И вот здесь я называю оценщик и обучение
word2vecEstimator = tf.estimator.Estimator(
model_fn=my_model,
params={
'batch_size': 16,
'embedding_size': 10,
'num_inputs': 3,
'num_sampled': 128,
'batch_size': 16
})
word2vecEstimator.train(
input_fn=generate_batch,
steps=10)
И это сообщение об ошибке, которое я получаю, когда вызываю обучение оценщика:
INFO:tensorflow:Calling model_fn.
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-22-955f44867ee5> in <module>()
1 word2vecEstimator.train(
2 input_fn=generate_batch,
----> 3 steps=10)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/estimator.py in train(self, input_fn, hooks, steps, max_steps, saving_listeners)
352
353 saving_listeners = _check_listeners_type(saving_listeners)
--> 354 loss = self._train_model(input_fn, hooks, saving_listeners)
355 logging.info('Loss for final step: %s.', loss)
356 return self
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/estimator.py in _train_model(self, input_fn, hooks, saving_listeners)
1205 return self._train_model_distributed(input_fn, hooks, saving_listeners)
1206 else:
-> 1207 return self._train_model_default(input_fn, hooks, saving_listeners)
1208
1209 def _train_model_default(self, input_fn, hooks, saving_listeners):
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/estimator.py in _train_model_default(self, input_fn, hooks, saving_listeners)
1235 worker_hooks.extend(input_hooks)
1236 estimator_spec = self._call_model_fn(
-> 1237 features, labels, model_fn_lib.ModeKeys.TRAIN, self.config)
1238 global_step_tensor = training_util.get_global_step(g)
1239 return self._train_with_estimator_spec(estimator_spec, worker_hooks,
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/estimator.py in _call_model_fn(self, features, labels, mode, config)
1193
1194 logging.info('Calling model_fn.')
-> 1195 model_fn_results = self._model_fn(features=features, **kwargs)
1196 logging.info('Done calling model_fn.')
1197
<ipython-input-20-9d389437162a> in my_model(features, labels, mode, params)
33 inputs=embed,
34 num_sampled=num_sampled,
---> 35 num_classes=vocabulary_size))
36
37 # Add the loss value as a scalar to summary.
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/nn_impl.py in nce_loss(weights, biases, labels, inputs, num_sampled, num_classes, num_true, sampled_values, remove_accidental_hits, partition_strategy, name)
1246 remove_accidental_hits=remove_accidental_hits,
1247 partition_strategy=partition_strategy,
-> 1248 name=name)
1249 sampled_losses = sigmoid_cross_entropy_with_logits(
1250 labels=labels, logits=logits, name="sampled_losses")
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/nn_impl.py in _compute_sampled_logits(weights, biases, labels, inputs, num_sampled, num_classes, num_true, sampled_values, subtract_log_q, remove_accidental_hits, partition_strategy, name, seed)
1029 with ops.name_scope(name, "compute_sampled_logits",
1030 weights + [biases, inputs, labels]):
-> 1031 if labels.dtype != dtypes.int64:
1032 labels = math_ops.cast(labels, dtypes.int64)
1033 labels_flat = array_ops.reshape(labels, [-1])
TypeError: data type not understood
Изменить: по запросу вот как выглядит типичный вывод для input_fn
print(generate_batch(batch_size=8, num_skips=2, skip_window=1))
(array([3081, 3081, 12, 12, 6, 6, 195, 195], dtype=int32), array([[5234],
[ 12],
[ 6],
[3081],
[ 12],
[ 195],
[ 6],
[ 2]], dtype=int32))
