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modelzoo_widedeep_modify

This commit is contained in:
yao_yf 2020-06-17 15:47:41 +08:00
parent 2e002ab64c
commit 60fa330322
2 changed files with 14 additions and 13 deletions
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4
model_zoo/wide_and_deep/src/config.py
View File

@ -26,7 +26,7 @@ def argparse_init():
parser.add_argument("--batch_size", type=int, default=16000)
parser.add_argument("--eval_batch_size", type=int, default=16000)
parser.add_argument("--field_size", type=int, default=39)
parser.add_argument("--vocab_size", type=int, default=184965)
parser.add_argument("--vocab_size", type=int, default=200000)
parser.add_argument("--emb_dim", type=int, default=80)
parser.add_argument("--deep_layer_dim", type=int, nargs='+', default=[1024, 512, 256, 128])
parser.add_argument("--deep_layer_act", type=str, default='relu')
@ -50,7 +50,7 @@ class WideDeepConfig():
self.batch_size = 16000
self.eval_batch_size = 16000
self.field_size = 39
self.vocab_size = 184965
self.vocab_size = 200000
self.emb_dim = 80
self.deep_layer_dim = [1024, 512, 256, 128]
self.deep_layer_act = 'relu'

23
model_zoo/wide_and_deep/src/wide_and_deep.py
View File

@ -82,7 +82,7 @@ class DenseLayer(nn.Cell):
"""
def __init__(self, input_dim, output_dim, weight_bias_init, act_str,
keep_prob=0.7, scale_coef=1.0, convert_dtype=True, drop_out=False):
keep_prob=0.7, use_activation=True, convert_dtype=True, drop_out=False):
super(DenseLayer, self).__init__()
weight_init, bias_init = weight_bias_init
self.weight = init_method(
@ -93,9 +93,7 @@ class DenseLayer(nn.Cell):
self.bias_add = P.BiasAdd()
self.cast = P.Cast()
self.dropout = Dropout(keep_prob=keep_prob)
self.mul = P.Mul()
self.realDiv = P.RealDiv()
self.scale_coef = scale_coef
self.use_activation = use_activation
self.convert_dtype = convert_dtype
self.drop_out = drop_out
@ -110,20 +108,23 @@ class DenseLayer(nn.Cell):
return act_func
def construct(self, x):
x = self.act_func(x)
if self.training and self.drop_out:
x = self.dropout(x)
x = self.mul(x, self.scale_coef)
if self.convert_dtype:
x = self.cast(x, mstype.float16)
weight = self.cast(self.weight, mstype.float16)
bias = self.cast(self.bias, mstype.float16)
wx = self.matmul(x, weight)
wx = self.bias_add(wx, bias)
if self.use_activation:
wx = self.act_func(wx)
wx = self.cast(wx, mstype.float32)
else:
wx = self.matmul(x, self.weight)
wx = self.realDiv(wx, self.scale_coef)
output = self.bias_add(wx, self.bias)
return output
wx = self.bias_add(wx, self.bias)
if self.use_activation:
wx = self.act_func(wx)
return wx
class WideDeepModel(nn.Cell):
@ -185,7 +186,7 @@ class WideDeepModel(nn.Cell):
self.all_dim_list[5],
self.weight_bias_init,
self.deep_layer_act,
convert_dtype=True, drop_out=config.dropout_flag)
use_activation=False, convert_dtype=True, drop_out=config.dropout_flag)
self.gather_v2 = P.GatherV2()
self.mul = P.Mul()
@ -270,7 +271,7 @@ class TrainStepWrap(nn.Cell):
sens (Number): The adjust parameter. Default: 1000.0
"""
def __init__(self, network, sens=1000.0):
def __init__(self, network, sens=1024.0):
super(TrainStepWrap, self).__init__()
self.network = network
self.network.set_train()