python - 如何保存 keras 推荐网络模型?
问题描述
从 Embedding RecommenderNet 模型构建模型后,如何保存,文档链接为https://keras.io/examples/structured_data/collaborative_filtering_movielens/
class RecommenderNet(keras.Model):
def __init__(self, num_users, num_movies, embedding_size, **kwargs):
super(RecommenderNet, self).__init__(**kwargs)
self.num_users = num_users
self.num_movies = num_movies
self.embedding_size = embedding_size
self.user_embedding = layers.Embedding(
num_users,
embedding_size,
embeddings_initializer="he_normal",
embeddings_regularizer=keras.regularizers.l2(1e-6),
)
self.user_bias = layers.Embedding(num_users, 1)
self.movie_embedding = layers.Embedding(
num_movies,
embedding_size,
embeddings_initializer="he_normal",
embeddings_regularizer=keras.regularizers.l2(1e-6),
)
self.movie_bias = layers.Embedding(num_movies, 1)
def call(self, inputs):
user_vector = self.user_embedding(inputs[:, 0])
user_bias = self.user_bias(inputs[:, 0])
movie_vector = self.movie_embedding(inputs[:, 1])
movie_bias = self.movie_bias(inputs[:, 1])
dot_user_movie = tf.tensordot(user_vector, movie_vector, 2)
# Add all the components (including bias)
x = dot_user_movie + user_bias + movie_bias
# The sigmoid activation forces the rating to between 0 and 1
return tf.nn.sigmoid(x)
model = RecommenderNet(num_users, num_movies, EMBEDDING_SIZE)
model.compile(
loss=tf.keras.losses.BinaryCrossentropy(), optimizer=keras.optimizers.Adam(lr=0.001)
)
history = model.fit(
x=x_train,
y=y_train,
batch_size=64,
epochs=5,
verbose=1,
validation_data=(x_val, y_val),
)
试过这些
model.save('model.h5py')
tf.keras.models.save_model(model, overwrite=True, include_optimizer=True, save_format='h5')
两次投掷
NotImplementedError: Saving the model to HDF5 format requires the model to be a Functional model or
a Sequential model.It does not work for subclassed models, because such models are defined via the body of
a Python method, which isn't safely serializable. Consider saving to the Tensorflow SavedModel
format (by setting save_format="tf") or using `save_weights`.
模型类型主要是.RecommenderNet
解决方案
实际上重新创建模型
keras.models.load_model('path_to_my_model')
对我不起作用首先我们必须从构建的模型中保存权重
model.save_weights('model_weights', save_format='tf')
然后我们必须为子类 Model 启动一个新实例,然后使用构建模型的一条记录和 load_weights 编译和 train_on_batch
loaded_model = RecommenderNet(num_users, num_movies, EMBEDDING_SIZE)
loaded_model.compile(loss=tf.keras.losses.BinaryCrossentropy(), optimizer=keras.optimizers.Adam(lr=0.001))
loaded_model.train_on_batch(x_train[:1], y_train[:1])
loaded_model.load_weights('model_weights')
这在 TensorFlow==2.2.0 中完美运行
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