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Я использую нейронную сеть с двумя башнями для рекомендательной системы. Способ, которым я интерпретирую журнал ошибок, заключается в том, что в строке 32 col 0 набора данных train он находит значение 329416640, которое не вписывается в диапазон [0, 853515). Я абсолютно не представляю, чему соответствует это значение 329416640.

Я использую следующий набор данных.

набор данных был разделен на поезд и тестовое разделение 80/20. Форма данных о поездах: (4728919, 3) Форма данных испытаний: (1182230, 3)

Затем я определяю длину своего вокаба следующим образом:

 client_vocab_len = (int(client_data.CLI_ID.nunique())) libelle_vocab_len = (int(items_data.prod_id.nunique())) print(client_vocab_len) # = 853514 print(libelle_vocab_len) # = 1484   

Моя архитектура модели выглядит так:

 #user input network input_clients = tf.layers.Input(shape=[1]) dense = tf.layers.Dense(32, activation="relu") yy = dense(input_clients) embed_clients = tf.layers.Embedding(client_vocab_len 1, 15)(yy) clients_out = tf.layers.Flatten()(embed_clients)  #product input network input_products = tf.layers.Input(shape=[1]) dense = tf.layers.Dense(32, activation="relu") xx = dense(input_products) embed_products = tf.layers.Embedding(libelle_vocab_len 1, 15)(xx) products_out = tf.layers.Flatten()(embed_products)   conc_layer = tf.layers.Concatenate()([clients_out, products_out]) x = tf.layers.Dense(128, activation='relu')(conc_layer) #x = tf.layers.Dropout(0.3)(x) x = tf.layers.Dense(128, activation='relu')(x) x_out = x = tf.layers.Dense(1, activation='relu')(x) model = tf.Model([input_clients, input_products], x_out)  

когда я пытаюсь обучить свою модель, я получаю следующий журнал ошибок:

 # Create a callback to stop the model if overfits early_stop = EarlyStopping(monitor='val_loss',patience=3)  lr_reduce = ReduceLROnPlateau(monitor='val_loss',  factor=0.75,  patience=2,  verbose=2,  mode='auto')   hist = model.fit([Xtrain.CLI_ID, Xtrain.prod_id], Xtrain.QTY,   batch_size=128,   epochs=15,   verbose=1,  validation_data=([Xtest.CLI_ID, Xtest.prod_id], Xtest.QTY),  callbacks=[early_stop,lr_reduce,PlotLossesKeras()])    --------------------------------------------------------------------------- InvalidArgumentError Traceback (most recent call last) lt;ipython-input-166-d767717c6437gt; in lt;modulegt;  8   9  ---gt; 10 hist = model.fit([Xtrain.CLI_ID, Xtrain.prod_id], Xtrain.QTY,   11 batch_size=128,  12 epochs=15,  ~/opt/anaconda3/lib/python3.8/site-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)  1182 _r=1):  1183 callbacks.on_train_batch_begin(step) -gt; 1184 tmp_logs = self.train_function(iterator)  1185 if data_handler.should_sync:  1186 context.async_wait()  ~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)  883   884 with OptionalXlaContext(self._jit_compile): --gt; 885 result = self._call(*args, **kwds)  886   887 new_tracing_count = self.experimental_get_tracing_count()  ~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)  948 # Lifting succeeded, so variables are initialized and we can run the  949 # stateless function. --gt; 950 return self._stateless_fn(*args, **kwds)  951 else:  952 _, _, _, filtered_flat_args =   ~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in __call__(self, *args, **kwargs)  3037 (graph_function,  3038 filtered_flat_args) = self._maybe_define_function(args, kwargs) -gt; 3039 return graph_function._call_flat(  3040 filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access  3041   ~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _call_flat(self, args, captured_inputs, cancellation_manager)  1961 and executing_eagerly):  1962 # No tape is watching; skip to running the function. -gt; 1963 return self._build_call_outputs(self._inference_function.call(  1964 ctx, args, cancellation_manager=cancellation_manager))  1965 forward_backward = self._select_forward_and_backward_functions(  ~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in call(self, ctx, args, cancellation_manager)  589 with _InterpolateFunctionError(self):  590 if cancellation_manager is None: --gt; 591 outputs = execute.execute(  592 str(self.signature.name),  593 num_outputs=self._num_outputs,  ~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)  57 try:  58 ctx.ensure_initialized() ---gt; 59 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,  60 inputs, attrs, num_outputs)  61 except core._NotOkStatusException as e:  InvalidArgumentError: indices[32,0] = 329416640 is not in [0, 853515)  [[node model_27/embedding_55/embedding_lookup (defined at lt;ipython-input-166-d767717c6437gt;:10) ]] [Op:__inference_train_function_28184]  Errors may have originated from an input operation. Input Source operations connected to node model_27/embedding_55/embedding_lookup:  model_27/embedding_55/embedding_lookup/27907 (defined at /Users/jonathankhalifa/opt/anaconda3/lib/python3.8/contextlib.py:113)  Function call stack: train_function