def test_WDL():
name = "WDL"
sample_size = 64
feature_dim_dict = {
'sparse': {
'sparse_1': 2,
'sparse_2': 5,
'sparse_3': 10
},
'dense': ['dense_1', 'dense_2', 'dense_3']
}
sparse_input = [
np.random.randint(0, dim, sample_size)
for dim in feature_dim_dict['sparse'].values()
]
dense_input = [
np.random.random(sample_size) for name in feature_dim_dict['dense']
]
y = np.random.randint(0, 2, sample_size)
x = sparse_input + dense_input
model = WDL(feature_dim_dict,
feature_dim_dict,
hidden_size=[32, 32],
keep_prob=0.5)
model.compile('adam',
'binary_crossentropy',
metrics=['binary_crossentropy'])
model.fit(x + x, y, batch_size=100, epochs=1, validation_split=0.5)
print(name + " test train valid pass!")
model.save_weights(name + '_weights.h5')
model.load_weights(name + '_weights.h5')
print(name + "test save load weight pass!")
save_model(model, name + '.h5')
model = load_model(name + '.h5', custom_objects)
print(name + "test save load model pass!")
print(name + " test pass!")
model_input,
data[target].values,
callbacks=[checkpoint],
batch_size=batch_size,
epochs=50,
verbose=1,
validation_split=0.2,
)
elif mode == 'test':
model = WDL({
"sparse": sparse_feature_dim,
"dense": []
},
final_activation='sigmoid')
model.load_weights(
'model_save/wdl_sample-ep001-loss0.184-val_loss0.172.h5')
# model = load_model('model_save/deep_fm_sample-ep001-loss0.192-val_loss0.176.h5')
data = pd.read_csv("./data/sample/validation.txt")
# 1.Label Encoding for sparse features,and do simple Transformation for dense features
for feat in sparse_features:
lbe = LabelEncoder()
data[feat] = lbe.fit_transform(data[feat])
# 2.count #unique features for each sparse field
sparse_feature_dim = {
feat: data[feat].nunique()
for feat in sparse_features
}
# 3.generate input data for model
