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def get_region_score(features,feature_columns, region_number, l2_reg, init_std, seed,prefix='region_',seq_mask_zero=True):
region_logit =concat_func([get_linear_logit(features, feature_columns, init_std=init_std, seed=seed + i,
prefix=prefix + str(i + 1), l2_reg=l2_reg) for i in range(region_number)])
return Activation('softmax')(region_logit)
:param afm_dropout: float in [0,1), Fraction of the attention net output units to dropout.
:param init_std: float,to use as the initialize std of embedding vector
:param seed: integer ,to use as random seed.
:param task: str, ``"binary"`` for binary logloss or ``"regression"`` for regression loss
:return: A Keras model instance.
"""
features = build_input_features(
linear_feature_columns + dnn_feature_columns)
inputs_list = list(features.values())
group_embedding_dict, _ = input_from_feature_columns(features, dnn_feature_columns, l2_reg_embedding, init_std,
seed, support_dense=False, support_group=True)
linear_logit = get_linear_logit(features, linear_feature_columns, init_std=init_std, seed=seed, prefix='linear',
l2_reg=l2_reg_linear)
if use_attention:
fm_logit = add_func([AFMLayer(attention_factor, l2_reg_att, afm_dropout,
seed)(list(v)) for k, v in group_embedding_dict.items() if k in fm_group])
else:
fm_logit = add_func([FM()(concat_func(v, axis=1))
for k, v in group_embedding_dict.items() if k in fm_group])
final_logit = add_func([linear_logit, fm_logit])
output = PredictionLayer(task)(final_logit)
model = tf.keras.models.Model(inputs=inputs_list, outputs=output)
return model
:param seed: integer ,to use as random seed.
:param task: str, ``"binary"`` for binary logloss or ``"regression"`` for regression loss
:return: A Keras model instance.
"""
if len(conv_kernel_width) != len(conv_filters):
raise ValueError(
"conv_kernel_width must have same element with conv_filters")
features = build_input_features(
linear_feature_columns + dnn_feature_columns)
inputs_list = list(features.values())
sparse_embedding_list, _ = input_from_feature_columns(features, dnn_feature_columns, l2_reg_embedding, init_std,
seed, support_dense=False)
linear_logit = get_linear_logit(features, linear_feature_columns, init_std=init_std, seed=seed,
l2_reg=l2_reg_linear)
n = len(sparse_embedding_list)
l = len(conv_filters)
conv_input = concat_func(sparse_embedding_list, axis=1)
pooling_result = tf.keras.layers.Lambda(
lambda x: tf.expand_dims(x, axis=3))(conv_input)
for i in range(1, l + 1):
filters = conv_filters[i - 1]
width = conv_kernel_width[i - 1]
k = max(1, int((1 - pow(i / l, l - i)) * n)) if i < l else 3
conv_result = tf.keras.layers.Conv2D(filters=filters, kernel_size=(width, 1), strides=(1, 1), padding='same',
activation='tanh', use_bias=True, )(pooling_result)
:param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate.
:param dnn_use_bn: bool. Whether use BatchNormalization before activation or not DNN
:param dnn_activation: Activation function to use in DNN
:param task: str, ``"binary"`` for binary logloss or ``"regression"`` for regression loss
:return: A Keras model instance.
"""
if len(dnn_hidden_units) == 0 and cross_num == 0:
raise ValueError("Either hidden_layer or cross layer must > 0")
features = build_input_features(dnn_feature_columns)
inputs_list = list(features.values())
sparse_embedding_list, dense_value_list = input_from_feature_columns(features, dnn_feature_columns,
l2_reg_embedding, init_std, seed)
linear_logit = get_linear_logit(features, linear_feature_columns, init_std=init_std, seed=seed, prefix='linear',
l2_reg=l2_reg_linear)
dnn_input = combined_dnn_input(sparse_embedding_list, dense_value_list)
if len(dnn_hidden_units) > 0 and cross_num > 0: # Deep & Cross
deep_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout,
dnn_use_bn, seed)(dnn_input)
cross_out = CrossNet(cross_num, l2_reg=l2_reg_cross)(dnn_input)
stack_out = tf.keras.layers.Concatenate()([cross_out, deep_out])
final_logit = tf.keras.layers.Dense(
1, use_bias=False, activation=None)(stack_out)
elif len(dnn_hidden_units) > 0: # Only Deep
deep_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout,
dnn_use_bn, seed)(dnn_input)
final_logit = tf.keras.layers.Dense(
1, use_bias=False, activation=None)(deep_out)
elif cross_num > 0: # Only Cross
:param init_std: float,to use as the initialize std of embedding vector
:param seed: integer ,to use as random seed.
:param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate.
:param dnn_activation: Activation function to use in DNN
:param task: str, ``"binary"`` for binary logloss or ``"regression"`` for regression loss
:return: A Keras model instance.
"""
features = build_input_features(
linear_feature_columns + dnn_feature_columns)
inputs_list = list(features.values())
sparse_embedding_list, dense_value_list = input_from_feature_columns(features, dnn_feature_columns,
l2_reg_embedding, init_std, seed)
linear_logit = get_linear_logit(features, linear_feature_columns, init_std=init_std, seed=seed, prefix='linear',
l2_reg=l2_reg_linear)
dnn_input = combined_dnn_input(sparse_embedding_list, dense_value_list)
deep_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn,
dnn_dropout, False, seed)(dnn_input)
dnn_logit = tf.keras.layers.Dense(
1, use_bias=False, activation=None)(deep_out)
final_logit = add_func([dnn_logit, linear_logit])
output = PredictionLayer(task)(final_logit)
model = tf.keras.models.Model(inputs=inputs_list,
outputs=output)
return model
:param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate.
:param init_std: float,to use as the initialize std of embedding vector
:param seed: integer ,to use as random seed.
:param task: str, ``"binary"`` for binary logloss or ``"regression"`` for regression loss
:return: A Keras model instance.
"""
if not (len(conv_kernel_width) == len(conv_filters) == len(new_maps) == len(pooling_width)):
raise ValueError(
"conv_kernel_width,conv_filters,new_maps and pooling_width must have same length")
features = build_input_features(dnn_feature_columns)
inputs_list = list(features.values())
linear_logit = get_linear_logit(features, linear_feature_columns, init_std=init_std, seed=seed, prefix='linear',
l2_reg=l2_reg_linear)
deep_emb_list, _ = input_from_feature_columns(features, dnn_feature_columns, l2_reg_embedding, init_std, seed)
fg_deep_emb_list,_ = input_from_feature_columns(features, dnn_feature_columns, l2_reg_embedding, init_std, seed,
prefix='fg')
fg_input = concat_func(fg_deep_emb_list, axis=1)
origin_input = concat_func(deep_emb_list, axis=1)
if len(conv_filters) > 0:
new_features = FGCNNLayer(
conv_filters, conv_kernel_width, new_maps, pooling_width)(fg_input)
combined_input = concat_func([origin_input, new_features], axis=1)
else:
combined_input = origin_input
:param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate.
:param dnn_activation: Activation function to use in DNN
:param dnn_use_bn: bool. Whether use BatchNormalization before activation or not in DNN
:param task: str, ``"binary"`` for binary logloss or ``"regression"`` for regression loss
:return: A Keras model instance.
"""
features = build_input_features(
linear_feature_columns + dnn_feature_columns)
inputs_list = list(features.values())
group_embedding_dict, dense_value_list = input_from_feature_columns(features, dnn_feature_columns, l2_reg_embedding,
init_std, seed, support_group=True)
linear_logit = get_linear_logit(features, linear_feature_columns, init_std=init_std, seed=seed, prefix='linear',
l2_reg=l2_reg_linear)
fm_logit = add_func([FM()(concat_func(v, axis=1))
for k, v in group_embedding_dict.items() if k in fm_group])
dnn_input = combined_dnn_input(list(chain.from_iterable(
group_embedding_dict.values())), dense_value_list)
dnn_output = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout,
dnn_use_bn, seed)(dnn_input)
dnn_logit = tf.keras.layers.Dense(
1, use_bias=False, activation=None)(dnn_output)
final_logit = add_func([linear_logit, fm_logit, dnn_logit])
output = PredictionLayer(task)(final_logit)
model = tf.keras.models.Model(inputs=inputs_list, outputs=output)
return model
:param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate.
:param dnn_activation: Activation function to use in DNN
:param dnn_use_bn: bool. Whether use BatchNormalization before activation or not in DNN
:param task: str, ``"binary"`` for binary logloss or ``"regression"`` for regression loss
:return: A Keras model instance.
"""
features = build_input_features(
linear_feature_columns + dnn_feature_columns)
inputs_list = list(features.values())
sparse_embedding_list, dense_value_list = input_from_feature_columns(features, dnn_feature_columns,
l2_reg_embedding, init_std, seed)
linear_logit = get_linear_logit(features, linear_feature_columns, init_std=init_std, seed=seed, prefix='linear',
l2_reg=l2_reg_linear)
fm_input = concat_func(sparse_embedding_list, axis=1)
dnn_input = combined_dnn_input(sparse_embedding_list, dense_value_list)
dnn_output = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout,
dnn_use_bn, seed)(dnn_input)
dnn_logit = tf.keras.layers.Dense(
1, use_bias=False, activation=None)(dnn_output)
final_logit = add_func([linear_logit, dnn_logit])
if len(cin_layer_size) > 0:
exFM_out = CIN(cin_layer_size, cin_activation,
cin_split_half, l2_reg_cin, seed)(fm_input)
exFM_logit = tf.keras.layers.Dense(1, activation=None, )(exFM_out)
:param seed: integer ,to use as random seed.
:param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate.
:param dnn_activation: Activation function to use in DNN
:param task: str, ``"binary"`` for binary logloss or ``"regression"`` for regression loss
:return: A Keras model instance.
"""
features = build_input_features(
linear_feature_columns + dnn_feature_columns)
inputs_list = list(features.values())
sparse_embedding_list, dense_value_list = input_from_feature_columns(features, dnn_feature_columns,
l2_reg_embedding, init_std, seed)
linear_logit = get_linear_logit(features, linear_feature_columns, init_std=init_std, seed=seed, prefix='linear',
l2_reg=l2_reg_linear)
dnn_input = combined_dnn_input(sparse_embedding_list, dense_value_list)
dnn_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout,
False, seed)(dnn_input)
dnn_logit = Dense(
1, use_bias=False, activation=None)(dnn_out)
final_logit = add_func([dnn_logit, linear_logit])
output = PredictionLayer(task)(final_logit)
model = Model(inputs=inputs_list, outputs=output)
return model