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203 строки
8.9 KiB
203 строки
8.9 KiB
import sys
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sys.path.append('../')
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import tensorflow as tf
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try:
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import tensorflow.python.keras as keras
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from tensorflow.python.keras import layers
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import tensorflow.python.keras.backend as K
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except:
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import tensorflow.keras as keras
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from tensorflow.keras import layers
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import tensorflow.keras.backend as K
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from typing import Optional
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from PIPE.config import Config
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from PIPE.keras_attention_layer import Attention_layer
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class Code2VecModel():
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def __init__(self,config: Config):
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self.keras_train_model: Optional[keras.Model] = None
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self.config = config
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##################################################搭建模型结构函数########################################################
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def _create_keras_model(self):
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path_source_token_input = layers.Input((self.config.MAX_CONTEXTS,), dtype=tf.int32)
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path_input = layers.Input((self.config.MAX_CONTEXTS,), dtype=tf.int32)
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path_target_token_input = layers.Input((self.config.MAX_CONTEXTS,), dtype=tf.int32)
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context_valid_mask = layers.Input((self.config.MAX_CONTEXTS,))
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# path embedding layer
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# (None, max_contents) -> (None,max_contents,path_embedding_size)
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paths_embedded = layers.Embedding(
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self.config.path_vocab_size, self.config.PATH_EMBEDDINGS_SIZE, name = 'path_embedding'
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)(path_input)
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# terminal embedding layer
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# (None, max_contents) -> (None,max_contents,token_embedding_size)
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token_embedding_shared_layer = layers.Embedding(
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self.config.token_vocab_size, self.config.TOKEN_EMBEDDINGS_SIZE, name = 'token_embedding'
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)
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path_source_token_embedded = token_embedding_shared_layer(path_source_token_input)
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path_target_token_embedded = token_embedding_shared_layer(path_target_token_input)
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# concatenate layer: paths -> [source, path, target]
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# [3 * (None,max_contents, token_embedding_size)] -> (None, max_contents,3*embedding_size)
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context_embedded = layers.Concatenate()([path_source_token_embedded, paths_embedded, path_target_token_embedded])
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context_embedded = layers.Dropout(1 - self.config.DROPOUT_KEEP_RATE)(context_embedded)
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# Dense layer: (None,max_contents,3*embedding_size) -> (None,max_contents, code_vector_size)
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context_after_dense = layers.TimeDistributed(
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layers.Dense(self.config.CODE_VECTOR_SIZE, use_bias=False, activation='tanh')
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)(context_embedded)
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# attention layer: (None, max_contents,code_vector_size) -> (None,code_vector_size)
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code_vectors, attention_weights = Attention_layer(name='attention')(
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[context_after_dense, context_valid_mask]
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)
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"""
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下面是用C2AE分类器进行分类的模型
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"""
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Fx = layers.Dense(
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186 , use_bias=True,activation=None,name='Fx'
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)(code_vectors)
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Fx_relu = tf.tanh(Fx)
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Fx_dropout = layers.Dropout(0.5)(Fx_relu)
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targets_input = layers.Input((self.config.categories,), dtype=tf.float32)
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targets_hidden = layers.Dense(
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186, use_bias=True,activation=None,name='targets_hidden'
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)(targets_input)
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targets_hidden_relu = tf.tanh(targets_hidden)
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targets_hidden_dropout = layers.Dropout(0.5)(targets_hidden_relu)
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targets_output = layers.Dense(
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186, use_bias=True,activation=None,name='targets_embedding'
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)(targets_hidden_dropout)
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targets_output_relu = tf.tanh(targets_output)
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targets_output_dropout = layers.Dropout(0.5)(targets_output_relu)
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targets_loss = layers.subtract([targets_output_dropout,Fx_dropout],name='targets_loss')
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Fd1 = layers.Dense(
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186, use_bias=True,activation=None,name='Fd1'
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)(Fx_dropout)
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Fd_relu1 = tf.tanh(Fd1)
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Fd_dropout1 = layers.Dropout(0.5)(Fd_relu1)
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Fd = layers.Dense(
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186, use_bias=True, activation=None, name='Fd'
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)(Fd_dropout1)
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Fd_relu = tf.tanh(Fd)
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Fd_dropout = layers.Dropout(0.5)(Fd_relu)
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target_index_3 = layers.Dense(
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self.config.categories, use_bias=True,activation='sigmoid',name='target_index_3'
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)(Fd_dropout)
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inputs = [path_source_token_input, path_input, path_target_token_input, context_valid_mask, targets_input]
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self.keras_train_model = keras.Model(inputs = inputs, outputs = [target_index_3,targets_loss])
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print("------------------create_keras_model Done.-------------------------")
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@classmethod
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def _create_optimizer(self):
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return tf.keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
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def _comile_keras_model(self, optimizer=None):
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if optimizer is None:
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optimizer = self._create_optimizer()
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################################评估指标##############################
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def exactMatch(y_true, y_pred):
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y_pred1 = y_pred
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row_dif = K.cast(K.sum(K.round(K.clip(y_true * (1 - y_pred1) + (1-y_true) * y_pred1,0,1)), axis=1) > K.epsilon(),'float32')
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dif = K.sum(K.round(row_dif))
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row_equ = K.cast(K.abs(K.sum(K.round(K.clip(y_true * y_pred1 + (1-y_true) * (1 - y_pred1),0,1)), axis=1) - self.config.categories) < K.epsilon(),'float32')
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equ = K.sum(K.round(row_equ))
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return equ / (equ + dif + K.epsilon())
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def micro_getPrecsion(y_true, y_pred):
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TP = tf.reduce_sum(y_true * tf.round(y_pred))
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TN = tf.reduce_sum((1 - y_true) * (1 - tf.round(y_pred)))
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FP = tf.reduce_sum((1 - y_true) * tf.round(y_pred))
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FN = tf.reduce_sum(y_true * (1 - tf.round(y_pred)))
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precision = TP / (TP + FP)
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return precision
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def micro_getRecall(y_true, y_pred):
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TP = tf.reduce_sum(y_true * tf.round(y_pred))
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TN = tf.reduce_sum((1 - y_true) * (1 - tf.round(y_pred)))
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FP = tf.reduce_sum((1 - y_true) * tf.round(y_pred))
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FN = tf.reduce_sum(y_true * (1 - tf.round(y_pred)))
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precision = TP / (TP + FP)
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recall = TP / (TP + FN)
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return recall
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# F1-score评价指标
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def micro_F1score(y_true, y_pred):
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TP = tf.reduce_sum(y_true * tf.round(y_pred))
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TN = tf.reduce_sum((1 - y_true) * (1 - tf.round(y_pred)))
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FP = tf.reduce_sum((1 - y_true) * tf.round(y_pred))
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FN = tf.reduce_sum(y_true * (1 - tf.round(y_pred)))
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precision = TP / (TP + FP)
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recall = TP / (TP + FN)
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F1score = 2 * precision * recall / (precision + recall)
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return F1score
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def macro_getPrecison(y_true, y_pred):
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col_TP = K.sum(y_true * K.round(y_pred), axis=0)
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col_TN = K.sum((1 - y_true) * (1 - K.round(y_pred)), axis=0)
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col_FP = K.sum((1 - y_true) * K.round(y_pred), axis=0)
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col_FN = K.sum(y_true * (1 - K.round(y_pred)), axis=0)
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precsion = K.mean(col_TP / (col_TP + col_FP + K.epsilon()))
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return precsion
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def macro_getRecall(y_true, y_pred):
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# print(y_true)
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row_TP = K.sum(y_true * K.round(y_pred), axis=0)
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row_TN = K.sum((1 - y_true) * (1 - K.round(y_pred)), axis=0)
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row_FP = K.sum((1 - y_true) * K.round(y_pred), axis=0)
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row_FN = K.sum(y_true * (1 - K.round(y_pred)), axis=0)
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recall = K.mean(row_TP / (row_TP + row_FN + K.epsilon()))
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return recall
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def macro_getF1score(y_true, y_pred):
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precision = macro_getPrecison(y_true, y_pred)
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recall = macro_getRecall(y_true, y_pred)
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F1score = 2 * precision * recall / (precision + recall)
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return F1score
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"""
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C2AE损失函数：
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custom_loss:
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返回模型最后一层target_loss的平方和,这里y_true是随机设的
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a_cross_loss:
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返回输出的二分类交叉熵
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"""
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def custom_loss(y_true,y_pred):
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return 1*tf.reduce_mean(tf.square(y_pred))
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def a_cross_loss(y_true, y_pred):
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cross_loss = tf.add(tf.log(1e-10 + y_pred) * y_true, tf.log(1e-10 + (1 - y_pred)) * (1 - y_true))
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cross_entropy_label = -1 * tf.reduce_mean(tf.reduce_sum(cross_loss, 1))
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return 0.1*cross_entropy_label
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self.keras_train_model.compile(
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loss = {'target_index_3':a_cross_loss,'targets_loss':custom_loss},
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optimizer=optimizer,
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metrics={'target_index_3':[exactMatch, micro_getPrecsion, micro_getRecall,micro_F1score,macro_getPrecison,macro_getRecall,macro_getF1score]}
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)
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if __name__ == "__main__":
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config = Config(set_defaults=True, load_from_args=True, verify=False)
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model = Code2VecModel(config)
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model._create_keras_model()
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