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我正在使用TensorFlow教程,该教程使用"怪异"格式上传数据.我想使用NumPy或pandas格式的数据,以便我可以将它与scikit-learn结果进行比较.

我从Kaggle获得了数字识别数据:https://www.kaggle.com/c/digit-recognizer/data .

这里是TensorFlow教程的代码(工作正常):

# Stuff from tensorflow tutorial 
import tensorflow as tf

sess = tf.InteractiveSession()

x = tf.placeholder("float", shape=[None, 784])
y_ = tf.placeholder("float", shape=[None, 10])

W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))

y = tf.nn.softmax(tf.matmul(x, W) + b)

cross_entropy = -tf.reduce_sum(y_ * tf.log(y))

train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)

在这里,我读取数据,去掉目标变量并将数据分成测试和训练数据集(这一切都正常):

# Read dataframe from training data
csvfile='train.csv'
from pandas import DataFrame, read_csv
df = read_csv(csvfile)

# Strip off the target data and make it a separate …