[英]How to save keras recommenderNet model?
从 Embedding RecommenderNet model 构建 model 后,如何保存它,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')
两个都扔
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`.
model类型为主.RecommenderNet
因此,正如错误所说,您可以使用save_format="tf"
因为您的 model 不是Functional
或Sequential
:
model.save('model.py')
tf.keras.models.save_model(model, overwrite=True, include_optimizer=True, save_format='tf')
同样如 文档中所示,您可以使用:
keras_model_path = "/tmp/keras_save"
model.save(keras_model_path)
实际上重新创建 model
keras.models.load_model('path_to_my_model')
对我不起作用首先我们必须从内置的 model 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,然后用一条记录和构建的 model 的 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
我将 tensorflow 1.14.0 升级到 2.2.0,save_model 有效
tf.keras.models.save_model(model,'./saved_model')
但是使用 load_model,在加载 model 之后,同时进行预测
loaded_model = tf.keras.models.load_model('./saved_model')
我得到值错误
rates = loaded_model.predict(user_product_array).flatten()
ValueError: Python inputs incompatible with input_signature:
inputs: (
Tensor("IteratorGetNext:0", shape=(None, 2), dtype=int32))
input_signature: (
TensorSpec(shape=(None, 2), dtype=tf.int64, name='input_1'))
我可以知道这里的问题吗?
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