forked from mindspore-Ecosystem/mindspore
fix textcnn possible file write incomplete
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14cf33a6df
commit
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@ -53,12 +53,12 @@ class DataProcessor:
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""" collect weight """
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""" collect weight """
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vocab_size = self.get_dict_len()
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vocab_size = self.get_dict_len()
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embedding_index = {}
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embedding_index = {}
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f = open(glove_path)
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with open(glove_path) as f:
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for line in f:
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for line in f:
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values = line.split()
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values = line.split()
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word = values[0]
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word = values[0]
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vec = np.array(values[1:], dtype='float32')
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vec = np.array(values[1:], dtype='float32')
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embedding_index[word] = vec
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embedding_index[word] = vec
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weight_np = np.zeros((vocab_size, embed_size)).astype(np.float32)
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weight_np = np.zeros((vocab_size, embed_size)).astype(np.float32)
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for word, vec in embedding_index.items():
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for word, vec in embedding_index.items():
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@ -129,10 +129,10 @@ class MovieReview(DataProcessor):
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self.Pos = []
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self.Pos = []
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self.Neg = []
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self.Neg = []
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for filename in self.files:
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for filename in self.files:
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f = codecs.open(filename, 'r')
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with codecs.open(filename, 'r') as f:
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ff = f.read()
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ff = f.read()
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file_object = codecs.open(filename, 'w', 'utf-8')
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with codecs.open(filename, 'w', 'utf-8') as file_object:
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file_object.write(ff)
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file_object.write(ff)
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self.read_data(filename)
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self.read_data(filename)
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self.PosNeg = self.Pos + self.Neg
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self.PosNeg = self.Pos + self.Neg
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self.text2vec(maxlen=maxlen)
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self.text2vec(maxlen=maxlen)
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@ -394,10 +394,10 @@ class SST2(DataProcessor):
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self.maxlen = float("-inf")
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self.maxlen = float("-inf")
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for filename in self.files:
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for filename in self.files:
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if 'train' in filename or 'dev' in filename:
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if 'train' in filename or 'dev' in filename:
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f = codecs.open(filename, 'r')
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with codecs.open(filename, 'r') as f:
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ff = f.read()
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ff = f.read()
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file_object = codecs.open(filename, 'w', 'utf-8')
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with codecs.open(filename, 'w', 'utf-8') as file_object:
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file_object.write(ff)
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file_object.write(ff)
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self.read_data(filename)
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self.read_data(filename)
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self.text2vec(maxlen=maxlen)
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self.text2vec(maxlen=maxlen)
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self.split_dataset(split=split)
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self.split_dataset(split=split)
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