neuralmonkey.encoders package¶
Submodules¶
neuralmonkey.encoders.cnn_encoder module¶
CNN for image processing.
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class
neuralmonkey.encoders.cnn_encoder.
CNNEncoder
(name: str, data_id: str, convolutions: typing.List[typing.Tuple[int, int, typing.Union[int, NoneType]]], image_height: int, image_width: int, pixel_dim: int, fully_connected: typing.Union[typing.List[int], NoneType] = None, dropout_keep_prob: float = 0.5, save_checkpoint: typing.Union[str, NoneType] = None, load_checkpoint: typing.Union[str, NoneType] = None) → None¶ Bases:
neuralmonkey.model.model_part.ModelPart
,neuralmonkey.model.stateful.SpatialStatefulWithOutput
An image encoder.
It projects the input image through a serie of convolutioal operations. The projected image is vertically cut and fed to stacked RNN layers which encode the image into a single vector.
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feed_dict
(dataset: neuralmonkey.dataset.Dataset, train: bool = False) → typing.Dict[tensorflow.python.framework.ops.Tensor, typing.Any]¶
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image_input
¶
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image_processing_layers
¶ Do all convolutions and return the last conditional map.
Applies convolutions on the input tensor with optional max pooling. All the intermediate layers are stored in the image_processing_layers attribute. There is not dropout between the convolutional layers, by default the activation function is ReLU.
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output
¶ Output vector of the CNN.
If there are specified some fully connected layers, there are applied on top of the last convolutional map. Dropout is applied between all layers, default activation function is ReLU. There are only projection layers, no softmax is applied.
If there is fully_connected layer specified, average-pooled last convolutional map is used as a vector output.
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spatial_mask
¶
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spatial_states
¶
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train_mode
¶
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neuralmonkey.encoders.facebook_conv module¶
From the paper Convolutional Sequence to Sequence Learning.
http://arxiv.org/abs/1705.03122
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class
neuralmonkey.encoders.facebook_conv.
SentenceEncoder
(name: str, input_sequence: neuralmonkey.model.sequence.EmbeddedSequence, conv_features: int, encoder_layers: int, kernel_width: int = 5, dropout_keep_prob: float = 1.0, save_checkpoint: str = None, load_checkpoint: str = None) → None¶ Bases:
neuralmonkey.model.model_part.ModelPart
,neuralmonkey.model.stateful.TemporalStatefulWithOutput
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feed_dict
(dataset: neuralmonkey.dataset.Dataset, train: bool = False) → typing.Dict[tensorflow.python.framework.ops.Tensor, typing.Any]¶
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order_embeddings
¶
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ordered_embedded_inputs
¶
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output
¶
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temporal_mask
¶
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temporal_states
¶
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train_mode
¶
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neuralmonkey.encoders.imagenet_encoder module¶
Pre-trained ImageNet networks.
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class
neuralmonkey.encoders.imagenet_encoder.
ImageNet
(name: str, data_id: str, network_type: str, attention_layer: typing.Union[str, NoneType] = None, fine_tune: bool = False, encoded_layer: typing.Union[str, NoneType] = None, load_checkpoint: typing.Union[str, NoneType] = None, save_checkpoint: typing.Union[str, NoneType] = None) → None¶ Bases:
neuralmonkey.model.model_part.ModelPart
,neuralmonkey.model.stateful.SpatialStatefulWithOutput
Pre-trained ImageNet network.
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HEIGHT
= 224¶
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WIDTH
= 224¶
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feed_dict
(dataset: neuralmonkey.dataset.Dataset, train: bool = False) → typing.Dict[tensorflow.python.framework.ops.Tensor, typing.Any]¶
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input_image
¶
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output
¶
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spatial_mask
¶
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spatial_states
¶
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neuralmonkey.encoders.numpy_encoder module¶
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class
neuralmonkey.encoders.numpy_encoder.
PostCNNImageEncoder
(name: str, input_shape: typing.List[int], output_shape: int, data_id: str, save_checkpoint: typing.Union[str, NoneType] = None, load_checkpoint: typing.Union[str, NoneType] = None) → None¶ Bases:
neuralmonkey.model.model_part.ModelPart
,neuralmonkey.model.stateful.SpatialStatefulWithOutput
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feed_dict
(dataset: neuralmonkey.dataset.Dataset, train: bool = False) → typing.Dict[tensorflow.python.framework.ops.Tensor, typing.Any]¶
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output
¶
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spatial_mask
¶
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spatial_states
¶
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class
neuralmonkey.encoders.numpy_encoder.
VectorEncoder
(name: str, dimension: int, data_id: str, output_shape: int = None, save_checkpoint: str = None, load_checkpoint: str = None) → None¶ Bases:
neuralmonkey.model.model_part.ModelPart
,neuralmonkey.model.stateful.Stateful
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feed_dict
(dataset: neuralmonkey.dataset.Dataset, train: bool = False) → typing.Dict[tensorflow.python.framework.ops.Tensor, typing.Any]¶
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output
¶
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neuralmonkey.encoders.raw_rnn_encoder module¶
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class
neuralmonkey.encoders.raw_rnn_encoder.
RNNSpec
(size, direction, cell_type)¶ Bases:
tuple
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cell_type
¶ Alias for field number 2
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direction
¶ Alias for field number 1
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size
¶ Alias for field number 0
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class
neuralmonkey.encoders.raw_rnn_encoder.
RawRNNEncoder
(name: str, data_id: str, input_size: int, rnn_layers: typing.List[typing.Union[typing.Tuple[int], typing.Tuple[int, str], typing.Tuple[int, str, str]]], max_input_len: typing.Union[int, NoneType] = None, dropout_keep_prob: float = 1.0, save_checkpoint: typing.Union[str, NoneType] = None, load_checkpoint: typing.Union[str, NoneType] = None) → None¶ Bases:
neuralmonkey.model.model_part.ModelPart
,neuralmonkey.model.stateful.TemporalStatefulWithOutput
A raw RNN encoder that gets input as a tensor.
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feed_dict
(dataset: neuralmonkey.dataset.Dataset, train: bool = False) → typing.Dict[tensorflow.python.framework.ops.Tensor, typing.Any]¶ Populate the feed dictionary with the encoder inputs.
Parameters: - dataset – The dataset to use
- train – Boolean flag telling whether it is training time
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output
¶
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temporal_mask
¶
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temporal_states
¶
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neuralmonkey.encoders.recurrent module¶
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class
neuralmonkey.encoders.recurrent.
FactoredEncoder
(name: str, vocabularies: typing.List[neuralmonkey.vocabulary.Vocabulary], data_ids: typing.List[str], embedding_sizes: typing.List[int], rnn_size: int, max_input_len: int = None, dropout_keep_prob: float = 1.0, rnn_cell: str = 'GRU', output_size: int = None, save_checkpoint: str = None, load_checkpoint: str = None) → None¶
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class
neuralmonkey.encoders.recurrent.
RecurrentEncoder
(name: str, input_sequence: neuralmonkey.model.sequence.Sequence, rnn_size: int, dropout_keep_prob: float = 1.0, rnn_cell: str = 'GRU', output_size: int = None, save_checkpoint: str = None, load_checkpoint: str = None) → None¶ Bases:
neuralmonkey.model.model_part.ModelPart
,neuralmonkey.model.stateful.TemporalStatefulWithOutput
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bidirectional_rnn
¶
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feed_dict
(dataset: neuralmonkey.dataset.Dataset, train: bool = False) → typing.Dict[tensorflow.python.framework.ops.Tensor, typing.Any]¶
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output
¶
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states
¶
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states_mask
¶
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temporal_mask
¶
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temporal_states
¶
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train_mode
¶
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class
neuralmonkey.encoders.recurrent.
SentenceEncoder
(name: str, vocabulary: neuralmonkey.vocabulary.Vocabulary, data_id: str, embedding_size: int, rnn_size: int, max_input_len: int = None, dropout_keep_prob: float = 1.0, rnn_cell: str = 'GRU', output_size: int = None, save_checkpoint: str = None, load_checkpoint: str = None) → None¶
neuralmonkey.encoders.sentence_cnn_encoder module¶
Encoder for sentences withou explicit segmentation.
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class
neuralmonkey.encoders.sentence_cnn_encoder.
SentenceCNNEncoder
(name: str, input_sequence: neuralmonkey.model.sequence.Sequence, segment_size: int, highway_depth: int, rnn_size: int, filters: typing.List[typing.Tuple[int, int]], dropout_keep_prob: float = 1.0, use_noisy_activations: bool = False, save_checkpoint: typing.Union[str, NoneType] = None, load_checkpoint: typing.Union[str, NoneType] = None) → None¶ Bases:
neuralmonkey.model.model_part.ModelPart
,neuralmonkey.model.stateful.TemporalStatefulWithOutput
Recurrent over Convolutional Encoder.
Encoder processing a sentence using a CNN then running a bidirectional RNN on the result.
Based on: Jason Lee, Kyunghyun Cho, Thomas Hofmann: Fully Character-Level Neural Machine Translation without Explicit Segmentation.
See https://arxiv.org/pdf/1610.03017.pdf
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bidirectional_rnn
¶
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cnn_encoded
¶ 1D convolution with max-pool that processing characters.
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feed_dict
(dataset: neuralmonkey.dataset.Dataset, train: bool = False) → typing.Dict[tensorflow.python.framework.ops.Tensor, typing.Any]¶ Populate the feed dictionary with the encoder inputs.
Parameters: - dataset – The dataset to use
- train – Boolean flag telling whether it is training time
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highway_layer
¶ Highway net projection following the CNN.
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output
¶
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rnn_cells
() → typing.Tuple[tensorflow.python.ops.rnn_cell_impl.RNNCell, tensorflow.python.ops.rnn_cell_impl.RNNCell]¶ Return the graph template to for creating RNN memory cells.
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temporal_mask
¶
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temporal_states
¶
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train_mode
¶
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neuralmonkey.encoders.sequence_cnn_encoder module¶
Encoder for sentence classification with 1D convolutions and max-pooling.
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class
neuralmonkey.encoders.sequence_cnn_encoder.
SequenceCNNEncoder
(name: str, vocabulary: neuralmonkey.vocabulary.Vocabulary, data_id: str, embedding_size: int, filters: typing.List[typing.Tuple[int, int]], max_input_len: typing.Union[int, NoneType] = None, dropout_keep_prob: float = 1.0, save_checkpoint: typing.Union[str, NoneType] = None, load_checkpoint: typing.Union[str, NoneType] = None) → None¶ Bases:
neuralmonkey.model.model_part.ModelPart
,neuralmonkey.model.stateful.Stateful
Encoder processing a sequence using a CNN.
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embedded_inputs
¶
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feed_dict
(dataset: neuralmonkey.dataset.Dataset, train: bool = False) → typing.Dict[tensorflow.python.framework.ops.Tensor, typing.Any]¶ Populate the feed dictionary with the encoder inputs.
- Encoder input placeholders:
encoder_input
: Stores indices to the vocabulary,- shape (batch, time)
encoder_padding
: Stores the padding (ones and zeros,- indicating valid words and positions after the end of sentence, shape (batch, time)
train_mode
: Boolean scalar specifying the mode (train- vs runtime)
Parameters: - dataset – The dataset to use
- train – Boolean flag telling whether it is training time
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input_mask
¶
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inputs
¶
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output
¶
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train_mode
¶
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