def test_DIN_att():
model_name = "DIN_att"
x, y, feature_dim_dict, behavior_feature_list = get_xy_fd()
model = DIN(
feature_dim_dict,
behavior_feature_list,
hist_len_max=4,
embedding_size=8,
use_din=True,
hidden_size=[4, 4, 4],
keep_prob=0.6,
)
model.compile('adam',
'binary_crossentropy',
metrics=['binary_crossentropy'])
model.fit(x, y, verbose=1, validation_split=0.5)
print(model_name + " test train valid pass!")
model.save_weights(model_name + '_weights.h5')
model.load_weights(model_name + '_weights.h5')
print(model_name + " test save load weight pass!")
# try:
# save_model(model, name + '.h5')
# model = load_model(name + '.h5', custom_objects)
# print(name + " test save load model pass!")
# except:
# print("【Error】There is a bug when save model use Dice---------------------------------------------------")
print(model_name + " test pass!")
def test_DIN_sum():
model_name = "DIN_sum"
x, y, feature_dim_dict, behavior_feature_list = get_xy_fd()
model = DIN(feature_dim_dict,
behavior_feature_list,
hist_len_max=4,
embedding_size=8,
use_din=False,
hidden_size=[4, 4, 4],
keep_prob=0.6,
activation="sigmoid")
model.compile('adam',
'binary_crossentropy',
metrics=['binary_crossentropy'])
model.fit(x, y, verbose=1, validation_split=0.5)
print(model_name + " test train valid pass!")
model.save_weights(model_name + '_weights.h5')
model.load_weights(model_name + '_weights.h5')
print(model_name + " test save load weight pass!")
save_model(model, model_name + '.h5')
model = load_model(model_name + '.h5', custom_objects)
print(model_name + " test save load model pass!")
print(model_name + " test pass!")
def test_DIN_att():
model_name = "DIN_att"
x, y, feature_dim_dict, behavior_feature_list = get_xy_fd()
model = DIN(feature_dim_dict, behavior_feature_list, hist_len_max=4, embedding_size=8,
use_din=True, hidden_size=[4, 4, 4], keep_prob=0.6,)
model.compile('adam', 'binary_crossentropy',
metrics=['binary_crossentropy'])
model.fit(x, y, verbose=1, validation_split=0.5)
print(model_name+" test train valid pass!")
model.save_weights(model_name + '_weights.h5')
model.load_weights(model_name + '_weights.h5')
print(model_name+" test save load weight pass!")
# try:
# save_model(model, name + '.h5')
# model = load_model(name + '.h5', custom_objects)
# print(name + " test save load model pass!")
# except:
# print("【Error】There is a bug when save model use Dice---------------------------------------------------")
print(model_name + " test pass!")
att_activation='dice',
att_weight_normalization=False,
hist_len_max=sess_len_max,
dnn_hidden_units=(200, 80),
att_hidden_size=(
64,
16,
))
model.compile('adagrad',
'binary_crossentropy',
metrics=['binary_crossentropy'])
model_dir = "../model_dir_" + str(EMBEDDING_SIZE)
if not os.path.exists(model_dir):
os.mkdir(model_dir)
if os.path.exists(model_dir + '/ckpt.h5'):
model.load_weights(model_dir + '/ckpt.h5')
"""
test_input_pos = pd.read_pickle(
'../model_input/din_input_'+test_date+'.pkl')
test_input_neg = pd.read_pickle(
'../model_input/din_input_'+test_date+'_neg.pkl')
test_input = []
for i in range(len(test_input_pos)):
feature_input = np.concatenate([test_input_pos[i],test_input_neg[i]],axis=0)
test_input.append(feature_input)
# model_input = np.concatenate([model_input_pos,model_input_neg],axis=1)
test_label_pos = pd.read_pickle('../model_input/din_label_'+test_date+'.pkl')
test_label_neg = pd.read_pickle('../model_input/din_label_'+test_date+'_neg.pkl')
test_label = np.concatenate([test_label_pos,test_label_neg],axis=-1)
del test_input_pos
