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xuming06
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update tensorflow to multi classification. xuming 20180226
1 parent df951d2 commit 1093c07

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Lines changed: 35292 additions & 35257 deletions

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‎07keras/09fasttext_multi_classification.py‎

Lines changed: 2 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -24,7 +24,8 @@ def get_corpus(data_dir):
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for file_name in os.listdir(data_dir):
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with open(os.path.join(data_dir, file_name), mode='r', encoding='utf-8') as f:
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for line in f:
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parts = line.rstrip().split(',')
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# label in first sep
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parts = line.rstrip().split(',', 1)
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if parts and len(parts) > 1:
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# keras categorical label start with 0
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lbl = int(parts[0]) - 1

‎17tensorflow/4_cnn_text_classification/config.py‎

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Original file line numberDiff line numberDiff line change
@@ -2,34 +2,36 @@
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# Author: XuMing <[email protected]>
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# Data: 17/10/17
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# Brief: 配置
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import os
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config = {
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# data
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"dev_sample_percentage": 0.1, # percentage of the training data for validation
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"positive_data_file": "./data/zh_polarity/pos.txt", # positive data
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"negative_data_file": "./data/zh_polarity/neg.txt", # negative data
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# model
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"embedding_dim": 128, # dimensionality of character embedding (default: 128)
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"filter_sizes": "3,4,5", # comma-separated filter size (default: "3,4,5")
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"num_filters": 128, # number of filters per filter size
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"dropout_keep_prob": 0.5, # dropout keep probability
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"l2_reg_lambda": 0.0, # l2 regulaization lambda
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# data
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dev_sample_percentage = 0.1 # percentage of the training data for validation
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data_dir = "./data/zh_polarity" # data file path
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# train
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"batch_size": 64, # batch size (default: 64)
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"num_epochs": 200, # number of training epochs (default: 200)
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"evaluate_every": 100, # evaluate model on dev set after this many steps (default: 100)
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"checkpoint_every": 100, # save model after this many steps (default: 100)
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"num_checkpoints": 5, # number of checkpoints to store
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# model
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embedding_dim = 128 # dimensionality of character embedding (default: 128)
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filter_sizes = "3,4,5" # comma-separated filter size (default: "3,4,5")
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num_filters = 128 # number of filters per filter size
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dropout_keep_prob = 0.5 # dropout keep probability
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l2_reg_lambda = 0.0 # l2 regulaization lambda
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# proto
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"allow_soft_placement": True, # allow device soft device placement
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"log_device_placement": False, # log placement of ops on devices
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}
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# train
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batch_size = 64 # batch size (default: 64)
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num_epochs = 200 # number of training epochs (default: 200)
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evaluate_every = 100 # evaluate model on dev set after this many steps (default: 100)
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checkpoint_every = 100 # save model after this many epochs (default: 100)
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num_checkpoints = 5 # number of checkpoints to store
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evaluate = {
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"infer_data": "./data/input_data.txt", # infer data
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"checkpoint_dir": "runs/20171020-1508503142/checkpoints", # checkpoint directory from training run
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"eval_all_train_data": False, # evaluate on all training data
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}
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# proto
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allow_soft_placement = True # allow device soft device placement
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log_device_placement = False # log placement of ops on devices
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infer_data_path = "./data/input_data.txt" # infer data
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checkpoint_dir = "./models/checkpoints" # checkpoint directory from training run
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eval_all_train_data = False # evaluate on all training data
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# directory to save the trained model
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# create a new directory if the dir does not exist
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if not os.path.exists(checkpoint_dir):
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os.mkdir(checkpoint_dir)

‎17tensorflow/4_cnn_text_classification/data/zh_polarity/neg.txt‎

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‎17tensorflow/4_cnn_text_classification/data/zh_polarity/neg_sample.txt‎

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