def __init__(self):
self.total_emb, self.single_size, self.numerical_size, self.multi_size = _get_conf()
self.field_size = self.single_size + self.numerical_size + self.multi_size
self.embedding_length = self.field_size * Config.embedding_size
self._init_data()
self._init_placeholder()
self._init_Variable()
self._init_Model()
self.valid_batch = self._get_batch(self.valid, -1)
self.valid_label = get_label(self.valid_batch[0], 2)
self.valid_dict = {
self.ph['single_index']: self.valid_batch[1],
self.ph['numerical_index']: self.valid_batch[2],
self.ph['numerical_value']: self.valid_batch[3],
self.ph['value']: self.valid_batch[-1],
self.ph['label']: self.valid_label,
self.train_phase: False
}
if Config.multi_features:
for idx, s in enumerate(Config.multi_features):
self.valid_dict[self.ph['multi_index_%s' % s]] = self.valid_batch[4]
self.valid_dict[self.ph['multi_value_%s' % s]] = self.valid_batch[5]
self.global_step = []
self.global_train_auc = []
self.global_valid_auc = []
self._train()
self._save_loss()
def __init__(self):
self.total_emb, self.single_size, self.numerical_size, self.multi_size = _get_conf(
)
self.field_size = self.single_size + self.numerical_size + self.multi_size
self.embedding_length = self.field_size * Config.embedding_size
self._init_data()
self._init_placeholder()
self._init_Variable()
self._init_Model()
self._train()
with tf.Session() as sess:
new_saver = tf.train.import_meta_graph(meta_path)
new_saver.restore(sess, model_path)
# init = tf.global_variables_initializer()
# sess.run(init)
graph = tf.get_default_graph()
ph, train_phase = _init_placeholder(graph)
print(ph)
prediction = tf.get_collection('pred_network')[0]
# loss = tf.get_collection('loss')[0]
loss = graph.get_tensor_by_name('loss:0')
total_emb, single_size, numerical_size, multi_size = _get_conf()
field_size = single_size + numerical_size + multi_size
embedding_length = field_size * Config.embedding_size
test = _get_data(Config.test_save_file)
test = test[:30000]
test_batch = _get_batch(test,
-1,
single_size=single_size,
numerical_size=numerical_size,
multi_size=multi_size)
test_label = get_label(test_batch[0], 2)
test_dict = {
ph['single_index']: test_batch[1],
ph['numerical_index']: test_batch[2],