我刚开始研究Tensorflow，我在训练数据时遇到了一个问题。 我的问题是阅读csv文件，然后使用softmax分类根据他们的学习时间和出勤率来评估学生的成绩（A，B或C）。
我定义，然后将csv文件加载为
Security.h之后我定义了功能的张量，并像这样标记
COLUMNS = ["studytime", "attendance", "A", "B", "C"]
FEATURES = ["studytime", "attendance"]
LABEL = ["A", "B", "C"]
training_set = pd.read_csv("hw1.csv", skipinitialspace=True,
skiprows=1, names=COLUMNS)
然后我按照以下方式训练softmax和MNIST数据 Tensorflow for MNIST
但我不知道如何定义 batch_xs 和 batch_ys 来训练此循环
feature_cols = [tf.contrib.layers.real_valued_column(k) for k in FEATURES]
labels = [tf.contrib.layers.real_valued_column(k) for k in LABEL]
如果他们的学习和参与时间如何，我如何定义三个学生的分数估计的功能，例如，[11,7]，[3,4]，[1,0]
你能帮我弄清楚这个问题吗？
提前致谢，
答案 0 :(得分：0)
您好像正在将CSV读入DataFrame？您当然可以手动实现批处理过程，但有一种有效的内置方法可以在TF中构建队列和批处理。它有点复杂，但它适用于顺序或随机改组提供行，这非常方便。只需确保您的行长度相等，这样您就可以轻松指定哪些卖出代表Xes，哪些卖出代表Y.
您需要的两个功能是tf.decode_csv和tf.train.shuffle_batch（或tf.train.batch，如果您不需要随机改组）。
我们在这篇文章中详细讨论了这个问题，其中包括一个完整的代码示例： TF CSV Batching Example
看起来你的数据都是数字的，Y是单热格式的，所以MNIST的例子应该适合实现你的估算函数。
***更新：
这大致是操作的顺序：
1.定义链接示例中显示的两个函数 - 一个用于逐行读取CSV文件，另一个用于将每个行打包成N个批次（随机或顺序）
2.通过while not coord.should_stop():开始阅读循环此循环将一直运行，直到它耗尽您提供给队列的所有CSV文件的内容
3.在循环的每次迭代中，对这些变量执行sess.run会为您提供批量的X和Y，以及您可能希望从CSV文件的每一行获得的任何额外的元类型内容，例如日期 - 在这个例子中的标签（在你的情况下，它可能是学生的名字或其他：
dateLbl_batch, feature_batch, label_batch = sess.run([dateLbl, features, labels])
当TF到达文件的末尾时，它会抛出一个异常，这就是为什么上面所有代码都在try / catch块中 - 通过捕获该异常，你就知道你已经完成了。
上述功能为您提供了对CSV文件进行非常精细的逐个单元级别访问，并允许您将它们批量分批为N批次，分成您想要的历元数等。
*****更新2 **
以下是您应该以您的格式批量读取CSV文件的完整代码。它只是打印每批的内容。从这里，您可以轻松地将此代码与实际执行培训等的代码相关联。
import tensorflow as tf
fileName = 'data/study.csv'
try_epochs = 1
batch_size = 3
S = 1 # this is your Student label
F = 2 # this is the list of your features
L = 3 # this is one-hot vector of 3 representing the label
# set defaults to something (TF requires defaults for the number of cells you are going to read)
rDefaults = [['a'] for row in range((S+F+L))]
# function that reads the input file, line-by-line
def read_from_csv(filename_queue):
reader = tf.TextLineReader(skip_header_lines=True) # skipt the header line
_, csv_row = reader.read(filename_queue) # read one line
data = tf.decode_csv(csv_row, record_defaults=rDefaults) # use defaults for this line (in case of missing data)
studentLbl = tf.slice(data, [0], [S]) # first cell is my 'date-label' for internal pruposes
features = tf.string_to_number(tf.slice(data, [S], [F]), tf.float32) # cells 2-480 is the list of features
label = tf.string_to_number(tf.slice(data, [S+F], [L]), tf.float32) # the remainin 3 cells is the list for one-hot label
return studentLbl, features, label
# function that packs each read line into batches of specified size
def input_pipeline(fName, batch_size, num_epochs=None):
filename_queue = tf.train.string_input_producer(
[fName],
num_epochs=num_epochs,
shuffle=True) # this refers to multiple files, not line items within files
dateLbl, features, label = read_from_csv(filename_queue)
min_after_dequeue = 10000 # min of where to start loading into memory
capacity = min_after_dequeue + 3 * batch_size # max of how much to load into memory
# this packs the above lines into a batch of size you specify:
dateLbl_batch, feature_batch, label_batch = tf.train.shuffle_batch(
[dateLbl, features, label],
batch_size=batch_size,
capacity=capacity,
min_after_dequeue=min_after_dequeue)
return dateLbl_batch, feature_batch, label_batch
# these are the student label, features, and label:
studentLbl, features, labels = input_pipeline(fileName, batch_size, try_epochs)
with tf.Session() as sess:
gInit = tf.global_variables_initializer().run()
lInit = tf.local_variables_initializer().run()
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
try:
while not coord.should_stop():
# load student-label, features, and label as a batch:
studentLbl_batch, feature_batch, label_batch = sess.run([studentLbl, features, labels])
print(studentLbl_batch);
print(feature_batch);
print(label_batch);
print('----------');
except tf.errors.OutOfRangeError:
print("Done looping through the file")
finally:
coord.request_stop()
coord.join(threads)
假设您的CSV文件如下所示：
name studytime attendance A B C
S1 2 1 0 1 0
S2 3 2 1 0 0
S3 4 3 0 0 1
S4 3 5 0 0 1
S5 4 4 0 1 0
S6 2 1 1 0 0
上面的代码应该打印以下输出：
[[b'S5']
[b'S6']
[b'S3']]
[[ 4. 4.]
[ 2. 1.]
[ 4. 3.]]
[[ 0. 1. 0.]
[ 1. 0. 0.]
[ 0. 0. 1.]]
----------
[[b'S2']
[b'S1']
[b'S4']]
[[ 3. 2.]
[ 2. 1.]
[ 3. 5.]]
[[ 1. 0. 0.]
[ 0. 1. 0.]
[ 0. 0. 1.]]
----------
Done looping through the file
因此，不要打印批次的内容，只需将它们用作X和Y，以便在feed_dict
答案 1 :(得分：0)
这是我的尝试。但准确性并不像我预期的那么高。
import tensorflow as tf
fileName = 'hw1.csv'
try_epochs = 1
batch_size = 8
S = 1 # this is your Student label
F = 2 # this is the list of your features
L = 3 # this is one-hot vector of 3 representing the label
# set defaults to something (TF requires defaults for the number of cells you are going to read)
rDefaults = [['a'] for row in range((S+F+L))]
# function that reads the input file, line-by-line
def read_from_csv(filename_queue):
reader = tf.TextLineReader(skip_header_lines=True) # skipt the header line
_, csv_row = reader.read(filename_queue) # read one line
data = tf.decode_csv(csv_row, record_defaults=rDefaults) # use defaults for this line (in case of missing data)
studentLbl = tf.slice(data, [0], [S]) # first cell is my 'date-label' for internal pruposes
features = tf.string_to_number(tf.slice(data, [S], [F]), tf.float32) # cells 2-480 is the list of features
label = tf.string_to_number(tf.slice(data, [S+F], [L]), tf.float32) # the remainin 3 cells is the list for one-hot label
return studentLbl, features, label
# function that packs each read line into batches of specified size
def input_pipeline(fName, batch_size, num_epochs=None):
filename_queue = tf.train.string_input_producer(
[fName],
num_epochs=num_epochs,
shuffle=True) # this refers to multiple files, not line items within files
dateLbl, features, label = read_from_csv(filename_queue)
min_after_dequeue = 10000 # min of where to start loading into memory
capacity = min_after_dequeue + 3 * batch_size # max of how much to load into memory
# this packs the above lines into a batch of size you specify:
dateLbl_batch, feature_batch, label_batch = tf.train.shuffle_batch(
[dateLbl, features, label],
batch_size=batch_size,
capacity=capacity,
min_after_dequeue=min_after_dequeue)
return dateLbl_batch, feature_batch, label_batch
# these are the student label, features, and label:
studentLbl, features, labels = input_pipeline(fileName, batch_size,
try_epochs)
x = tf.placeholder(tf.float32, [None, 2])
W = tf.Variable(tf.zeros([2, 3]))
b = tf.Variable(tf.zeros([3]))
y = tf.nn.softmax(tf.matmul(x, W) + b)
y_ = tf.placeholder(tf.float32, [None, 3])
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_,logits=y))
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(cross_entropy)
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
with tf.Session() as sess:
gInit = tf.global_variables_initializer().run()
lInit = tf.local_variables_initializer().run()
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
try:
while not coord.should_stop():
# load student-label, features, and label as a batch:
studentLbl_batch, feature_batch, label_batch = sess.run([studentLbl, features, labels])
print(studentLbl_batch);
print(feature_batch);
print(label_batch);
print('----------');
batch_xs = feature_batch
batch_ys = label_batch
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) # feeding data
except tf.errors.OutOfRangeError:
print("Done looping through the file")
finally:
coord.request_stop()
coord.join(threads)
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict={x: feature_batch, y_: label_batch}))
print(sess.run(W))
print(sess.run(b))
准确度
0.375
W，B
[[ 0.00555556 0.00972222 -0.01527778] [ 0.00555556 0.01388889 -0.01944444]]
[-0.00277778 0.00138889 0.00138889]