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問題
問題是這樣的，要把一個數組存到tfrecord中，然后讀取
a = np.array([[0, 54, 91, 153, 177,1], [0, 50, 89, 147, 196], [0, 38, 79, 157], [0, 49, 89, 147, 177], [0, 32, 73, 145]])
圖片我都存儲了，這個不還是小意思，一頓操作
import tensorflow as tf
import numpy as np
def _int64_feature(value):
if not isinstance(value,list):
value = [value]
return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
# Write an array to TFrecord.
# a is an array which contains lists of variant length.
a = np.array([[0, 54, 91, 153, 177,1],
[0, 50, 89, 147, 196],
[0, 38, 79, 157],
[0, 49, 89, 147, 177],
[0, 32, 73, 145]])
writer = tf.python_io.TFRecordWriter('file')
for i in range(a.shape[0]):
feature = {'i' : _int64_feature(i),
'data': _int64_feature(a[i])}
# Create an example protocol buffer
example = tf.train.Example(features=tf.train.Features(feature=feature))
# Serialize to string and write on the file
writer.write(example.SerializeToString())
writer.close()
# Use Dataset API to read the TFRecord file.
filenames = ["file"]
dataset = tf.data.TFRecordDataset(filenames)
def _parse_function(example_proto):
keys_to_features = {'i':tf.FixedLenFeature([],tf.int64),
'data':tf.FixedLenFeature([],tf.int64)}
parsed_features = tf.parse_single_example(example_proto, keys_to_features)
return parsed_features['i'], parsed_features['data']
dataset = dataset.map(_parse_function)
dataset = dataset.shuffle(buffer_size=1)
dataset = dataset.repeat()
dataset = dataset.batch(1)
iterator = dataset.make_one_shot_iterator()
i, data = iterator.get_next()
with tf.Session() as sess:
print(sess.run([i, data]))
print(sess.run([i, data]))
print(sess.run([i, data]))
報了奇怪的錯誤，Name: <unknown>, Key: data, Index: 0. Number of int64 values != expected. Values size: 6 but output shape: [] 這意思是我數據長度為6，但是讀出來的是[]，這到底是哪里錯了，我先把讀取的代碼注釋掉，看看tfreocrd有沒有寫成功，發現寫成功了，這就表明是讀取的問題，我懷疑是因為每次寫入的長度是變化的原因，但是又有覺得不是，因為圖片的尺寸都是不同的，我還是可以讀取的，百思不得其解的時候我發現存儲圖片的時候是img.tobytes(),我把一個數組轉換成了bytes，而且用的也是bytes存儲，是不是tensorflow會把這個bytes當成一個元素，雖然每個圖片的size不同，但是tobytes后tensorflow都會當成一個元素，然后讀取的時候再根據(height,width,channel)來解析成圖片。
我來試試不存為int64，而是存為bytes。 又是一頓厲害的操作
數據轉為bytes
# -*- coding: utf-8 -*-
import tensorflow as tf
import numpy as np
def _byte_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def _int64_feature(value):
if not isinstance(value,list):
value = [value]
return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
# Write an array to TFrecord.
# a is an array which contains lists of variant length.
a = np.array([[0, 54, 91, 153, 177,1],
[0, 50, 89, 147, 196],
[0, 38, 79, 157],
[0, 49, 89, 147, 177],
[0, 32, 73, 145]])
writer = tf.python_io.TFRecordWriter('file')
for i in range(a.shape[0]): # i = 0 ~ 4
feature = {'len' : _int64_feature(len(a[i])), # 將無意義的i改成len，為了后面還原
'data': _byte_feature(np.array(a[i]).tobytes())} # 我也不知道為什么a[i]是list（后面就知道了），要存bytes需要numpy一下
# Create an example protocol buffer
example = tf.train.Example(features=tf.train.Features(feature=feature))
# Serialize to string and write on the file
writer.write(example.SerializeToString())
writer.close()
#
# Use Dataset API to read the TFRecord file.
filenames = ["file"]
dataset = tf.data.TFRecordDataset(filenames)
def _parse_function(example_proto):
keys_to_features = {'len':tf.FixedLenFeature([],tf.int64),
'data':tf.FixedLenFeature([],tf.string)} # 改成string
parsed_features = tf.parse_single_example(example_proto, keys_to_features)
return parsed_features['len'], parsed_features['data']
dataset = dataset.map(_parse_function)
dataset = dataset.shuffle(buffer_size=1)
dataset = dataset.repeat()
dataset = dataset.batch(1)
iterator = dataset.make_one_shot_iterator()
i, data = iterator.get_next()
with tf.Session() as sess:
print(sess.run([i, data]))
print(sess.run([i, data]))
print(sess.run([i, data]))
"""
[array([6], dtype=int64), array([b'\x00\x00\x00\x006\x00\x00\x00[\x00\x00\x00\x99\x00\x00\x00\xb1\x00\x00\x00\x01\x00\x00\x00'],
dtype=object)]
[array([5], dtype=int64), array([b'\x00\x00\x00\x002\x00\x00\x00Y\x00\x00\x00\x93\x00\x00\x00\xc4\x00\x00\x00'],
dtype=object)]
[array([4], dtype=int64), array([b'\x00\x00\x00\x00&\x00\x00\x00O\x00\x00\x00\x9d\x00\x00\x00'],
dtype=object)]
"""
bytes數據解碼
如愿的輸出來了，但是這個bytes我該如何解碼呢
方法一，我們自己解析
a,b= sess.run([i,data]) c = np.frombuffer(b[0],dtype=np.int,count=a[0])
方法二使用tensorflow的解析函數
def _parse_function(example_proto):
keys_to_features = {'len':tf.FixedLenFeature([],tf.int64),
'data':tf.FixedLenFeature([],tf.string)} # 改成string
parsed_features = tf.parse_single_example(example_proto, keys_to_features)
dat = tf.decode_raw(parsed_features['data'],tf.int64) # 用的是這個解析函數，我們使用int64的格式存儲的，解析的時候也是轉換為int64
return parsed_features['len'], dat
"""
[array([6]), array([[ 0, 54, 91, 153, 177, 1]])]
[array([5]), array([[ 0, 50, 89, 147, 196]])]
[array([4]), array([[ 0, 38, 79, 157]])]
"""
可以看到是二維數組，這是因為我們使用的是batch輸出，雖然我們的bathc_size=1，但是還是會以二維list的格式輸出。我手賤再來修改點東西，
def _parse_function(example_proto):
keys_to_features = {'len':tf.FixedLenFeature([1],tf.int64),
'data':tf.FixedLenFeature([1],tf.string)}
parsed_features = tf.parse_single_example(example_proto, keys_to_features)
dat = tf.decode_raw(parsed_features['data'],tf.int64)
return parsed_features['len'], dat
"""
[array([[6]]), array([[[ 0, 54, 91, 153, 177, 1]]])]
[array([[5]]), array([[[ 0, 50, 89, 147, 196]]])]
[array([[4]]), array([[[ 0, 38, 79, 157]]])]
"""
呦呵，又變成3維的了，讓他報個錯試試
def _parse_function(example_proto):
keys_to_features = {'len':tf.FixedLenFeature([2],tf.int64), # 1 修改為 2
'data':tf.FixedLenFeature([1],tf.string)} # 改成string
parsed_features = tf.parse_single_example(example_proto, keys_to_features)
return parsed_features['len'], parsed_features['data']
"""
InvalidArgumentError: Key: len. Can't parse serialized Example.
[[Node: ParseSingleExample/ParseSingleExample = ParseSingleExample[Tdense=[DT_STRING, DT_INT64], dense_keys=["data", "len"], dense_shapes=[[1], [2]], num_sparse=0, sparse_keys=[], sparse_types=[]](arg0, ParseSingleExample/Const, ParseSingleExample/Const_1)]]
[[Node: IteratorGetNext_22 = IteratorGetNext[output_shapes=[[?,2], [?,1]], output_types=[DT_INT64, DT_STRING], _device="/job:localhost/replica:0/task:0/device:CPU:0"](OneShotIterator_22)]]
"""
可以看到dense_keys=["data", "len"], dense_shapes=[[1], [2]],，tf.FixedLenFeature是讀取固定長度的數據，我猜測[]的意思就是讀取全部數據，[1]就是讀取一個數據，每個數據可能包含多個數據，形如[[1，2],[3，3，4],[2]....]，哈哈這都是我瞎猜的，做我女朋友好不好。
tensorflow 變長數組存儲
反正是可以讀取了。但是如果是自己定義的變長數組，每次都要自己解析，這樣很麻煩（我瞎遍的），所以tensorflow就定義了變長數組的解析方法tf.VarLenFeature，我們就不需要把邊長數組變為bytes再解析了，又是一頓操作
import tensorflow as tf
import numpy as np
def _int64_feature(value):
if not isinstance(value,list):
value = [value]
return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
# Write an array to TFrecord.
# a is an array which contains lists of variant length.
a = np.array([[0, 54, 91, 153, 177,1],
[0, 50, 89, 147, 196],
[0, 38, 79, 157],
[0, 49, 89, 147, 177],
[0, 32, 73, 145]])
writer = tf.python_io.TFRecordWriter('file')
for i in range(a.shape[0]): # i = 0 ~ 4
feature = {'i' : _int64_feature(i),
'data': _int64_feature(a[i])}
# Create an example protocol buffer
example = tf.train.Example(features=tf.train.Features(feature=feature))
# Serialize to string and write on the file
writer.write(example.SerializeToString())
writer.close()
# Use Dataset API to read the TFRecord file.
filenames = ["file"]
dataset = tf.data.TFRecordDataset(filenames)
def _parse_function(example_proto):
keys_to_features = {'i':tf.FixedLenFeature([],tf.int64),
'data':tf.VarLenFeature(tf.int64)}
parsed_features = tf.parse_single_example(example_proto, keys_to_features)
return parsed_features['i'], tf.sparse_tensor_to_dense(parsed_features['data'])
dataset = dataset.map(_parse_function)
dataset = dataset.shuffle(buffer_size=1)
dataset = dataset.repeat()
dataset = dataset.batch(1)
iterator = dataset.make_one_shot_iterator()
i, data = iterator.get_next()
with tf.Session() as sess:
print(sess.run([i, data]))
print(sess.run([i, data]))
print(sess.run([i, data]))
"""
[array([0], dtype=int64), array([[ 0, 54, 91, 153, 177, 1]], dtype=int64)]
[array([1], dtype=int64), array([[ 0, 50, 89, 147, 196]], dtype=int64)]
[array([2], dtype=int64), array([[ 0, 38, 79, 157]], dtype=int64)]
"""
batch輸出
輸出還是數組，哈哈哈。再來一波操作
dataset = dataset.batch(2) """ Cannot batch tensors with different shapes in component 1. First element had shape [6] and element 1 had shape [5]. """
這是因為一個batch中數據的shape必須是一致的，第一個元素長度為6，第二個元素長度為5，就會報錯。辦法就是補成一樣的長度，在這之前先測試點別的
a = np.array([[0, 54, 91, 153, 177,1], [0, 50, 89, 147, 196], [0, 38, 79, 157], [0, 49, 89, 147, 177], [0, 32, 73, 145]]) for i in range(a.shape[0]): print(type(a[i])) """ <class 'list'> <class 'list'> <class 'list'> <class 'list'> <class 'list'> """
可以發現長度不一的array每一個數據是list（一開始我以為是object）。然后補齊
a = np.array([[0, 54, 91, 153, 177,1], [0, 50, 89, 147, 196,0], [0, 38, 79, 157,0,0], [0, 49, 89, 147, 177,0], [0, 32, 73, 145,0,0]]) for i in range(a.shape[0]): print(type(a[i])) """ <class 'numpy.ndarray'> <class 'numpy.ndarray'> <class 'numpy.ndarray'> <class 'numpy.ndarray'> <class 'numpy.ndarray'> """
返回的是numpy。為什么要做這件事呢？
def _int64_feature(value): if not isinstance(value,list): value = [value] return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
tensorflow要求我們輸入的是list或者直接是numpy.ndarry，如果是list中包含numpy.ndarry [numpy.ndarry]就會報錯。上面的那個數組時邊長的，返回的時list，沒有什么錯誤，我們補齊看看
a = np.array([[0, 54, 91, 153, 177,1], [0, 50, 89, 147, 196,0], [0, 38, 79, 157,0,0], [0, 49, 89, 147, 177,0], [0, 32, 73, 145,0,0]]) """ TypeError: only size-1 arrays can be converted to Python scalars """
這就是因為返回的不是list，而是numpy.ndarry,而_int64_feature函數中先判斷numpy.ndarry不是list，所以轉成了[numpy.ndarry]就報錯了。可以做些修改，一種方法是將numpy.ndarry轉為list
for i in range(a.shape[0]): # i = 0 ~ 4
feature = {'i' : _int64_feature(i),
'data': _int64_feature(a[i].tolist())}
這樣補齊了我們就可以修改batch的值了
dataset = dataset.batch(2) """ [array([0, 2], dtype=int64), array([[ 0, 54, 91, 153, 177, 1], [ 0, 38, 79, 157, 0, 0]], dtype=int64)] [array([1, 3], dtype=int64), array([[ 0, 50, 89, 147, 196, 0], [ 0, 49, 89, 147, 177, 0]], dtype=int64)] [array([4, 0], dtype=int64), array([[ 0, 32, 73, 145, 0, 0], [ 0, 54, 91, 153, 177, 1]], dtype=int64)] """
當然tensorflow不會讓我自己補齊，已經提供了補齊函數padded_batch，
# -*- coding: utf-8 -*-
import tensorflow as tf
def _int64_feature(value):
if not isinstance(value,list):
value = [value]
return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
a = [[0, 54, 91, 153, 177,1],
[0, 50, 89, 147, 196],
[0, 38, 79, 157],
[0, 49, 89, 147, 177],
[0, 32, 73, 145]]
writer = tf.python_io.TFRecordWriter('file')
for v in a: # i = 0 ~ 4
feature = {'data': _int64_feature(v)}
# Create an example protocol buffer
example = tf.train.Example(features=tf.train.Features(feature=feature))
# Serialize to string and write on the file
writer.write(example.SerializeToString())
writer.close()
# Use Dataset API to read the TFRecord file.
filenames = ["file"]
dataset = tf.data.TFRecordDataset(filenames)
def _parse_function(example_proto):
keys_to_features = {'data':tf.VarLenFeature(tf.int64)}
parsed_features = tf.parse_single_example(example_proto, keys_to_features)
return tf.sparse_tensor_to_dense( parsed_features['data'])
dataset = dataset.map(_parse_function)
dataset = dataset.shuffle(buffer_size=1)
dataset = dataset.repeat()
dataset = dataset.padded_batch(2,padded_shapes=([None]))
iterator = dataset.make_one_shot_iterator()
data = iterator.get_next()
with tf.Session() as sess:
print(sess.run([data]))
print(sess.run([data]))
print(sess.run([data]))
"""
[array([[ 0, 54, 91, 153, 177, 1],
[ 0, 50, 89, 147, 196, 0]])]
[array([[ 0, 38, 79, 157, 0],
[ 0, 49, 89, 147, 177]])]
[array([[ 0, 32, 73, 145, 0, 0],
[ 0, 54, 91, 153, 177, 1]])]
"""
可以看到的確是自動補齊了。
圖片batch
直接來測試一下圖片數據
# -*- coding: utf-8 -*-
import tensorflow as tf
import matplotlib.pyplot as plt
def _byte_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
files = tf.gfile.Glob('*.jpeg')
writer = tf.python_io.TFRecordWriter('file')
for file in files:
with tf.gfile.FastGFile(file,'rb') as f:
img_buff = f.read()
feature = {'img': _byte_feature(tf.compat.as_bytes(img_buff))}
example = tf.train.Example(features=tf.train.Features(feature=feature))
writer.write(example.SerializeToString())
writer.close()
filenames = ["file"]
dataset = tf.data.TFRecordDataset(filenames)
def _parse_function(example_proto):
keys_to_features = {'img':tf.FixedLenFeature([], tf.string)}
parsed_features = tf.parse_single_example(example_proto, keys_to_features)
image = tf.image.decode_jpeg(parsed_features['img'])
return image
dataset = dataset.map(_parse_function)
dataset = dataset.shuffle(buffer_size=1)
dataset = dataset.repeat()
dataset = dataset.batch(2)
iterator = dataset.make_one_shot_iterator()
image = iterator.get_next()
with tf.Session() as sess:
img = sess.run([image])
print(len(img))
print(img[0].shape)
plt.imshow(img[0][0])
"""
Cannot batch tensors with different shapes in component 0. First element had shape [440,440,3] and element 1 had shape [415,438,3].
"""
看到了沒有，一個batch中圖片的尺寸不同，就不可以batch了，我們必須要將一個batch的圖片resize成相同的代大小。
def _parse_function(example_proto):
keys_to_features = {'img':tf.FixedLenFeature([], tf.string)}
parsed_features = tf.parse_single_example(example_proto, keys_to_features)
image = tf.image.decode_jpeg(parsed_features['img'])
image = tf.image.convert_image_dtype(image,tf.float32)# 直接resize，會將uint8轉為float類型，但是plt.imshow只能顯示uint8或者0-1之間float類型，這個函數就是將uint8轉為0-1之間的float類型，相當于除以255.0
image = tf.image.resize_images(image,(224,224))
return image
但是有時候我們希望輸入圖片尺寸是不一樣的，不需要reize，這樣只能將batch_size=1。一個batch中的圖片shape必須是一樣的，我們可以這樣折中訓練，使用tensorflow提供的動態填充接口，將一個batch中的圖片填充為相同的shape。
dataset = dataset.padded_batch(2,padded_shapes=([None,None,3]))
如果我們想要將圖片的名稱作為標簽保存下來要怎么做呢？
# -*- coding: utf-8 -*-
import tensorflow as tf
import matplotlib.pyplot as plt
import os
out_charset="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789"
def _byte_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def _int64_feature(values):
if not isinstance(values,list):
values = [values]
return tf.train.Feature(int64_list=tf.train.Int64List(value=values))
files = tf.gfile.Glob('*.jpg')
writer = tf.python_io.TFRecordWriter('file')
for file in files:
with tf.gfile.FastGFile(file,'rb') as f:
img_buff = f.read()
filename = os.path.basename(file).split('.')[0]
label = list(map(lambda x:out_charset.index(x),filename))
feature = {'label':_int64_feature(label),
'filename':_byte_feature(tf.compat.as_bytes(filename)),
'img': _byte_feature(tf.compat.as_bytes(img_buff))}
example = tf.train.Example(features=tf.train.Features(feature=feature))
writer.write(example.SerializeToString())
writer.close()
filenames = ["file"]
dataset = tf.data.TFRecordDataset(filenames)
def _parse_function(example_proto):
keys_to_features = {
'label':tf.VarLenFeature(tf.int64),
'filename':tf.FixedLenFeature([],tf.string),
'img':tf.FixedLenFeature([], tf.string)}
parsed_features = tf.parse_single_example(example_proto, keys_to_features)
label = tf.sparse_tensor_to_dense(parsed_features['label'])
filename = parsed_features['filename']
image = tf.image.decode_jpeg(parsed_features['img'])
return image,label,filename
dataset = dataset.map(_parse_function)
dataset = dataset.shuffle(buffer_size=1)
dataset = dataset.repeat()
dataset = dataset.padded_batch(3,padded_shapes=([None,None,3],[None],[]))
#因為返回有三個，所以每一個都要有padded_shapes,但是解碼后的image和label都是變長的
#所以需要pad None,而filename沒有解碼，返回來是byte類型的，只有一個值，所以不需要pad
iterator = dataset.make_one_shot_iterator()
image,label,filename = iterator.get_next()
with tf.Session() as sess:
print(label.eval())
瞎試
如果寫入的數據是一個list會是怎樣呢
a = np.arange(16).reshape(2,4,2) """ TypeError: [0, 1] has type list, but expected one of: int, long """
不過想想也是，tf.train.Feature(int64_list=tf.train.Int64List(value=value))這個函數就是存儲數據類型為int64的list的。但是如果我們要存儲詞向量該怎么辦呢？例如一句話是一個樣本s1='我愛你',假如使用one-hot編碼，我=[0,0,1],愛=[0,1,0],你=[1,0,0],s1=[[0,0,1],[0,1,0],[1,0,0]]。這一個樣本該怎么存儲呢？
以上這篇tensorflow 變長序列存儲實例就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支持億速云。
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