Python版本= 3.6.3 Tensorflow版本= 1.3.0
我曾在Keras工作，但我现在正试图直接在TensorFlow工作。 我正在努力实现Keras fit_generator的等效性，我不必在开始时将所有训练数据加载到内存中，但可以将其输入培训所需的网络。下面的代码代表我尝试开始这样的事情，但是如果我说这一切都错了，我很想知道我应该看哪些文档以及我应该使用哪些关键字搜索这个。
我的系统目前基于一个生成器，该生成器读取sqlite数据库文件以提取np.arrays，然后将它们转换为我想要的数据形状（具有一个前向预测的时间序列）。我现在正在尝试迁移该系统以使用Tensorflow Dataset并且无法应用tf.py_func。以下是我现在正在努力工作的方式
import tensorflow as tf
import os
from tensorflow.contrib.data import Dataset, Iterator
import sqlite3
import pandas as pd
import numpy as np
LOOKBACK_ROWS = 600
DATA_DIR = '/mnt/derived_data/processedData'
files = os.listdir(DATA_DIR)
def data_from_files(f):
with sqlite3.connect(DATA_DIR + f) as conn:
results = conn.execute("SELECT col1, col2, FROM tbl")
col_names = [d[0] for d in results.description]
arr = np.array(results.fetchall())
num_obs = arr.shape[0] - LOOKBACK_ROWS + 1
X = np.zeros((num_obs, LOOKBACK_ROWS, 1), dtype = np.float32)
Y = np.zeros((num_obs, 1), dtype = np.float32)
for i in range(num_obs):
idx = i + LOOKBACK_ROWS - 1
X[i , :, 0] = arr[(idx - LOOKBACK_ROWS + 1):(idx + 1), 0]
Y[i, 0] = arr[idx, 1]
return tf.convert_to_tensor(X, name = 'X'), tf.convert_to_tensor(Y, name = 'Y')
filenames = tf.constant(files)
dataset = Dataset.from_tensor_slices((filenames))
dataset = dataset.map(lambda filename: tuple(tf.py_func(
data_from_files,
[filename],
[tf.float32, tf.float32])))
iterator = Iterator.from_structure(dataset.output_types, dataset.output_shapes)
next_element = iterator.get_next()
dataset_init_op = iterator.make_initializer(dataset)
with tf.Session() as sess:
sess.run(dataset_init_op)
while True:
try:
elem = sess.run(next_element)
print('Success')
except tf.errors.OutOfRangeError:
print('End of dataset.')
break
初始化运行正常，但是当我启动会话并运行时，我得到以下错误：
2017-10-16 16:58:45.227612: I tensorflow/core/common_runtime/gpu/gpu_device.cc:976] DMA: 0
2017-10-16 16:58:45.227615: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 0: Y
2017-10-16 16:58:45.227620: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:65:00.0)
2017-10-16 16:58:45.276138: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: TypeError: must be str, not bytes
2017-10-16 16:58:45.276306: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: TypeError: must be str, not bytes
[[Node: PyFunc = PyFunc[Tin=[DT_STRING], Tout=[DT_FLOAT, DT_FLOAT], token="pyfunc_0"](arg0)]]
Traceback (most recent call last):
File "/opt/python/3.6.3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1327, in _do_call
return fn(*args)
File "/opt/python/3.6.3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1306, in _run_fn
status, run_metadata)
File "/opt/python/3.6.3/lib/python3.6/contextlib.py", line 88, in __exit__
next(self.gen)
File "/opt/python/3.6.3/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py", line 466, in raise_exception_on_not_ok_status
pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: TypeError: must be str, not bytes
[[Node: PyFunc = PyFunc[Tin=[DT_STRING], Tout=[DT_FLOAT, DT_FLOAT], token="pyfunc_0"](arg0)]]
[[Node: IteratorGetNext = IteratorGetNext[output_shapes=[<unknown>, <unknown>], output_types=[DT_FLOAT, DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](Iterator)]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/usr/code/nn/data_folder/pipeline.py", line 51, in <module>
elem = sess.run(next_element)
File "/opt/python/3.6.3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 895, in run
run_metadata_ptr)
File "/opt/python/3.6.3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1124, in _run
feed_dict_tensor, options, run_metadata)
File "/opt/python/3.6.3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1321, in _do_run
options, run_metadata)
File "/opt/python/3.6.3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1340, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: TypeError: must be str, not bytes
[[Node: PyFunc = PyFunc[Tin=[DT_STRING], Tout=[DT_FLOAT, DT_FLOAT], token="pyfunc_0"](arg0)]]
[[Node: IteratorGetNext = IteratorGetNext[output_shapes=[<unknown>, <unknown>], output_types=[DT_FLOAT, DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](Iterator)]]
>>> python.el: native completion setup loaded
>>>
问题
（1）这似乎只是py_func的用例，但我错了吗？如果没有，有人能指出一些比Tensorflow文档更深入的资源吗？ （我确实注意到git上有一个潜在的相关问题：https://github.com/tensorflow/tensorflow/issues/12396但是使用tuple包装所有内容的修复程序对我没有帮助。）
（2）我应该遵循的一般流程是什么，尤其是我想从一堆文件名开始，并为每个文件名输出多个培训Example？
谢谢。
下面我重写了我的脚本，以便它可以是一个自包含的可运行示例。我相信问题仍然与上面的代码相同，但我也在重复这个错误以确认。
自包含的可运行代码示例，其中包含来自@ mrry的答案的更改：
import tensorflow as tf
import os
import numpy as np
LOOKBACK_ROWS = 600
arr = np.random.random_sample((2000, 2))
np.save("npfile.npy", arr)
def data_from_files(f):
arr = np.load(f)
num_obs = arr.shape[0] - LOOKBACK_ROWS + 1
X = np.zeros((num_obs, LOOKBACK_ROWS, 1), dtype = np.float32)
Y = np.zeros((num_obs, 1), dtype = np.float32)
for i in range(num_obs):
idx = i + LOOKBACK_ROWS - 1
X[i , :, 0] = arr[(idx - LOOKBACK_ROWS + 1):(idx + 1), 0]
Y[i, 0] = arr[idx, 1]
return X, Y
files = ["npfile.npy"]
filenames = tf.constant(files)
# NOTE: In TensorFlow 1.4, `tf.contrib.data` is now `tf.data`.
dataset = tf.contrib.data.Dataset.from_tensor_slices(filenames)
# NOTE: In TensorFlow 1.4, the `tuple` is no longer needed.
dataset = dataset.map(lambda filename: tuple(tf.py_func(
data_from_files,
[filename],
[tf.float32, tf.float32])))
# NOTE: If you only have one `Dataset`, you do not need to use
# `Iterator.from_structure()`.
iterator = dataset.make_initializable_iterator()
next_element = iterator.get_next()
with tf.Session() as sess:
sess.run(iterator.initializer)
while True:
try:
elem = sess.run(next_element)
print('Success')
except tf.errors.OutOfRangeError:
print('End of dataset.')
break
错误：
2017-10-16 18:30:44.143668: I tensorflow/core/common_runtime/gpu/gpu_device.cc:976] DMA: 0
2017-10-16 18:30:44.143672: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 0: Y
2017-10-16 18:30:44.143679: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:65:00.0)
2017-10-16 18:30:44.190852: W tensorflow/core/framework/op_kernel.cc:1192] Unknown: AttributeError: 'bytes' object has no attribute 'read'
2017-10-16 18:30:44.190959: W tensorflow/core/framework/op_kernel.cc:1192] Unknown: AttributeError: 'bytes' object has no attribute 'read'
[[Node: PyFunc = PyFunc[Tin=[DT_STRING], Tout=[DT_FLOAT, DT_FLOAT], token="pyfunc_0"](arg0)]]
Traceback (most recent call last):
File "/opt/python/3.6.3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1327, in _do_call
return fn(*args)
File "/opt/python/3.6.3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1306, in _run_fn
status, run_metadata)
File "/opt/python/3.6.3/lib/python3.6/contextlib.py", line 88, in __exit__
next(self.gen)
File "/opt/python/3.6.3/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py", line 466, in raise_exception_on_not_ok_status
pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.UnknownError: AttributeError: 'bytes' object has no attribute 'read'
[[Node: PyFunc = PyFunc[Tin=[DT_STRING], Tout=[DT_FLOAT, DT_FLOAT], token="pyfunc_0"](arg0)]]
[[Node: IteratorGetNext = IteratorGetNext[output_shapes=[<unknown>, <unknown>], output_types=[DT_FLOAT, DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](Iterator)]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "demo.py", line 48, in <module>
elem = sess.run(next_element)
File "/opt/python/3.6.3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 895, in run
run_metadata_ptr)
File "/opt/python/3.6.3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1124, in _run
feed_dict_tensor, options, run_metadata)
File "/opt/python/3.6.3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1321, in _do_run
options, run_metadata)
File "/opt/python/3.6.3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1340, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.UnknownError: AttributeError: 'bytes' object has no attribute 'read'
[[Node: PyFunc = PyFunc[Tin=[DT_STRING], Tout=[DT_FLOAT, DT_FLOAT], token="pyfunc_0"](arg0)]]
[[Node: IteratorGetNext = IteratorGetNext[output_shapes=[<unknown>, <unknown>], output_types=[DT_FLOAT, DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](Iterator)]]
答案 0 :(得分：2)
以相反的顺序提出问题：
我应该遵循的一般流程是什么，特别是在我想从一堆文件名开始并为每个文件名输出多个培训示例的地方？
要将一个元素转换为多个元素，请使用Dataset.flat_map(f)转换。通过此转换，您可以将映射单个元素X = tenrand([13 10 80]) %<-- Generate data
U1 = nvecs(X,1,4); %<-- Mode 1
U2 = nvecs(X,2,3); %<-- Mode 2
U3 = nvecs(X,3,15); %<-- Mode 3
S = ttm(X,{pinv(U1),pinv(U2),pinv(U3)}); %<-- Core
Y = ttensor(S,{U1,U2,U3});
的函数f(x)定义为嵌套的x对象，然后处理展平< / strong>嵌套数据集。
这似乎只是
Dataset的用例，但我错了吗？
这是tf.py_func()的用例，但是您的程序有一个小错误：py_func op期望您的函数（tf.py_func()）返回NumPy数组，不< / strong> data_from_files()个对象。只需返回tf.Tensor和X即可。
回答这两点后，让我们来看看如何重写代码：
Y答案 1 :(得分：1)
当我使用tensorflow时，我有相同的错误（AttributeError：“ bytes”对象没有属性“ read”）。我做了这些事情：卸载numpy，删除“库\ site-packages”中有关numpy的文件，重新安装numpy。错误消失了。也许在我更新numpy的地方发生了一些错误。