FAQ¶

1. Save or load weights/models¶

To save/load weights,you can write codes just like any other keras models.

model = DeepFM()
model.save_weights('DeepFM_w.h5')
model.load_weights('DeepFM_w.h5')

To save/load models,just a little different.

from tensorflow.keras.models import save_model, load_model
model = DeepFM()
save_model(model, 'DeepFM.h5')# save_model, same as before

from deepctr.layers import custom_objects
model = load_model('DeepFM.h5',custom_objects)# load_model,just add a parameter

2. Set learning rate and use earlystopping¶

You can use any models in DeepCTR like a keras model object. Here is a example of how to set learning rate and earlystopping:

import deepctr
from tensorflow.keras.optimizers import Adam, Adagrad
from tensorflow.keras.callbacks import EarlyStopping

model = deepctr.models.DeepFM(linear_feature_columns,dnn_feature_columns)
model.compile(Adagrad(0.1024),'binary_crossentropy',metrics=['binary_crossentropy'])

es = EarlyStopping(monitor='val_binary_crossentropy')
history = model.fit(model_input, data[target].values,batch_size=256, epochs=10, verbose=2, validation_split=0.2,callbacks=[es] )

If you are using Estimator models, you can set learning rate like:

from deepctr.estimator import DeepFMEstimator
import tensorflow as tf

model = DeepFMEstimator(linear_feature_columns, dnn_feature_columns, task='binary', 
                            linear_optimizer=tf.train.FtrlOptimizer(0.05), dnn_optimizer=tf.train.AdagradOptimizer(0.1)
                            )

3. Get the attentional weights of feature interactions in AFM¶

First,make sure that you have install the latest version of deepctr.

Then,use the following code,the attentional_weights[:,i,0] is the feature_interactions[i]’s attentional weight of all samples.

import itertools
import deepctr
from deepctr.models import AFM
from deepctr.feature_column import get_feature_names
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Lambda

model = AFM(linear_feature_columns,dnn_feature_columns)
model.fit(model_input,target)

afmlayer = model.layers[-3]
afm_weight_model = Model(model.input,outputs=Lambda(lambda x:afmlayer.normalized_att_score)(model.input))
attentional_weights = afm_weight_model.predict(model_input,batch_size=4096)

feature_names = get_feature_names(dnn_feature_columns)
feature_interactions = list(itertools.combinations(feature_names ,2))

4. How to extract the embedding vectors in deepfm?¶

feature_columns = [SparseFeat('user_id',120,),SparseFeat('item_id',60,),SparseFeat('cate_id',60,)]

def get_embedding_weights(dnn_feature_columns,model):
    embedding_dict = {}
    for fc in dnn_feature_columns:
        if hasattr(fc,'embedding_name'):
            if fc.embedding_name is not None:
                name = fc.embedding_name
            else:
                name = fc.name
            embedding_dict[name] = model.get_layer("sparse_emb_"+name).get_weights()[0]
    return embedding_dict
    
embedding_dict = get_embedding_weights(feature_columns,model)

user_id_emb = embedding_dict['user_id']
item_id_emb = embedding_dict['item_id']

5. How to add a long dense feature vector as a input to the model?¶

from deepctr.models import DeepFM
from deepctr.feature_column import SparseFeat, DenseFeat,get_feature_names
import numpy as np

feature_columns = [SparseFeat('user_id',120,),SparseFeat('item_id',60,),DenseFeat("pic_vec",5)]
fixlen_feature_names = get_feature_names(feature_columns)

user_id = np.array([[1],[0],[1]])
item_id = np.array([[30],[20],[10]])
pic_vec = np.array([[0.1,0.5,0.4,0.3,0.2],[0.1,0.5,0.4,0.3,0.2],[0.1,0.5,0.4,0.3,0.2]])
label = np.array([1,0,1])

model_input = {'user_id':user_id,'item_id':item_id,'pic_vec':pic_vec}

model = DeepFM(feature_columns,feature_columns)
model.compile('adagrad','binary_crossentropy')
model.fit(model_input,label)

6. How to use pretrained weights to initialize embedding weights and frozen embedding weights?¶

Use tf.initializers.identity() to set the embeddings_initializer of SparseFeat,and set trainable=False to frozen embedding weights.

import numpy as np
import tensorflow as tf
from deepctr.models import DeepFM
from deepctr.feature_column import SparseFeat,get_feature_names

pretrained_item_weights = np.random.randn(60,4)
pretrained_weights_initializer = tf.initializers.constant(pretrained_item_weights)

feature_columns = [SparseFeat('user_id',120,),SparseFeat('item_id',60,embedding_dim=4,embeddings_initializer=pretrained_weights_initializer,trainable=False)]
fixlen_feature_names = get_feature_names(feature_columns)

user_id = np.array([[1],[0],[1]])
item_id = np.array([[30],[20],[10]])
label = np.array([1,0,1])

model_input = {'user_id':user_id,'item_id':item_id,}

model = DeepFM(feature_columns,feature_columns)
model.compile('adagrad','binary_crossentropy')
model.fit(model_input,label)

7. How to run the demo with GPU ?¶

Install the TensorFlow build recommended for your CUDA, cuDNN, and platform combination, then install deepctr.

8. How to avoid TensorFlow, Keras, h5py, or NumPy compatibility errors?¶

Install TensorFlow separately before installing DeepCTR. Pick the TensorFlow release according to your Python version, CPU/GPU environment, and platform.

$ pip install tensorflow
$ pip install deepctr

For Python >=3.9, DeepCTR uses h5py>=3.7.0, so newer h5py releases such as 3.9+ and 3.12+ are allowed. If TensorFlow reports a NumPy conflict, follow the TensorFlow requirement for the TensorFlow release you installed, for example using numpy<2 when required by TensorFlow.

Use public tensorflow.keras imports in your own code:

from tensorflow.keras.models import load_model
from tensorflow.keras.optimizers import Adam

Avoid mixing tensorflow.python.keras with tensorflow.keras. tensorflow.python.* is private TensorFlow API and can break serialization, optimizer loading, or metric loading across TensorFlow versions.

9. How to run the demo with multiple GPUs¶

you can use multiple gpus with tensorflow version higher than 1.4,see run_classification_criteo_multi_gpu.py