Source code for tensorforce.core.layers.embedding

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import tensorflow as tf

from tensorforce import TensorforceError, util
from tensorforce.core.layers import TransformationBase


[docs]class Embedding(TransformationBase): """ Embedding layer (specification key: `embedding`). Args: name (string): Layer name (<span style="color:#00C000"><b>default</b></span>: internally chosen). size (int >= 0): Layer output size, 0 implies additionally removing the axis (<span style="color:#C00000"><b>required</b></span>). num_embeddings (int > 0): If set, specifies the number of embeddings (<span style="color:#00C000"><b>default</b></span>: none). partition_strategy ('mod' | 'div'): Partitioning strategy, see `TensorFlow docs <https://www.tensorflow.org/api_docs/python/tf/nn/embedding_lookup>`__ (<span style="color:#00C000"><b>default</b></span>: 'mod'). max_norm (float): If set, embeddings are clipped if their L2-norm is larger (<span style="color:#00C000"><b>default</b></span>: none). bias (bool): Whether to add a trainable bias variable (<span style="color:#00C000"><b>default</b></span>: false). activation ('crelu' | 'elu' | 'leaky-relu' | 'none' | 'relu' | 'selu' | 'sigmoid' | 'softmax' | 'softplus' | 'softsign' | 'swish' | 'tanh'): Activation nonlinearity (<span style="color:#00C000"><b>default</b></span>: "tanh"). dropout (parameter, 0.0 <= float < 1.0): Dropout rate (<span style="color:#00C000"><b>default</b></span>: 0.0). is_trainable (bool): Whether layer variables are trainable (<span style="color:#00C000"><b>default</b></span>: true). input_spec (specification): Input tensor specification (<span style="color:#00C000"><b>internal use</b></span>). summary_labels ('all' | iter[string]): Labels of summaries to record (<span style="color:#00C000"><b>default</b></span>: inherit value of parent module). l2_regularization (float >= 0.0): Scalar controlling L2 regularization (<span style="color:#00C000"><b>default</b></span>: inherit value of parent module). kwargs: Additional arguments for potential parent class. """ def __init__( self, name, size, num_embeddings=None, partition_strategy='mod', max_norm=None, bias=False, activation='tanh', dropout=0.0, is_trainable=True, input_spec=None, summary_labels=None, l2_regularization=None ): """ Embedding constructor. Args: size (int >= 0): Layer output size, 0 implies additionally removing the axis (**required**). bias (bool): Whether to add a trainable bias variable (default: false). activation ('crelu' | 'elu' | 'leaky-relu' | 'none' | 'relu' | 'selu' | 'sigmoid' | 'softmax' | 'softplus' | 'softsign' | 'swish' | 'tanh'): Activation nonlinearity (default: 'tanh'). """ super().__init__( name=name, size=size, bias=bias, activation=activation, dropout=dropout, is_trainable=is_trainable, input_spec=input_spec, summary_labels=summary_labels, l2_regularization=l2_regularization ) self.num_embeddings = num_embeddings self.partition_strategy = partition_strategy self.max_norm = max_norm def default_input_spec(self): return dict(type=('int', 'bool'), shape=None, num_values=0) def get_output_spec(self, input_spec): input_spec['type'] = 'float' if not self.squeeze: if input_spec['shape'] is None: input_spec['shape'] = (None, self.size) else: input_spec['shape'] = input_spec['shape'] + (self.size,) input_spec.pop('num_values', None) return input_spec def tf_initialize(self): super().tf_initialize() if self.num_embeddings is None: if self.input_spec['type'] == 'bool': self.num_embeddings = 2 elif self.input_spec['type'] == 'int': self.num_embeddings = self.input_spec['num_values'] if self.num_embeddings == 0: raise TensorforceError.value( name='input_spec', argument='num_values', value=self.num_embeddings ) initializer = 'normal' if self.activation is not None and self.activation.nonlinearity == 'relu': initializer += '-relu' self.weights = self.add_variable( name='embeddings', dtype='float', shape=(self.num_embeddings, self.size), is_trainable=self.is_trainable, initializer=initializer ) def tf_apply(self, x): if util.tf_dtype('int') not in (tf.int32, tf.int64): x = tf.dtypes.cast(x=x, dtype=tf.int32) elif util.dtype(x=x) == 'bool': x = tf.dtypes.cast(x=x, dtype=util.tf_dtype('int')) x = tf.nn.embedding_lookup( params=self.weights, ids=x, partition_strategy=self.partition_strategy, max_norm=self.max_norm ) return super().tf_apply(x=x)