def test_SDM():
model_name = "SDM"
x, y, user_feature_columns, item_feature_columns, history_feature_list = get_xy_fd_sdm(False)
if tf.__version__ >= '2.0.0':
tf.compat.v1.disable_eager_execution()
else:
K.set_learning_phase(True)
model = SDM(user_feature_columns, item_feature_columns, history_feature_list, units=8)
# model.summary()
model.compile('adam', sampledsoftmaxloss)
check_model(model, model_name, x, y)
VarLenSparseFeat(SparseFeat('prefer_genres', feature_max_idx['genres'], embedding_dim,
embedding_name="genres"), SEQ_LEN_prefer, 'mean',
'prefer_sess_length'),
]
item_feature_columns = [SparseFeat('movie_id', feature_max_idx['movie_id'], embedding_dim)]
K.set_learning_phase(True)
import tensorflow as tf
if tf.__version__ >= '2.0.0':
tf.compat.v1.disable_eager_execution()
# units must be equal to item embedding dim!
model = SDM(user_feature_columns, item_feature_columns, history_feature_list=['movie_id', 'genres'],
units=embedding_dim, num_sampled=100, )
model.compile(optimizer='adam', loss=sampledsoftmaxloss) # "binary_crossentropy")
history = model.fit(train_model_input, train_label, # train_label,
batch_size=512, epochs=1, verbose=1, validation_split=0.0, )
K.set_learning_phase(False)
# 3.Define Model,train,predict and evaluate
test_user_model_input = test_model_input
all_item_model_input = {"movie_id": item_profile['movie_id'].values, }
user_embedding_model = Model(inputs=model.user_input, outputs=model.user_embedding)
item_embedding_model = Model(inputs=model.item_input, outputs=model.item_embedding)
user_embs = user_embedding_model.predict(test_user_model_input, batch_size=2 ** 12)