加載保存后的預測錯誤 Keras model

Question

我想保存，加載我的 model，並將其用於預測。

審查的答案

• keras > 加載保存后總是相同的預測值 model
• 在 keras 中加載 model 后的不同預測
• 保存/加載 Tensorflow Keras model 僅用於預測
• (tf.)keras 加載保存的 model 權重和可訓練的詞嵌入
• 加載保存后的ValueError keras model
• 找不到匹配的 function 以調用從 SavedModel 加載
• https://github.com/tensorflow/tensorflow/issues/37973 --> https://www.tensorflow.org/guide <-/keras

MODEL

### ML MODEL
EMBEDDING_SIZE = 50


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)
    )

##### LOG HISTORY
from keras.callbacks import CSVLogger

csv_logger = CSVLogger('save_training_history/' + 'history' + '_rows_of_data_' + str(number_of_table_rows) + '_time_' + str_date_time + 'training.log'
                       , separator=','
                       , append=False)


#### TRAIN
history = model.fit(
    x=x_train,
    y=y_train,
    batch_size=64,
    epochs=10,
    verbose=1,
    validation_data=(x_val, y_val),
    callbacks=[csv_logger]
)

節省

model.save('saved_model/' + 'model' + '_rows_of_data_' + str(number_of_table_rows) + '_' + str_date_time )

加載

#Load 
reconstructed_model = keras.models.load_model("my_model")

# Load: I have tried this one as well but it gave the same error
loaded_model = tf.keras.models.load_model('/tmp/model')

預測

movides_watched_by_user = df_big[df_big.userid == user_id]

'''GET movies NOT owned/played by users'''
movies_not_watched1 = movie_df[~movie_df["movieid"].isin(movies_watched_by_user.movieid.values)]["movieid"]

'''NOT df Series to list'''
movies_not_watched2 = movies_not_watched1.copy()
movies_not_watched3 = movies_not_watched2.values.tolist()

'''(NOT list) Double wrap in to a list of lists BY EXTERANL FUNCTION'''
movies_not_watched = extractDigits(movies_not_watched3)


'''UNIFY 2 rows Transpose matrix'''
user_movie_array = np.hstack((
                        [[user_id]] * len(movies_not_watched), movies_not_watched
                    ))

'''Predict probability sores + user_movie_array -1D'''
ratings = model.predict(user_movie_array).flatten()

錯誤

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-67-7f843e09ac34> in <module>
     54 
     55         '''Predict probability sores + user_movie_array -1D'''
---> 56         ratings = model.predict(user_movie_array).flatten()
     57         #print(ratings.shape)
     58 

~/anaconda3/envs/tfkgpu/lib/python3.7/site-packages/keras/utils/traceback_utils.py in error_handler(*args, **kwargs)
     65     except Exception as e:  # pylint: disable=broad-except
     66       filtered_tb = _process_traceback_frames(e.__traceback__)
---> 67       raise e.with_traceback(filtered_tb) from None
     68     finally:
     69       del filtered_tb

~/anaconda3/envs/tfkgpu/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py in autograph_handler(*args, **kwargs)
   1145           except Exception as e:  # pylint:disable=broad-except
   1146             if hasattr(e, "ag_error_metadata"):
-> 1147               raise e.ag_error_metadata.to_exception(e)
   1148             else:
   1149               raise

ValueError: in user code:

    File "/home/ubuntu/anaconda3/envs/tfkgpu/lib/python3.7/site-packages/keras/engine/training.py", line 1801, in predict_function  *
        return step_function(self, iterator)
    File "/home/ubuntu/anaconda3/envs/tfkgpu/lib/python3.7/site-packages/keras/engine/training.py", line 1790, in step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "/home/ubuntu/anaconda3/envs/tfkgpu/lib/python3.7/site-packages/keras/engine/training.py", line 1783, in run_step  **
        outputs = model.predict_step(data)
    File "/home/ubuntu/anaconda3/envs/tfkgpu/lib/python3.7/site-packages/keras/engine/training.py", line 1751, in predict_step
        return self(x, training=False)
    File "/home/ubuntu/anaconda3/envs/tfkgpu/lib/python3.7/site-packages/keras/utils/traceback_utils.py", line 67, in error_handler
        raise e.with_traceback(filtered_tb) from None

    ValueError: Exception encountered when calling layer "recommender_net" (type RecommenderNet).
    
    Could not find matching concrete function to call loaded from the SavedModel. Got:
      Positional arguments (1 total):
        * Tensor("inputs:0", shape=(None, 2), dtype=int64)
      Keyword arguments: {}
    
     Expected these arguments to match one of the following 2 option(s):
    
    Option 1:
      Positional arguments (1 total):
        * TensorSpec(shape=(None, 2), dtype=tf.int32, name='inputs')
      Keyword arguments: {}
    
    Option 2:
      Positional arguments (1 total):
        * TensorSpec(shape=(None, 2), dtype=tf.int32, name='input_1')
      Keyword arguments: {}
    
    Call arguments received:
      • args=('tf.Tensor(shape=(None, 2), dtype=int64)',)
      • kwargs=<class 'inspect._empty'>

Answer 1

您有一個自定義 model 架構，需要在加載時重新初始化。 這可以通過SavedModel完成，但您需要指定一個get_config和from_config ，它們具有初始化 model 的值。

或者，您可以自己重新定義 model 並使用save_weights和load_weights 。

例如，請參閱文檔。

在您的情況下，您需要將其添加到您的RecommenderNet class：

def get_config(self):
    return {
        "num_users": self.num_users,
        "num_movies": self.num_movies,
        "embedding_size": self.embedding_size,
    }

@classmethod
def from_config(cls, config):
    return cls(**config)