] use_resnet = extra_options['ccpm_use_resnet'] use_densenet = extra_options['ccpm_use_densenet'] kernel_sizes = extra_options['ccpm_kernel_sizes'] filter_nums = extra_options['ccpm_filter_nums'] leaky_relu_alpha = extra_options['leaky_relu_alpha'] swish_beta = extra_options['swish_beta'] activation_fn = get_activation_fn(activation_fn=activation_fn, leaky_relu_alpha=leaky_relu_alpha, swish_beta=swish_beta) if not use_shared_embedding: feature_vectors = get_feature_vectors(features, feature_columns) else: feature_vectors = shared_feature_vectors if use_project: feature_vectors = project(feature_vectors, project_size) y = _build_ccpm_model(feature_vectors=feature_vectors, kernel_sizes=kernel_sizes, filter_nums=filter_nums, hidden_units=hidden_units, activation_fn=activation_fn, dropout=dropout, is_training=is_training, batch_norm=batch_norm, layer_norm=layer_norm, use_resnet=use_resnet, use_densenet=use_densenet) with tf.variable_scope('logits') as logits_scope: logits = fc(y, units=units, name=logits_scope) add_hidden_layer_summary(logits, logits_scope.name) return logits
def get_nfm_logits(features, feature_columns, shared_feature_vectors, units, is_training, extra_options): with tf.variable_scope('nfm'): _check_nfm_args(extra_options) use_shared_embedding = extra_options['nfm_use_shared_embedding'] use_project = extra_options['nfm_use_project'] project_size = extra_options['nfm_project_size'] hidden_units = extra_options['nfm_hidden_units'] activation_fn = extra_options['nfm_activation_fn'] dropout = extra_options['nfm_dropout'] batch_norm = extra_options['nfm_batch_norm'] layer_norm = extra_options['nfm_layer_norm'] use_resnet = extra_options['nfm_use_resnet'] use_densenet = extra_options['nfm_use_densenet'] leaky_relu_alpha = extra_options['leaky_relu_alpha'] swish_beta = extra_options['swish_beta'] activation_fn = get_activation_fn(activation_fn=activation_fn, leaky_relu_alpha=leaky_relu_alpha, swish_beta=swish_beta) if not use_shared_embedding: feature_vectors = get_feature_vectors(features, feature_columns) else: feature_vectors = shared_feature_vectors if use_project: feature_vectors = project(feature_vectors, project_size) # Neural FM y = _fm(feature_vectors, reduce_sum=False) y = add_hidden_layers(y, hidden_units=hidden_units, activation_fn=activation_fn, dropout=dropout, is_training=is_training, batch_norm=batch_norm, layer_norm=layer_norm, use_resnet=use_resnet, use_densenet=use_densenet, scope='hidden_layers') with tf.variable_scope('logits') as logits_scope: logits = fc(y, units, name=logits_scope) add_hidden_layer_summary(logits, logits_scope.name) return logits
def get_autoint_logits( features, feature_columns, shared_feature_vectors, units, is_training, extra_options): with tf.variable_scope('autoint'): _check_autoint_args(extra_options) use_shared_embedding = extra_options['autoint_use_shared_embedding'] use_project = extra_options['autoint_use_project'] project_size = extra_options['autoint_project_size'] size_per_head = extra_options['autoint_size_per_head'] num_heads = extra_options['autoint_num_heads'] num_blocks = extra_options['autoint_num_blocks'] dropout = extra_options['autoint_dropout'] has_residual = extra_options['autoint_has_residual'] if not use_shared_embedding: feature_vectors = get_feature_vectors(features, feature_columns) else: feature_vectors = shared_feature_vectors if use_project: feature_vectors = project(feature_vectors, project_size) check_feature_dims(feature_vectors) x = tf.stack(feature_vectors, axis=1) # [B, N, D] y = _autoint(x, num_blocks=num_blocks, num_units=size_per_head*num_heads, num_heads=num_heads, dropout=dropout, is_training=is_training, has_residual=has_residual) tf.logging.info("autoint output = {}".format(y)) with tf.variable_scope('logits') as logits_scope: logits = fc(y, units, name=logits_scope) add_hidden_layer_summary(logits, logits_scope.name) return logits
def shallow_fibinet(features, feature_columns, shared_feature_vectors, se_use_shared_embedding, use_project, project_size, interaction_type, se_interaction_type, use_se, name='shallow_fibinet'): """Shallow part of FiBiNET feature_vectors: list of 2-D tensors of shape [B, D], size N. Return: Tensor of shape [B, -1] """ with tf.variable_scope(name): check_feature_dims(shared_feature_vectors) y = bilinear(shared_feature_vectors, interaction_type) # [B, -1] if use_se: if se_use_shared_embedding: se_feature_vectors = shared_feature_vectors else: se_feature_vectors = get_feature_vectors(features, feature_columns) if use_project: se_feature_vectors = project(se_feature_vectors, project_size) check_feature_dims(se_feature_vectors) x = tf.stack(se_feature_vectors, axis=1) # [B, N, D] se_x = selayer(x) # [B, N, D] new_se_feature_vectors = tf.unstack(se_x, axis=1) # N tensors of shape [B, D] se_y = bilinear(new_se_feature_vectors, se_interaction_type, name='se_bilinear') # [B, -1] y = tf.concat([y, se_y], axis=1) # [B, -1] return y
def get_fibinet_logits( features, feature_columns, shared_feature_vectors, units, is_training, extra_options): with tf.variable_scope('fibinet'): _check_fibinet_args(extra_options) use_shared_embedding = extra_options['fibinet_use_shared_embedding'] use_project = extra_options['fibinet_use_project'] project_size = extra_options['fibinet_project_size'] hidden_units = extra_options['fibinet_hidden_units'] activation_fn = extra_options['fibinet_activation_fn'] dropout = extra_options['fibinet_dropout'] batch_norm = extra_options['fibinet_batch_norm'] layer_norm = extra_options['fibinet_layer_norm'] use_resnet = extra_options['fibinet_use_resnet'] use_densenet = extra_options['fibinet_use_densenet'] use_se = extra_options['fibinet_use_se'] use_deep = extra_options['fibinet_use_deep'] interaction_type = extra_options['fibinet_interaction_type'] se_interaction_type = extra_options['fibinet_se_interaction_type'] se_use_shared_embedding = extra_options['fibinet_se_use_shared_embedding'] leaky_relu_alpha = extra_options['leaky_relu_alpha'] swish_beta = extra_options['swish_beta'] activation_fn = get_activation_fn(activation_fn=activation_fn, leaky_relu_alpha=leaky_relu_alpha, swish_beta=swish_beta) if not use_shared_embedding: feature_vectors = get_feature_vectors(features, feature_columns) else: feature_vectors = shared_feature_vectors if use_project: feature_vectors = project(feature_vectors, project_size) y = shallow_fibinet(features=features, feature_columns=feature_columns, shared_feature_vectors=feature_vectors, se_use_shared_embedding=se_use_shared_embedding, use_project=use_project, project_size=project_size, interaction_type=interaction_type, se_interaction_type=se_interaction_type, use_se=use_se) # [B, -1] if use_deep: y = add_hidden_layers(y, hidden_units=hidden_units, activation_fn=activation_fn, dropout=dropout, is_training=is_training, batch_norm=batch_norm, layer_norm=layer_norm, use_resnet=use_resnet, use_densenet=use_densenet, scope='hidden_layers') with tf.variable_scope('logits') as logits_scope: logits = fc(y, units, name=logits_scope) add_hidden_layer_summary(logits, logits_scope.name) else: assert units == 1, "shallow_fibinet's units must be 1" with tf.variable_scope('logits') as logits_scope: logits = tf.reduce_sum(y, axis=-1, keepdims=True) # [B, 1] add_hidden_layer_summary(logits, logits_scope.name) return logits