import paddle.fluid as fluid import math from fleetrec.core.utils import envs from fleetrec.core.model import Model as ModelBase class Model(ModelBase): def __init__(self, config): ModelBase.__init__(self, config) def wide_part(self, data): out = fluid.layers.fc(input=data, size=1, param_attr=fluid.ParamAttr(initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=1.0 / math.sqrt(data.shape[1])), regularizer=fluid.regularizer.L2DecayRegularizer(regularization_coeff=1e-4)), act=None, name='wide') return out def fc(self, data, hidden_units, active, tag): output = fluid.layers.fc(input=data, size=hidden_units, param_attr=fluid.ParamAttr(initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=1.0 / math.sqrt(data.shape[1]))), act=active, name=tag) return output def deep_part(self, data, hidden1_units, hidden2_units, hidden3_units): l1 = self.fc(data, hidden1_units, 'relu', 'l1') l2 = self.fc(l1, hidden2_units, 'relu', 'l2') l3 = self.fc(l2, hidden3_units, 'relu', 'l3') return l3 def train_net(self): wide_input = fluid.data(name='wide_input', shape=[None, 8], dtype='float32') deep_input = fluid.data(name='deep_input', shape=[None, 58], dtype='float32') label = fluid.data(name='label', shape=[None, 1], dtype='float32') self._data_var.append(wide_input) self._data_var.append(deep_input) self._data_var.append(label) hidden1_units = envs.get_global_env("hyper_parameters.hidden1_units", 75, self._namespace) hidden2_units = envs.get_global_env("hyper_parameters.hidden2_units", 50, self._namespace) hidden3_units = envs.get_global_env("hyper_parameters.hidden3_units", 25, self._namespace) wide_output = self.wide_part(wide_input) deep_output = self.deep_part(deep_input, hidden1_units, hidden2_units, hidden3_units) wide_model = fluid.layers.fc(input=wide_output, size=1, param_attr=fluid.ParamAttr(initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=1.0)), act=None, name='w_wide') deep_model = fluid.layers.fc(input=deep_output, size=1, param_attr=fluid.ParamAttr(initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=1.0)), act=None, name='w_deep') prediction = fluid.layers.elementwise_add(wide_model, deep_model) pred = fluid.layers.sigmoid(fluid.layers.clip(prediction, min=-15.0, max=15.0), name="prediction") num_seqs = fluid.layers.create_tensor(dtype='int64') acc = fluid.layers.accuracy(input=pred, label=fluid.layers.cast(x=label, dtype='int64'), total=num_seqs) auc_var, batch_auc, auc_states = fluid.layers.auc(input=pred, label=fluid.layers.cast(x=label, dtype='int64')) self._metrics["AUC"] = auc_var self._metrics["BATCH_AUC"] = batch_auc self._metrics["ACC"] = acc cost = fluid.layers.sigmoid_cross_entropy_with_logits(x=prediction, label=label) avg_cost = fluid.layers.mean(cost) self._cost = avg_cost def optimizer(self): learning_rate = envs.get_global_env("hyper_parameters.learning_rate", None, self._namespace) optimizer = fluid.optimizer.Adam(learning_rate, lazy_mode=True) return optimizer def infer_net(self, parameter_list): self.deepfm_net()