-
- 
- Zan Shuxun
- Dev
Beijing University
of Posts and
Telecommunications
- |
Zeng Kai
diff --git a/deepctr_torch/__init__.py b/deepctr_torch/__init__.py
index 760ef662..6161b74f 100644
--- a/deepctr_torch/__init__.py
+++ b/deepctr_torch/__init__.py
@@ -2,5 +2,5 @@
from . import models
from .utils import check_version
-__version__ = '0.2.3'
+__version__ = '0.2.4'
check_version(__version__)
\ No newline at end of file
diff --git a/deepctr_torch/callbacks.py b/deepctr_torch/callbacks.py
index d3fd7aed..d1a69fe5 100644
--- a/deepctr_torch/callbacks.py
+++ b/deepctr_torch/callbacks.py
@@ -1,9 +1,10 @@
import torch
from tensorflow.python.keras.callbacks import EarlyStopping
from tensorflow.python.keras.callbacks import ModelCheckpoint
+from tensorflow.python.keras.callbacks import History
EarlyStopping = EarlyStopping
-
+History = History
class ModelCheckpoint(ModelCheckpoint):
"""Save the model after every epoch.
diff --git a/deepctr_torch/inputs.py b/deepctr_torch/inputs.py
index ea272fb1..4371d404 100644
--- a/deepctr_torch/inputs.py
+++ b/deepctr_torch/inputs.py
@@ -266,6 +266,6 @@ def get_dense_input(X, features, feature_columns):
def maxlen_lookup(X, sparse_input_dict, maxlen_column):
if maxlen_column is None or len(maxlen_column)==0:
- raise ValueError('please add max length column for VarLenSparseFeat of DIEN input')
+ raise ValueError('please add max length column for VarLenSparseFeat of DIN/DIEN input')
lookup_idx = np.array(sparse_input_dict[maxlen_column[0]])
return X[:, lookup_idx[0]:lookup_idx[1]].long()
diff --git a/deepctr_torch/layers/activation.py b/deepctr_torch/layers/activation.py
index 6e2a8c35..4ba8758e 100644
--- a/deepctr_torch/layers/activation.py
+++ b/deepctr_torch/layers/activation.py
@@ -25,10 +25,11 @@ def __init__(self, emb_size, dim=2, epsilon=1e-8, device='cpu'):
self.sigmoid = nn.Sigmoid()
self.dim = dim
+ # wrap alpha in nn.Parameter to make it trainable
if self.dim == 2:
- self.alpha = torch.zeros((emb_size,)).to(device)
+ self.alpha = nn.Parameter(torch.zeros((emb_size,)).to(device))
else:
- self.alpha = torch.zeros((emb_size, 1)).to(device)
+ self.alpha = nn.Parameter(torch.zeros((emb_size, 1)).to(device))
def forward(self, x):
assert x.dim() == self.dim
diff --git a/deepctr_torch/layers/interaction.py b/deepctr_torch/layers/interaction.py
index f4ccd1b8..11213647 100644
--- a/deepctr_torch/layers/interaction.py
+++ b/deepctr_torch/layers/interaction.py
@@ -512,7 +512,7 @@ def forward(self, inputs):
# (2) E(x_l)
# project the input x_l to $\mathbb{R}^{r}$
- v_x = torch.matmul(self.V_list[i][expert_id].T, x_l) # (bs, low_rank, 1)
+ v_x = torch.matmul(self.V_list[i][expert_id].t(), x_l) # (bs, low_rank, 1)
# nonlinear activation in low rank space
v_x = torch.tanh(v_x)
diff --git a/deepctr_torch/models/afm.py b/deepctr_torch/models/afm.py
index 19eeaf9e..caf508ea 100644
--- a/deepctr_torch/models/afm.py
+++ b/deepctr_torch/models/afm.py
@@ -43,7 +43,7 @@ def __init__(self, linear_feature_columns, dnn_feature_columns, use_attention=Tr
if use_attention:
self.fm = AFMLayer(self.embedding_size, attention_factor, l2_reg_att, afm_dropout,
seed, device)
- self.add_regularization_weight(self.fm.attention_W, l2_reg_att)
+ self.add_regularization_weight(self.fm.attention_W, l2=l2_reg_att)
else:
self.fm = FM()
diff --git a/deepctr_torch/models/autoint.py b/deepctr_torch/models/autoint.py
index 027e229b..c3b10eaa 100644
--- a/deepctr_torch/models/autoint.py
+++ b/deepctr_torch/models/autoint.py
@@ -69,7 +69,7 @@ def __init__(self, linear_feature_columns, dnn_feature_columns, att_layer_num=3,
activation=dnn_activation, l2_reg=l2_reg_dnn, dropout_rate=dnn_dropout, use_bn=dnn_use_bn,
init_std=init_std, device=device)
self.add_regularization_weight(
- filter(lambda x: 'weight' in x[0] and 'bn' not in x[0], self.dnn.named_parameters()), l2_reg_dnn)
+ filter(lambda x: 'weight' in x[0] and 'bn' not in x[0], self.dnn.named_parameters()), l2=l2_reg_dnn)
self.int_layers = nn.ModuleList(
[InteractingLayer(self.embedding_size if i == 0 else att_embedding_size * att_head_num,
att_embedding_size, att_head_num, att_res, device=device) for i in range(att_layer_num)])
diff --git a/deepctr_torch/models/basemodel.py b/deepctr_torch/models/basemodel.py
index f0942b75..6d7998f5 100644
--- a/deepctr_torch/models/basemodel.py
+++ b/deepctr_torch/models/basemodel.py
@@ -26,8 +26,8 @@
from ..inputs import build_input_features, SparseFeat, DenseFeat, VarLenSparseFeat, get_varlen_pooling_list, \
create_embedding_matrix
from ..layers import PredictionLayer
-
from ..layers.utils import slice_arrays
+from ..callbacks import History
class Linear(nn.Module):
@@ -55,8 +55,8 @@ def __init__(self, feature_columns, feature_index, init_std=0.0001, device='cpu'
nn.init.normal_(tensor.weight, mean=0, std=init_std)
if len(self.dense_feature_columns) > 0:
- self.weight = nn.Parameter(torch.Tensor(sum(fc.dimension for fc in self.dense_feature_columns), 1)).to(
- device)
+ self.weight = nn.Parameter(torch.Tensor(sum(fc.dimension for fc in self.dense_feature_columns), 1).to(
+ device))
torch.nn.init.normal_(self.weight, mean=0, std=init_std)
def forward(self, X):
@@ -117,14 +117,16 @@ def __init__(self, linear_feature_columns, dnn_feature_columns, l2_reg_linear=1e
self.regularization_weight = []
- self.add_regularization_weight(
- self.embedding_dict.parameters(), l2_reg_embedding)
- self.add_regularization_weight(
- self.linear_model.parameters(), l2_reg_linear)
+ self.add_regularization_weight(self.embedding_dict.parameters(), l2=l2_reg_embedding)
+ self.add_regularization_weight(self.linear_model.parameters(), l2=l2_reg_linear)
self.out = PredictionLayer(task, )
self.to(device)
- self._is_graph_network = True # used for callbacks
+
+ # parameters of callbacks
+ self._is_graph_network = True # used for ModelCheckpoint
+ self.stop_training = False # used for EarlyStopping
+ self.history = History()
def fit(self, x=None, y=None, batch_size=None, epochs=1, verbose=1, initial_epoch=0, validation_split=0.,
validation_data=None, shuffle=True, callbacks=None):
@@ -142,6 +144,7 @@ def fit(self, x=None, y=None, batch_size=None, epochs=1, verbose=1, initial_epoc
:param shuffle: Boolean. Whether to shuffle the order of the batches at the beginning of each epoch.
:param callbacks: List of `deepctr_torch.callbacks.Callback` instances. List of callbacks to apply during training and validation (if ). See [callbacks](https://tensorflow.google.cn/api_docs/python/tf/keras/callbacks). Now available: `EarlyStopping` , `ModelCheckpoint`
+ :return: A `History` object. Its `History.history` attribute is a record of training loss values and metrics values at successive epochs, as well as validation loss values and validation metrics values (if applicable).
"""
if isinstance(x, dict):
x = [x[feature] for feature in self.feature_index]
@@ -200,10 +203,14 @@ def fit(self, x=None, y=None, batch_size=None, epochs=1, verbose=1, initial_epoc
sample_num = len(train_tensor_data)
steps_per_epoch = (sample_num - 1) // batch_size + 1
+ # configure callbacks
+ callbacks = (callbacks or []) + [self.history] # add history callback
callbacks = CallbackList(callbacks)
- callbacks.set_model(self)
callbacks.on_train_begin()
- self.stop_training = False # used for early stopping
+ callbacks.set_model(self)
+ if not hasattr(callbacks, 'model'):
+ callbacks.__setattr__('model', self)
+ callbacks.model.stop_training = False
# Train
print("Train on {0} samples, validate on {1} samples, {2} steps per epoch".format(
@@ -231,7 +238,7 @@ def fit(self, x=None, y=None, batch_size=None, epochs=1, verbose=1, initial_epoc
loss_epoch += loss.item()
total_loss_epoch += total_loss.item()
- total_loss.backward(retain_graph=True)
+ total_loss.backward()
optim.step()
if verbose > 0:
@@ -279,6 +286,8 @@ def fit(self, x=None, y=None, batch_size=None, epochs=1, verbose=1, initial_epoc
callbacks.on_train_end()
+ return self.history
+
def evaluate(self, x, y, batch_size=256):
"""
@@ -368,21 +377,32 @@ def compute_input_dim(self, feature_columns, include_sparse=True, include_dense=
input_dim += dense_input_dim
return input_dim
- def add_regularization_weight(self, weight_list, weight_decay, p=2):
- self.regularization_weight.append((list(weight_list), weight_decay, p))
+ def add_regularization_weight(self, weight_list, l1=0.0, l2=0.0):
+ # For a Parameter, put it in a list to keep Compatible with get_regularization_loss()
+ if isinstance(weight_list, torch.nn.parameter.Parameter):
+ weight_list = [weight_list]
+ # For generators, filters and ParameterLists, convert them to a list of tensors to avoid bugs.
+ # e.g., we can't pickle generator objects when we save the model.
+ else:
+ weight_list = list(weight_list)
+ self.regularization_weight.append((weight_list, l1, l2))
def get_regularization_loss(self, ):
total_reg_loss = torch.zeros((1,), device=self.device)
- for weight_list, weight_decay, p in self.regularization_weight:
- weight_reg_loss = torch.zeros((1,), device=self.device)
+ for weight_list, l1, l2 in self.regularization_weight:
for w in weight_list:
if isinstance(w, tuple):
- l2_reg = torch.norm(w[1], p=p, )
+ parameter = w[1] # named_parameters
else:
- l2_reg = torch.norm(w, p=p, )
- weight_reg_loss = weight_reg_loss + l2_reg
- reg_loss = weight_decay * weight_reg_loss
- total_reg_loss += reg_loss
+ parameter = w
+ if l1 > 0:
+ total_reg_loss += torch.sum(l1 * torch.abs(parameter))
+ if l2 > 0:
+ try:
+ total_reg_loss += torch.sum(l2 * torch.square(parameter))
+ except AttributeError:
+ total_reg_loss += torch.sum(l2 * parameter * parameter)
+
return total_reg_loss
def add_auxiliary_loss(self, aux_loss, alpha):
diff --git a/deepctr_torch/models/ccpm.py b/deepctr_torch/models/ccpm.py
index 62767271..73272b66 100644
--- a/deepctr_torch/models/ccpm.py
+++ b/deepctr_torch/models/ccpm.py
@@ -60,8 +60,8 @@ def __init__(self, linear_feature_columns, dnn_feature_columns, conv_kernel_widt
init_std=init_std, device=device)
self.dnn_linear = nn.Linear(dnn_hidden_units[-1], 1, bias=False).to(device)
self.add_regularization_weight(
- filter(lambda x: 'weight' in x[0] and 'bn' not in x[0], self.dnn.named_parameters()), l2_reg_dnn)
- self.add_regularization_weight(self.dnn_linear.weight, l2_reg_dnn)
+ filter(lambda x: 'weight' in x[0] and 'bn' not in x[0], self.dnn.named_parameters()), l2=l2_reg_dnn)
+ self.add_regularization_weight(self.dnn_linear.weight, l2=l2_reg_dnn)
self.to(device)
diff --git a/deepctr_torch/models/dcn.py b/deepctr_torch/models/dcn.py
index df8b71e2..4528b9a7 100644
--- a/deepctr_torch/models/dcn.py
+++ b/deepctr_torch/models/dcn.py
@@ -65,9 +65,9 @@ def __init__(self, linear_feature_columns, dnn_feature_columns, cross_num=2, cro
self.crossnet = CrossNet(in_features=self.compute_input_dim(dnn_feature_columns),
layer_num=cross_num, parameterization=cross_parameterization, device=device)
self.add_regularization_weight(
- filter(lambda x: 'weight' in x[0] and 'bn' not in x[0], self.dnn.named_parameters()), l2_reg_dnn)
- self.add_regularization_weight(self.dnn_linear.weight, l2_reg_linear)
- self.add_regularization_weight(self.crossnet.kernels, l2_reg_cross)
+ filter(lambda x: 'weight' in x[0] and 'bn' not in x[0], self.dnn.named_parameters()), l2=l2_reg_dnn)
+ self.add_regularization_weight(self.dnn_linear.weight, l2=l2_reg_linear)
+ self.add_regularization_weight(self.crossnet.kernels, l2=l2_reg_cross)
self.to(device)
def forward(self, X):
diff --git a/deepctr_torch/models/dcnmix.py b/deepctr_torch/models/dcnmix.py
index 8fb95a73..c01fd44c 100644
--- a/deepctr_torch/models/dcnmix.py
+++ b/deepctr_torch/models/dcnmix.py
@@ -68,11 +68,11 @@ def __init__(self, linear_feature_columns,
low_rank=low_rank, num_experts=num_experts,
layer_num=cross_num, device=device)
self.add_regularization_weight(
- filter(lambda x: 'weight' in x[0] and 'bn' not in x[0], self.dnn.named_parameters()), l2_reg_dnn)
- self.add_regularization_weight(self.dnn_linear.weight, l2_reg_linear)
- self.add_regularization_weight(self.crossnet.U_list, l2_reg_cross)
- self.add_regularization_weight(self.crossnet.V_list, l2_reg_cross)
- self.add_regularization_weight(self.crossnet.C_list, l2_reg_cross)
+ filter(lambda x: 'weight' in x[0] and 'bn' not in x[0], self.dnn.named_parameters()), l2=l2_reg_dnn)
+ self.add_regularization_weight(self.dnn_linear.weight, l2=l2_reg_linear)
+ self.add_regularization_weight(self.crossnet.U_list, l2=l2_reg_cross)
+ self.add_regularization_weight(self.crossnet.V_list, l2=l2_reg_cross)
+ self.add_regularization_weight(self.crossnet.C_list, l2=l2_reg_cross)
self.to(device)
def forward(self, X):
diff --git a/deepctr_torch/models/deepfm.py b/deepctr_torch/models/deepfm.py
index 5c351dcc..187c2592 100644
--- a/deepctr_torch/models/deepfm.py
+++ b/deepctr_torch/models/deepfm.py
@@ -59,8 +59,8 @@ def __init__(self,
dnn_hidden_units[-1], 1, bias=False).to(device)
self.add_regularization_weight(
- filter(lambda x: 'weight' in x[0] and 'bn' not in x[0], self.dnn.named_parameters()), l2_reg_dnn)
- self.add_regularization_weight(self.dnn_linear.weight, l2_reg_dnn)
+ filter(lambda x: 'weight' in x[0] and 'bn' not in x[0], self.dnn.named_parameters()), l2=l2_reg_dnn)
+ self.add_regularization_weight(self.dnn_linear.weight, l2=l2_reg_dnn)
self.to(device)
def forward(self, X):
diff --git a/deepctr_torch/models/din.py b/deepctr_torch/models/din.py
index 19cc8dfa..0a8e46af 100644
--- a/deepctr_torch/models/din.py
+++ b/deepctr_torch/models/din.py
@@ -63,7 +63,7 @@ def __init__(self, dnn_feature_columns, history_feature_list, dnn_use_bn=False,
self.attention = AttentionSequencePoolingLayer(att_hidden_units=att_hidden_size,
embedding_dim=att_emb_dim,
- activation=att_activation,
+ att_activation=att_activation,
return_score=False,
supports_masking=False,
weight_normalization=att_weight_normalization)
@@ -79,16 +79,15 @@ def __init__(self, dnn_feature_columns, history_feature_list, dnn_use_bn=False,
def forward(self, X):
- sparse_embedding_list, dense_value_list = self.input_from_feature_columns(X, self.dnn_feature_columns,
- self.embedding_dict)
+ _, dense_value_list = self.input_from_feature_columns(X, self.dnn_feature_columns, self.embedding_dict)
# sequence pooling part
query_emb_list = embedding_lookup(X, self.embedding_dict, self.feature_index, self.sparse_feature_columns,
- self.history_feature_list, self.history_feature_list, to_list=True)
+ return_feat_list=self.history_feature_list, to_list=True)
keys_emb_list = embedding_lookup(X, self.embedding_dict, self.feature_index, self.history_feature_columns,
- self.history_fc_names, self.history_fc_names, to_list=True)
- dnn_input_emb_list = embedding_lookup(X, self.embedding_dict, self.feature_index, self.sparse_feature_columns, mask_feat_list=self.history_feature_list, to_list=True)
-
+ return_feat_list=self.history_fc_names, to_list=True)
+ dnn_input_emb_list = embedding_lookup(X, self.embedding_dict, self.feature_index, self.sparse_feature_columns,
+ to_list=True)
sequence_embed_dict = varlen_embedding_lookup(X, self.embedding_dict, self.feature_index,
self.sparse_varlen_feature_columns)
@@ -97,12 +96,15 @@ def forward(self, X):
self.sparse_varlen_feature_columns, self.device)
dnn_input_emb_list += sequence_embed_list
+ deep_input_emb = torch.cat(dnn_input_emb_list, dim=-1)
# concatenate
query_emb = torch.cat(query_emb_list, dim=-1) # [B, 1, E]
keys_emb = torch.cat(keys_emb_list, dim=-1) # [B, T, E]
- keys_length = torch.ones((query_emb.size(0), 1)).to(self.device) # [B, 1]
- deep_input_emb = torch.cat(dnn_input_emb_list, dim=-1)
+
+ keys_length_feature_name = [feat.length_name for feat in self.varlen_sparse_feature_columns if
+ feat.length_name is not None]
+ keys_length = torch.squeeze(maxlen_lookup(X, self.feature_index, keys_length_feature_name), 1) # [B, 1]
hist = self.attention(query_emb, keys_emb, keys_length) # [B, 1, E]
diff --git a/deepctr_torch/models/nfm.py b/deepctr_torch/models/nfm.py
index d5ed9d9a..73a15923 100644
--- a/deepctr_torch/models/nfm.py
+++ b/deepctr_torch/models/nfm.py
@@ -48,8 +48,8 @@ def __init__(self,
self.dnn_linear = nn.Linear(
dnn_hidden_units[-1], 1, bias=False).to(device)
self.add_regularization_weight(
- filter(lambda x: 'weight' in x[0] and 'bn' not in x[0], self.dnn.named_parameters()), l2_reg_dnn)
- self.add_regularization_weight(self.dnn_linear.weight, l2_reg_dnn)
+ filter(lambda x: 'weight' in x[0] and 'bn' not in x[0], self.dnn.named_parameters()), l2=l2_reg_dnn)
+ self.add_regularization_weight(self.dnn_linear.weight, l2=l2_reg_dnn)
self.bi_pooling = BiInteractionPooling()
self.bi_dropout = bi_dropout
if self.bi_dropout > 0:
diff --git a/deepctr_torch/models/onn.py b/deepctr_torch/models/onn.py
index bdab09eb..b4d4d085 100644
--- a/deepctr_torch/models/onn.py
+++ b/deepctr_torch/models/onn.py
@@ -69,8 +69,7 @@ def __init__(self, linear_feature_columns, dnn_feature_columns,
dnn_feature_columns, embedding_size=embedding_size, sparse=False).to(device)
# add regularization for second_order_embedding
- self.add_regularization_weight(
- self.second_order_embedding_dict.parameters(), l2_reg_embedding)
+ self.add_regularization_weight(self.second_order_embedding_dict.parameters(), l2=l2_reg_embedding)
dim = self.__compute_nffm_dnn_dim(
feature_columns=dnn_feature_columns, embedding_size=embedding_size)
@@ -82,8 +81,8 @@ def __init__(self, linear_feature_columns, dnn_feature_columns,
self.dnn_linear = nn.Linear(
dnn_hidden_units[-1], 1, bias=False).to(device)
self.add_regularization_weight(
- filter(lambda x: 'weight' in x[0] and 'bn' not in x[0], self.dnn.named_parameters()), l2_reg_dnn)
- self.add_regularization_weight(self.dnn_linear.weight, l2_reg_dnn)
+ filter(lambda x: 'weight' in x[0] and 'bn' not in x[0], self.dnn.named_parameters()), l2=l2_reg_dnn)
+ self.add_regularization_weight(self.dnn_linear.weight, l2=l2_reg_dnn)
self.to(device)
def __compute_nffm_dnn_dim(self, feature_columns, embedding_size):
diff --git a/deepctr_torch/models/pnn.py b/deepctr_torch/models/pnn.py
index 00fb3416..7a79d827 100644
--- a/deepctr_torch/models/pnn.py
+++ b/deepctr_torch/models/pnn.py
@@ -69,8 +69,8 @@ def __init__(self, dnn_feature_columns, dnn_hidden_units=(128, 128), l2_reg_embe
self.dnn_linear = nn.Linear(
dnn_hidden_units[-1], 1, bias=False).to(device)
self.add_regularization_weight(
- filter(lambda x: 'weight' in x[0] and 'bn' not in x[0], self.dnn.named_parameters()), l2_reg_dnn)
- self.add_regularization_weight(self.dnn_linear.weight, l2_reg_dnn)
+ filter(lambda x: 'weight' in x[0] and 'bn' not in x[0], self.dnn.named_parameters()), l2=l2_reg_dnn)
+ self.add_regularization_weight(self.dnn_linear.weight, l2=l2_reg_dnn)
self.to(device)
diff --git a/deepctr_torch/models/wdl.py b/deepctr_torch/models/wdl.py
index f65b585a..0fdd374c 100644
--- a/deepctr_torch/models/wdl.py
+++ b/deepctr_torch/models/wdl.py
@@ -51,8 +51,8 @@ def __init__(self,
init_std=init_std, device=device)
self.dnn_linear = nn.Linear(dnn_hidden_units[-1], 1, bias=False).to(device)
self.add_regularization_weight(
- filter(lambda x: 'weight' in x[0] and 'bn' not in x[0], self.dnn.named_parameters()), l2_reg_dnn)
- self.add_regularization_weight(self.dnn_linear.weight, l2_reg_dnn)
+ filter(lambda x: 'weight' in x[0] and 'bn' not in x[0], self.dnn.named_parameters()), l2=l2_reg_dnn)
+ self.add_regularization_weight(self.dnn_linear.weight, l2=l2_reg_dnn)
self.to(device)
diff --git a/deepctr_torch/models/xdeepfm.py b/deepctr_torch/models/xdeepfm.py
index dc00ec4c..87cac472 100644
--- a/deepctr_torch/models/xdeepfm.py
+++ b/deepctr_torch/models/xdeepfm.py
@@ -54,9 +54,9 @@ def __init__(self, linear_feature_columns, dnn_feature_columns, dnn_hidden_units
init_std=init_std, device=device)
self.dnn_linear = nn.Linear(dnn_hidden_units[-1], 1, bias=False).to(device)
self.add_regularization_weight(
- filter(lambda x: 'weight' in x[0] and 'bn' not in x[0], self.dnn.named_parameters()), l2_reg_dnn)
+ filter(lambda x: 'weight' in x[0] and 'bn' not in x[0], self.dnn.named_parameters()), l2=l2_reg_dnn)
- self.add_regularization_weight(self.dnn_linear.weight, l2_reg_dnn)
+ self.add_regularization_weight(self.dnn_linear.weight, l2=l2_reg_dnn)
self.cin_layer_size = cin_layer_size
self.use_cin = len(self.cin_layer_size) > 0 and len(dnn_feature_columns) > 0
@@ -70,8 +70,8 @@ def __init__(self, linear_feature_columns, dnn_feature_columns, dnn_hidden_units
self.cin = CIN(field_num, cin_layer_size,
cin_activation, cin_split_half, l2_reg_cin, seed, device=device)
self.cin_linear = nn.Linear(self.featuremap_num, 1, bias=False).to(device)
- self.add_regularization_weight(
- filter(lambda x: 'weight' in x[0], self.cin.named_parameters()), l2_reg_cin)
+ self.add_regularization_weight(filter(lambda x: 'weight' in x[0], self.cin.named_parameters()),
+ l2=l2_reg_cin)
self.to(device)
diff --git a/docs/source/FAQ.md b/docs/source/FAQ.md
index 2909aacb..3399bb06 100644
--- a/docs/source/FAQ.md
+++ b/docs/source/FAQ.md
@@ -33,8 +33,9 @@ model = DeepFM(linear_feature_columns,dnn_feature_columns)
model.compile(Adagrad(model.parameters(),0.1024),'binary_crossentropy',metrics=['binary_crossentropy'])
es = EarlyStopping(monitor='val_binary_crossentropy', min_delta=0, verbose=1, patience=0, mode='min')
-mdckpt = ModelCheckpoint(filepath='model.ckpt')
+mdckpt = ModelCheckpoint(filepath = 'model.ckpt', save_best_only= True)
history = model.fit(model_input,data[target].values,batch_size=256,epochs=10,verbose=2,validation_split=0.2,callbacks=[es,mdckpt])
+print(history)
```
## 3. How to add a long dense feature vector as a input to the model?
diff --git a/docs/source/History.md b/docs/source/History.md
index 713480b8..c0469087 100644
--- a/docs/source/History.md
+++ b/docs/source/History.md
@@ -1,4 +1,5 @@
# History
+- 12/05/2020 : [v0.2.4](https://github.com/shenweichen/DeepCTR-Torch/releases/tag/v0.2.4) released.Imporve compatibility & fix issues.Add History callback.([example](https://deepctr-torch.readthedocs.io/en/latest/FAQ.html#set-learning-rate-and-use-earlystopping)).
- 10/18/2020 : [v0.2.3](https://github.com/shenweichen/DeepCTR-Torch/releases/tag/v0.2.3) released.Add [DCN-M](./Features.html#dcn-deep-cross-network)&[DCN-Mix](./Features.html#dcn-mix-improved-deep-cross-network-with-mix-of-experts-and-matrix-kernel).Add EarlyStopping and ModelCheckpoint callbacks([example](https://deepctr-torch.readthedocs.io/en/latest/FAQ.html#set-learning-rate-and-use-earlystopping)).
- 10/09/2020 : [v0.2.2](https://github.com/shenweichen/DeepCTR-Torch/releases/tag/v0.2.2) released.Improve the reproducibility & fix some bugs.
- 03/27/2020 : [v0.2.1](https://github.com/shenweichen/DeepCTR-Torch/releases/tag/v0.2.1) released.Add [DIN](./Features.html#din-deep-interest-network) and [DIEN](./Features.html#dien-deep-interest-evolution-network) .
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 425de06e..40f2e387 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -26,7 +26,7 @@
# The short X.Y version
version = ''
# The full version, including alpha/beta/rc tags
-release = '0.2.3'
+release = '0.2.4'
# -- General configuration ---------------------------------------------------
diff --git a/docs/source/index.rst b/docs/source/index.rst
index d1d154ff..fc1cec28 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -34,12 +34,12 @@ You can read the latest code at https://github.com/shenweichen/DeepCTR-Torch and
News
-----
+12/05/2020 : Imporve compatibility & fix issues.Add History callback(`example `_). `Changelog `_
+
10/18/2020 : Add `DCN-M <./Features.html#dcn-deep-cross-network>`_ and `DCN-Mix <./Features.html#dcn-mix-improved-deep-cross-network-with-mix-of-experts-and-matrix-kernel>`_ . Add EarlyStopping and ModelCheckpoint callbacks(`example `_). `Changelog `_
10/09/2020 : Improve the reproducibility & fix some bugs. `Changelog `_
-03/27/2020 : Add `DIN <./Features.html#din-deep-interest-network>`_ and `DIEN <./Features.html#dien-deep-interest-evolution-network>`_ . `Changelog `_
-
DisscussionGroup
-----------------------
diff --git a/examples/run_classification_criteo.py b/examples/run_classification_criteo.py
index 1201d2b0..881fdfbb 100644
--- a/examples/run_classification_criteo.py
+++ b/examples/run_classification_criteo.py
@@ -58,8 +58,8 @@
model.compile("adagrad", "binary_crossentropy",
metrics=["binary_crossentropy", "auc"], )
- model.fit(train_model_input, train[target].values, batch_size=32, epochs=10, verbose=2, validation_split=0.2)
-
+ history = model.fit(train_model_input, train[target].values, batch_size=32, epochs=10, verbose=2,
+ validation_split=0.2)
pred_ans = model.predict(test_model_input, 256)
print("")
print("test LogLoss", round(log_loss(test[target].values, pred_ans), 4))
diff --git a/examples/run_din.py b/examples/run_din.py
index fccc02c3..de716e16 100644
--- a/examples/run_din.py
+++ b/examples/run_din.py
@@ -14,9 +14,8 @@ def get_xy_fd():
SparseFeat('item', 3 + 1, embedding_dim=8), SparseFeat('item_gender', 2 + 1, embedding_dim=8),
DenseFeat('score', 1)]
- feature_columns += [VarLenSparseFeat(SparseFeat('hist_item', 3 + 1, embedding_dim=8), 4),
- VarLenSparseFeat(SparseFeat('hist_item_gender', 2 + 1, embedding_dim=8), 4)]
-
+ feature_columns += [VarLenSparseFeat(SparseFeat('hist_item', 3 + 1, embedding_dim=8), 4, length_name="seq_length"),
+ VarLenSparseFeat(SparseFeat('hist_item_gender', 2 + 1, embedding_dim=8), 4, length_name="seq_length")]
behavior_feature_list = ["item", "item_gender"]
uid = np.array([0, 1, 2])
ugender = np.array([0, 1, 0])
@@ -26,9 +25,11 @@ def get_xy_fd():
hist_iid = np.array([[1, 2, 3, 0], [1, 2, 3, 0], [1, 2, 0, 0]])
hist_igender = np.array([[1, 1, 2, 0], [2, 1, 1, 0], [2, 1, 0, 0]])
+ behavior_length = np.array([3, 3, 2])
feature_dict = {'user': uid, 'gender': ugender, 'item': iid, 'item_gender': igender,
- 'hist_item': hist_iid, 'hist_item_gender': hist_igender, 'score': score}
+ 'hist_item': hist_iid, 'hist_item_gender': hist_igender, 'score': score,
+ "seq_length": behavior_length}
x = {name: feature_dict[name] for name in get_feature_names(feature_columns)}
y = np.array([1, 0, 1])
@@ -43,7 +44,7 @@ def get_xy_fd():
print('cuda ready...')
device = 'cuda:0'
- model = DIN(feature_columns, behavior_feature_list, device=device)
+ model = DIN(feature_columns, behavior_feature_list, device=device, att_weight_normalization=True)
model.compile('adagrad', 'binary_crossentropy',
metrics=['binary_crossentropy'])
history = model.fit(x, y, batch_size=3, epochs=10, verbose=2, validation_split=0.0)
diff --git a/setup.py b/setup.py
index dd2bc9f3..5a44ccd1 100644
--- a/setup.py
+++ b/setup.py
@@ -9,7 +9,7 @@
setuptools.setup(
name="deepctr-torch",
- version="0.2.3",
+ version="0.2.4",
author="Weichen Shen",
author_email="wcshen1994@163.com",
description="Easy-to-use,Modular and Extendible package of deep learning based CTR(Click Through Rate) prediction models with PyTorch",
diff --git a/tests/models/DCNMix_test.py b/tests/models/DCNMix_test.py
index ce1119fa..1dc90ef6 100644
--- a/tests/models/DCNMix_test.py
+++ b/tests/models/DCNMix_test.py
@@ -7,7 +7,7 @@
@pytest.mark.parametrize(
'embedding_size,cross_num,hidden_size,sparse_feature_num',
- [(8, 0, (32,), 2),
+ [(8, 0, (32,), 2), (8, 1, (32,), 2)
] # ('auto', 1, (32,), 3) , ('auto', 1, (), 1), ('auto', 1, (32,), 3)
)
def test_DCNMix(embedding_size, cross_num, hidden_size, sparse_feature_num):
@@ -18,7 +18,7 @@ def test_DCNMix(embedding_size, cross_num, hidden_size, sparse_feature_num):
sample_size, sparse_feature_num=sparse_feature_num, dense_feature_num=sparse_feature_num)
model = DCNMix(linear_feature_columns=feature_columns, dnn_feature_columns=feature_columns,
- cross_num=cross_num, dnn_hidden_units=hidden_size, dnn_dropout=0.5, device=get_device())
+ cross_num=cross_num, dnn_hidden_units=hidden_size, dnn_dropout=0.5, device=get_device())
check_model(model, model_name, x, y)
diff --git a/tests/models/DCN_test.py b/tests/models/DCN_test.py
index 8be0ef00..bffc2ac1 100644
--- a/tests/models/DCN_test.py
+++ b/tests/models/DCN_test.py
@@ -7,7 +7,7 @@
@pytest.mark.parametrize(
'embedding_size,cross_num,hidden_size,sparse_feature_num,cross_parameterization',
- [(8, 0, (32,), 2, 'vector'), (8, 0, (32,), 2, 'matrix'),
+ [(8, 2, (32,), 2, 'vector'), (8, 1, (32,), 2, 'matrix'),
] # ('auto', 1, (32,), 3) , ('auto', 1, (), 1), ('auto', 1, (32,), 3)
)
def test_DCN(embedding_size, cross_num, hidden_size, sparse_feature_num, cross_parameterization):
diff --git a/tests/models/PNN_test.py b/tests/models/PNN_test.py
index 0e419a6c..8afc9efe 100644
--- a/tests/models/PNN_test.py
+++ b/tests/models/PNN_test.py
@@ -5,17 +5,18 @@
@pytest.mark.parametrize(
- 'use_inner, use_outter,sparse_feature_num',
- [(True, True, 2), (True, False, 2), (False, True, 3), (False, False, 1)
+ 'use_inner, use_outter, kernel_type, sparse_feature_num',
+ [(True, True, 'mat', 2), (True, False, 'mat', 2), (False, True, 'vec', 3), (False, True, 'num', 3),
+ (False, False, 'mat', 1)
]
)
-def test_PNN(use_inner, use_outter, sparse_feature_num):
+def test_PNN(use_inner, use_outter, kernel_type, sparse_feature_num):
model_name = "PNN"
sample_size = SAMPLE_SIZE
x, y, feature_columns = get_test_data(sample_size, sparse_feature_num=sparse_feature_num,
dense_feature_num=sparse_feature_num)
model = PNN(feature_columns, dnn_hidden_units=[32, 32], dnn_dropout=0.5, use_inner=use_inner,
- use_outter=use_outter, device=get_device())
+ use_outter=use_outter, kernel_type=kernel_type, device=get_device())
check_model(model, model_name, x, y)
diff --git a/tests/models/WDL_test.py b/tests/models/WDL_test.py
index bea91a8d..c06be14e 100644
--- a/tests/models/WDL_test.py
+++ b/tests/models/WDL_test.py
@@ -16,7 +16,7 @@ def test_WDL(sparse_feature_num, dense_feature_num):
x, y, feature_columns = get_test_data(
sample_size, sparse_feature_num=sparse_feature_num, dense_feature_num=dense_feature_num)
- model = WDL(feature_columns, feature_columns,
+ model = WDL(feature_columns, feature_columns, dnn_activation='prelu',
dnn_hidden_units=[32, 32], dnn_dropout=0.5, device=get_device())
check_model(model, model_name, x, y)
|
