如何从csv文件构建Tensorflow的数据集
(编辑：jimmy 日期: 2024/11/17 浏览：3 次 )
从csv文件构建Tensorflow的数据集
当我们有一系列CSV文件，如何构建Tensorflow的数据集呢"htmlcode">
pprint.pprint(train_filenames) # ['generate_csv\\train_00.csv', # 'generate_csv\\train_01.csv', # 'generate_csv\\train_02.csv', # 'generate_csv\\train_03.csv', # 'generate_csv\\train_04.csv', # 'generate_csv\\train_05.csv', # 'generate_csv\\train_06.csv', # 'generate_csv\\train_07.csv', # 'generate_csv\\train_08.csv', # 'generate_csv\\train_09.csv', # 'generate_csv\\train_10.csv', # 'generate_csv\\train_11.csv', # 'generate_csv\\train_12.csv', # 'generate_csv\\train_13.csv', # 'generate_csv\\train_14.csv', # 'generate_csv\\train_15.csv', # 'generate_csv\\train_16.csv', # 'generate_csv\\train_17.csv', # 'generate_csv\\train_18.csv', # 'generate_csv\\train_19.csv']
接着，我们用提前定义好的API构建文件名数据集file_dataset
filename_dataset = tf.data.Dataset.list_files(train_filenames) for filename in filename_dataset: print(filename) #tf.Tensor(b'generate_csv\\train_09.csv', shape=(), dtype=string) #tf.Tensor(b'generate_csv\\train_19.csv', shape=(), dtype=string) #tf.Tensor(b'generate_csv\\train_03.csv', shape=(), dtype=string) #tf.Tensor(b'generate_csv\\train_01.csv', shape=(), dtype=string) #tf.Tensor(b'generate_csv\\train_14.csv', shape=(), dtype=string) #tf.Tensor(b'generate_csv\\train_17.csv', shape=(), dtype=string) #tf.Tensor(b'generate_csv\\train_15.csv', shape=(), dtype=string) #tf.Tensor(b'generate_csv\\train_06.csv', shape=(), dtype=string) #tf.Tensor(b'generate_csv\\train_05.csv', shape=(), dtype=string) #tf.Tensor(b'generate_csv\\train_07.csv', shape=(), dtype=string) #tf.Tensor(b'generate_csv\\train_11.csv', shape=(), dtype=string) #tf.Tensor(b'generate_csv\\train_02.csv', shape=(), dtype=string) #tf.Tensor(b'generate_csv\\train_12.csv', shape=(), dtype=string) #tf.Tensor(b'generate_csv\\train_13.csv', shape=(), dtype=string) #tf.Tensor(b'generate_csv\\train_10.csv', shape=(), dtype=string) #tf.Tensor(b'generate_csv\\train_16.csv', shape=(), dtype=string) #tf.Tensor(b'generate_csv\\train_18.csv', shape=(), dtype=string) #tf.Tensor(b'generate_csv\\train_00.csv', shape=(), dtype=string) #tf.Tensor(b'generate_csv\\train_04.csv', shape=(), dtype=string) #tf.Tensor(b'generate_csv\\train_08.csv', shape=(), dtype=string)
第三步， 根据每个文件名，去读取文件里面的内容
dataset = filename_dataset.interleave( lambda filename: tf.data.TextLineDataset(filename).skip(1), cycle_length=5 ) for line in dataset.take(3): print(line) #tf.Tensor(b'0.46908349737250216,1.8718193706428006,0.13936365871212536,-0.011055733363841472,-0.6349261778219746,-0.036732316700563934,1.0259470089944995,-1.319095600336748,2.171', shape=(), dtype=string) #tf.Tensor(b'-1.102093775650278,1.313248890578542,-0.7212003024178728,-0.14707856286537277,0.34720121604358517,0.0965085401826684,-0.74698820254838,0.6810563907247876,1.428', shape=(), dtype=string) #tf.Tensor(b'-0.8901003715328659,0.9142699762469286,-0.1851678950250224,-0.12947457252940406,0.5958187430364827,-0.021255215877779534,0.7914317693724252,-0.45618713536506217,0.75', shape=(), dtype=string)
interleave的作用可以类比map, 对每个元素应用操作，然后还能把结果合起来。
因此，有了interleave, 我们就把第三四步，一起完成了
之所以skip(1),是因为这个csv第一行是header.
cycle_length是并行化构建数据集的线程数
好，第五步，解析每条记录
def parse_csv_line(line, n_fields=9):
defaults = [tf.constant(np.nan)] * n_fields
parsed_fields = tf.io.decode_csv(line, record_defaults=defaults)
x = tf.stack(parsed_fields[:-1])
y = tf.stack(parsed_fields[-1:])
return x, y
parse_csv_line('1.2286258796252256,-1.0806245954111382,0.4444161407754224,-0.0352172575329119,0.9740347681426992,-0.003516079473801425,-0.8126524696425611,0.865609068204283,2.803', 9)
#(<tf.Tensor: shape=(8,), dtype=float32, numpy= array([ 1.2286259 , -1.0806246 , 0.44441614, -0.03521726, 0.9740348 ,-0.00351608, -0.81265247, 0.86560905], dtype=float32)>,<tf.Tensor: shape=(1,), dtype=float32, numpy=array([2.803], dtype=float32)>)
最后，将每条记录都应用这个方法，就完成了构建。
dataset = dataset.map(parse_csv_line)
完整代码
def csv_2_dataset(filenames, n_readers_thread = 5, batch_size = 32, n_parse_thread = 5, shuffle_buffer_size = 10000):
dataset = tf.data.Dataset.list_files(filenames)
dataset = dataset.repeat()
dataset = dataset.interleave(
lambda filename: tf.data.TextLineDataset(filename).skip(1),
cycle_length=n_readers_thread
)
dataset.shuffle(shuffle_buffer_size)
dataset = dataset.map(parse_csv_line, num_parallel_calls = n_parse_thread)
dataset = dataset.batch(batch_size)
return dataset
如何使用
train_dataset = csv_2_dataset(train_filenames, batch_size=32)
valid_dataset = csv_2_dataset(valid_filenames, batch_size=32)
model = ...
model.fit(train_set, validation_data=valid_set,
steps_per_epoch = 11610 // 32,
validation_steps = 3870 // 32,
epochs=100, callbacks=callbacks)
这里的11610 和 3870是什么？
这是train_dataset 和 valid_dataset中数据的数量，需要在训练中手动指定每个batch中参与训练的数据的多少。
model.evaluate(test_set, steps=5160//32)
同理，测试的时候，使用这样的数据集，也需要手动指定。
5160是测试数据集的总量。
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