neuralmonkey.decoders.classifier module¶
-
class
neuralmonkey.decoders.classifier.
Classifier
(name: str, encoders: List[neuralmonkey.model.stateful.Stateful], vocabulary: neuralmonkey.vocabulary.Vocabulary, data_id: str, layers: List[int], activation_fn: Callable[[tensorflow.python.framework.ops.Tensor], tensorflow.python.framework.ops.Tensor] = <function relu>, dropout_keep_prob: float = 0.5, reuse: neuralmonkey.model.model_part.ModelPart = None, save_checkpoint: str = None, load_checkpoint: str = None, initializers: List[Tuple[str, Callable]] = None) → None¶ Bases:
neuralmonkey.model.model_part.ModelPart
A simple MLP classifier over encoders.
The API pretends it is an RNN decoder which always generates a sequence of length exactly one.
-
__init__
(name: str, encoders: List[neuralmonkey.model.stateful.Stateful], vocabulary: neuralmonkey.vocabulary.Vocabulary, data_id: str, layers: List[int], activation_fn: Callable[[tensorflow.python.framework.ops.Tensor], tensorflow.python.framework.ops.Tensor] = <function relu>, dropout_keep_prob: float = 0.5, reuse: neuralmonkey.model.model_part.ModelPart = None, save_checkpoint: str = None, load_checkpoint: str = None, initializers: List[Tuple[str, Callable]] = None) → None¶ Construct a new instance of the sequence classifier.
Parameters: - name – Name of the decoder. Should be unique accross all Neural Monkey objects
- encoders – Input encoders of the decoder
- vocabulary – Target vocabulary
- data_id – Target data series
- layers –
List defining structure of the NN. Ini example: layers=[100,20,5] ;creates classifier with hidden layers of
size 100, 20, 5 and one output layer depending on the size of vocabulary - activation_fn – activation function used on the output of each hidden layer.
- dropout_keep_prob – Probability of keeping a value during dropout
-
cost
¶
-
decoded
¶
-
decoded_logits
¶
-
decoded_seq
¶
-
feed_dict
(dataset: neuralmonkey.dataset.Dataset, train: bool = False) → Dict[tensorflow.python.framework.ops.Tensor, Any]¶ Return a feed dictionary for the given feedable object.
Parameters: - dataset – A dataset instance from which to get the data.
- train – Boolean indicating whether the model runs in training mode.
Returns: A FeedDict dictionary object.
-
loss_with_decoded_ins
¶
-
loss_with_gt_ins
¶
-
runtime_logprobs
¶
-
runtime_loss
¶
-
train_loss
¶
-