Python get_test_dataの例

コード例 #1

def test_AFN(afn_dnn_hidden_units, sparse_feature_num, dense_feature_num):
    model_name = 'AFN'
    sample_size = SAMPLE_SIZE
    x, y, feature_columns = get_test_data(
        sample_size, sparse_feature_num=sparse_feature_num, dense_feature_num=dense_feature_num)

    model = AFN(feature_columns, feature_columns, afn_dnn_hidden_units=afn_dnn_hidden_units, device=get_device())

    check_model(model, model_name, x, y)

コード例 #2

def test_CCPM_without_seq(sparse_feature_num, dense_feature_num):
    model_name = "CCPM"

    sample_size = SAMPLE_SIZE
    x, y, feature_dim_dict = get_test_data(
        sample_size, sparse_feature_num, dense_feature_num, sequence_feature=())

    model = CCPM(feature_dim_dict, conv_kernel_width=(3, 2), conv_filters=(2, 1), hidden_size=[32, ], keep_prob=0.5, )
    check_model(model, model_name, x, y)

コード例 #3

def test_FGCNN_without_seq(sparse_feature_num, dense_feature_num):
    model_name = "FGCNN_noseq"

    sample_size = SAMPLE_SIZE
    x, y, feature_dim_dict = get_test_data(
        sample_size, sparse_feature_num, dense_feature_num, sequence_feature=())

    model = FGCNN(feature_dim_dict, conv_kernel_width=(), conv_filters=(
    ), new_maps=(), pooling_width=(), dnn_hidden_units=(32,), dnn_dropout=0.5, )
    # TODO: add model_io check
    check_model(model, model_name, x, y, check_model_io=False)

コード例 #4

def test_FGCNN(sparse_feature_num, dense_feature_num):
    model_name = "FGCNN"

    sample_size = 32
    x, y, feature_dim_dict = get_test_data(
        sample_size, sparse_feature_num, dense_feature_num)

    model = FGCNN(feature_dim_dict, conv_kernel_width=(3, 2), conv_filters=(2, 1), new_maps=(
        2, 2), pooling_width=(2, 2), dnn_hidden_units=(32, ), dnn_dropout=0.5, )
    # TODO: add model_io check
    check_model(model, model_name, x, y, check_model_io=False)

コード例 #5

def test_CCPM_without_seq(sparse_feature_num, dense_feature_num):
    if tf.__version__ >= "2.0.0":
        return
    model_name = "CCPM"

    sample_size = SAMPLE_SIZE
    x, y, feature_columns = get_test_data(
        sample_size, sparse_feature_num, dense_feature_num, sequence_feature=())

    model = CCPM(feature_columns, feature_columns,conv_kernel_width=(3, 2), conv_filters=(
        2, 1), dnn_hidden_units=[32, ], dnn_dropout=0.5)
    check_model(model, model_name, x, y)

コード例 #6

ファイル: CCPM_test.py プロジェクト: zzAlpha/DeepCTR-Torch

def test_CCPM(sparse_feature_num, dense_feature_num):
    model_name = "CCPM"

    sample_size = SAMPLE_SIZE
    x, y, feature_columns = get_test_data(sample_size, sparse_feature_num,
                                          dense_feature_num)

    model = CCPM(feature_columns,
                 feature_columns,
                 conv_kernel_width=(3, 2),
                 conv_filters=(2, 1),
                 dnn_hidden_units=[
                     32,
                 ],
                 dnn_dropout=0.5)
    check_model(model, model_name, x, y)

コード例 #7

def test_CCPM(sparse_feature_num, dense_feature_num):
    model_name = "CCPM"

    sample_size = 32
    x, y, feature_dim_dict = get_test_data(sample_size, sparse_feature_num,
                                           dense_feature_num)

    model = CCPM(
        feature_dim_dict,
        conv_kernel_width=(3, 2),
        conv_filters=(2, 1),
        dnn_hidden_units=[
            32,
        ],
        dnn_dropout=0.5,
    )
    check_model(model, model_name, x, y)

コード例 #8

ファイル: FGCNN_test.py プロジェクト: zenwan/DeepCTR

def test_FGCNN(sparse_feature_num, dense_feature_num):
    model_name = "FGCNN"

    sample_size = 32
    x, y, feature_dim_dict = get_test_data(sample_size, sparse_feature_num,
                                           dense_feature_num)

    model = FGCNN(
        feature_dim_dict,
        conv_kernel_width=(3, 2),
        conv_filters=(2, 1),
        new_maps=(2, 2),
        pooling_width=(2, 2),
        hidden_size=[
            32,
        ],
        keep_prob=0.5,
    )
    check_model(model, model_name, x, y, check_model_io=False)

コード例 #9

ファイル: FGCNN_test.py プロジェクト: zenwan/DeepCTR

def test_FGCNN_without_seq(sparse_feature_num, dense_feature_num):
    model_name = "FGCNN"

    sample_size = SAMPLE_SIZE
    x, y, feature_dim_dict = get_test_data(sample_size,
                                           sparse_feature_num,
                                           dense_feature_num,
                                           sequence_feature=())

    model = FGCNN(
        feature_dim_dict,
        conv_kernel_width=(),
        conv_filters=(),
        new_maps=(),
        pooling_width=(),
        hidden_size=(32, ),
        keep_prob=0.5,
    )
    check_model(model, model_name, x, y, check_model_io=False)

コード例 #10

ファイル: FGCNN_test.py プロジェクト: gmlyytt-YANG/DeepCTR

def test_FGCNN(sparse_feature_num, dense_feature_num):
    model_name = "FGCNN"

    sample_size = SAMPLE_SIZE
    x, y, feature_columns = get_test_data(
        sample_size,
        embedding_size=8,
        sparse_feature_num=sparse_feature_num,
        dense_feature_num=dense_feature_num)

    model = FGCNN(
        feature_columns,
        feature_columns,
        conv_kernel_width=(3, 2),
        conv_filters=(2, 1),
        new_maps=(2, 2),
        pooling_width=(2, 2),
        dnn_hidden_units=(32, ),
        dnn_dropout=0.5,
    )
    # TODO: add model_io check
    check_model(model, model_name, x, y, check_model_io=False)