FAQ¶

1. Save or load weights/models¶

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

model = YoutubeDNN()
model.save_weights('YoutubeDNN_w.h5')
model.load_weights('YoutubeDNN_w.h5')

To save/load models,just a little different.

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

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

2. Set learning rate and use earlystopping¶

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

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

model = deepmatch.models.FM(user_feature_columns,item_feature_columns)
model.compile(Adagrad(0.01),'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] )

3. How to run the demo with GPU ?¶

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

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

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

$ pip install tensorflow
$ pip install deepmatch

For Python >=3.9, DeepMatch and its dependencies allow modern h5py releases with h5py>=3.7.0. 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.