Keras

2023-09-23 19:07| 来源: 网络整理| 查看: 265

最近在工作中进行了NLP的内容，使用的还是Keras中embedding的词嵌入来做的。

Keras中embedding层做一下介绍。

中文文档地址：https://keras.io/zh/layers/embeddings/

参数如下：

其中参数重点有input_dim,output_dim,非必选参数input_length.

初始化方法参数设置后面会单独总结一下。

demo使用预训练（使用百度百科（word2vec）的语料库）参考

embedding使用的demo参考：

def create_embedding(word_index, num_words, word2vec_model): embedding_matrix = np.zeros((num_words, EMBEDDING_DIM)) for word, i in word_index.items(): try: embedding_vector = word2vec_model[word] embedding_matrix[i] = embedding_vector except: continue return embedding_matrix #word_index:词典（统计词转换为索引） #num_word:词典长度+1 #word2vec_model:词向量的model

加载词向量model的方法：

def pre_load_embedding_model(model_file): # model = gensim.models.Word2Vec.load(model_file) # model = gensim.models.Word2Vec.load(model_file,binary=True) model = gensim.models.KeyedVectors.load_word2vec_format(model_file) return model

model中Embedding层的设置（注意参数，Input层的输入，初始化方法）：

embedding_matrix = create_embedding(word_index, num_words, word2vec_model) embedding_layer = Embedding(num_words, EMBEDDING_DIM, embeddings_initializer=Constant(embedding_matrix), input_length=MAX_SEQUENCE_LENGTH, trainable=False) sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32') embedded_sequences = embedding_layer(sequence_input)

embedding层的初始化设置

keras embeding设置初始值的两种方式

随机初始化Embedding

from keras.models import Sequential from keras.layers import Embedding import numpy as np model = Sequential() model.add(Embedding(1000, 64, input_length=10)) # the model will take as input an integer matrix of size (batch, input_length). # the largest integer (i.e. word index) in the input should be no larger than 999 (vocabulary size). # now model.output_shape == (None, 10, 64), where None is the batch dimension. input_array = np.random.randint(1000, size=(32, 10)) model.compile('rmsprop', 'mse') output_array = model.predict(input_array) print(output_array) assert output_array.shape == (32, 10, 64)

使用weights参数指明embedding初始值

import numpy as np import keras m = keras.models.Sequential() """ 可以通过weights参数指定初始的weights参数因为Embedding层是不可导的梯度东流至此回,所以把embedding放在中间层是没有意义的,emebedding只能作为第一层注意weights到embeddings的绑定过程很复杂，weights是一个列表 """ embedding = keras.layers.Embedding(input_dim=3, output_dim=2, input_length=1, weights=[np.arange(3 * 2).reshape((3, 2))], mask_zero=True) m.add(embedding) # 一旦add，就会自动调用embedding的build函数, print(keras.backend.get_value(embedding.embeddings)) m.compile(keras.optimizers.RMSprop(), keras.losses.mse) print(m.predict([1, 2, 2, 1, 2, 0])) print(m.get_layer(index=0).get_weights()) print(keras.backend.get_value(embedding.embeddings))

给embedding设置初始值的第二种方式：使用initializer

import numpy as np import keras m = keras.models.Sequential() """ 可以通过weights参数指定初始的weights参数因为Embedding层是不可导的梯度东流至此回,所以把embedding放在中间层是没有意义的,emebedding只能作为第一层给embedding设置权值的第二种方式，使用constant_initializer """ embedding = keras.layers.Embedding(input_dim=3, output_dim=2, input_length=1, embeddings_initializer=keras.initializers.constant(np.arange(3 * 2, dtype=np.float32).reshape((3, 2)))) m.add(embedding) print(keras.backend.get_value(embedding.embeddings)) m.compile(keras.optimizers.RMSprop(), keras.losses.mse) print(m.predict([1, 2, 2, 1, 2])) print(m.get_layer(index=0).get_weights()) print(keras.backend.get_value(embedding.embeddings))

关键的难点在于理清weights是怎么传入到embedding.embeddings张量里面去的。

Embedding是一个层，继承自Layer，Layer有weights参数，weights参数是一个list，里面的元素都是numpy数组。在调用Layer的构造函数的时候，weights参数就被存储到了_initial_weights变量

basic_layer.py 之Layer类

if 'weights' in kwargs: self._initial_weights = kwargs['weights'] else: self._initial_weights = None

当把Embedding层添加到模型中、跟模型的上一层进行拼接的时候，会调用layer(上一层)函数，此处layer是Embedding实例，Embedding是一个继承了Layer的类，Embedding类没有重写__call__()方法，Layer实现了__call__()方法。

父类Layer的__call__方法调用子类的call()方法来获取结果。

所以最终调用的是Layer.__call__()。在这个方法中，会自动检测该层是否build过（根据self.built布尔变量）。

Layer.__call__函数非常重要。

def __call__(self, inputs, **kwargs): """Wrapper around self.call(), for handling internal references. If a Keras tensor is passed: - We call self._add_inbound_node(). - If necessary, we `build` the layer to match the _keras_shape of the input(s). - We update the _keras_shape of every input tensor with its new shape (obtained via self.compute_output_shape). This is done as part of _add_inbound_node(). - We update the _keras_history of the output tensor(s) with the current layer. This is done as part of _add_inbound_node(). # Arguments inputs: Can be a tensor or list/tuple of tensors. **kwargs: Additional keyword arguments to be passed to `call()`. # Returns Output of the layer's `call` method. # Raises ValueError: in case the layer is missing shape information for its `build` call. """ if isinstance(inputs, list): inputs = inputs[:] with K.name_scope(self.name): # Handle laying building (weight creating, input spec locking). if not self.built:#如果未曾build，那就要先执行build再调用call函数 # Raise exceptions in case the input is not compatible # with the input_spec specified in the layer constructor. self.assert_input_compatibility(inputs) # Collect input shapes to build layer. input_shapes = [] for x_elem in to_list(inputs): if hasattr(x_elem, '_keras_shape'): input_shapes.append(x_elem._keras_shape) elif hasattr(K, 'int_shape'): input_shapes.append(K.int_shape(x_elem)) else: raise ValueError('You tried to call layer "' + self.name + '". This layer has no information' ' about its expected input shape, ' 'and thus cannot be built. ' 'You can build it manually via: ' '`layer.build(batch_input_shape)`') self.build(unpack_singleton(input_shapes)) self.built = True#这句话其实有些多余，因为self.build函数已经把built置为True了 # Load weights that were specified at layer instantiation. if self._initial_weights is not None:#如果传入了weights，把weights参数赋值到每个变量，此处会覆盖上面的self.build函数中的赋值。 self.set_weights(self._initial_weights) # Raise exceptions in case the input is not compatible # with the input_spec set at build time. self.assert_input_compatibility(inputs) # Handle mask propagation. previous_mask = _collect_previous_mask(inputs) user_kwargs = copy.copy(kwargs) if not is_all_none(previous_mask): # The previous layer generated a mask. if has_arg(self.call, 'mask'): if 'mask' not in kwargs: # If mask is explicitly passed to __call__, # we should override the default mask. kwargs['mask'] = previous_mask # Handle automatic shape inference (only useful for Theano). input_shape = _collect_input_shape(inputs) # Actually call the layer, # collecting output(s), mask(s), and shape(s). output = self.call(inputs, **kwargs) output_mask = self.compute_mask(inputs, previous_mask) # If the layer returns tensors from its inputs, unmodified, # we copy them to avoid loss of tensor metadata. output_ls = to_list(output) inputs_ls = to_list(inputs) output_ls_copy = [] for x in output_ls: if x in inputs_ls: x = K.identity(x) output_ls_copy.append(x) output = unpack_singleton(output_ls_copy) # Inferring the output shape is only relevant for Theano. if all([s is not None for s in to_list(input_shape)]): output_shape = self.compute_output_shape(input_shape) else: if isinstance(input_shape, list): output_shape = [None for _ in input_shape] else: output_shape = None if (not isinstance(output_mask, (list, tuple)) and len(output_ls) > 1): # Augment the mask to match the length of the output. output_mask = [output_mask] * len(output_ls) # Add an inbound node to the layer, so that it keeps track # of the call and of all new variables created during the call. # This also updates the layer history of the output tensor(s). # If the input tensor(s) had not previous Keras history, # this does nothing. self._add_inbound_node(input_tensors=inputs, output_tensors=output, input_masks=previous_mask, output_masks=output_mask, input_shapes=input_shape, output_shapes=output_shape, arguments=user_kwargs) # Apply activity regularizer if any: if (hasattr(self, 'activity_regularizer') and self.activity_regularizer is not None): with K.name_scope('activity_regularizer'): regularization_losses = [ self.activity_regularizer(x) for x in to_list(output)] self.add_loss(regularization_losses, inputs=to_list(inputs)) return output

如果没有build过，会自动调用Embedding类的build()函数。Embedding.build()这个函数并不会去管weights，如果它使用的initializer没有传入，self.embeddings_initializer会变成随机初始化。

如果传入了，那么在这一步就能够把weights初始化好。

如果同时传入embeddings_initializer和weights参数，那么weights参数稍后会把Embedding#embeddings覆盖掉。

embedding.py Embedding类的build函数

def build(self, input_shape): self.embeddings = self.add_weight( shape=(self.input_dim, self.output_dim), initializer=self.embeddings_initializer, name='embeddings', regularizer=self.embeddings_regularizer, constraint=self.embeddings_constraint, dtype=self.dtype) self.built = True

综上，在keras中，使用weights给Layer的变量赋值是一个比较通用的方法，但是不够直观。keras鼓励多多使用明确的initializer，而尽量不要触碰weights。

以上这篇Keras—embedding嵌入层的用法详解就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持脚本之家。

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