如何测试tensorflow cifar10 cifar10教程模型

Question

我对机器学习相对较新，目前几乎没有开发它的经验。

因此，我的问题是:在培训和评估来自tensorflow 教程的cifar10数据集之后，我想知道如何用示例图像来测试它？

我可以训练和评估caffe机器学习框架的Imagenet教程，使用python在定制应用程序上使用经过训练的模型是相对容易的。

任何帮助都将不胜感激！

Answer 1

这不是问题的100%的答案，但这是一种类似的解决方法，基于MNIST神经网络训练的例子，建议在对问题的评论。

基于TensorFlow乞讨者MNIST教程，感谢本教程，这是一种训练和使用自定义数据的神经网络的方法。

请注意，类似的教程，如CIFAR10，@Yaroslav在评论中提到。

import input_data
import datetime
import numpy as np
import tensorflow as tf
import cv2
from matplotlib import pyplot as plt
import matplotlib.image as mpimg
from random import randint


mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

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

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

y = tf.nn.softmax(tf.matmul(x,W) + b)
y_ = tf.placeholder("float", [None,10])

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

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

init = tf.initialize_all_variables()

sess = tf.Session()
sess.run(init)

#Train our model
iter = 1000
for i in range(iter):
  batch_xs, batch_ys = mnist.train.next_batch(100)
  sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})

#Evaluationg our model:
correct_prediction=tf.equal(tf.argmax(y,1), tf.argmax(y_,1))

accuracy=tf.reduce_mean(tf.cast(correct_prediction,"float"))
print "Accuracy: ", sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})

#1: Using our model to classify a random MNIST image from the original test set:
num = randint(0, mnist.test.images.shape[0])
img = mnist.test.images[num]

classification = sess.run(tf.argmax(y, 1), feed_dict={x: [img]})
'''
#Uncomment this part if you want to plot the classified image.
plt.imshow(img.reshape(28, 28), cmap=plt.cm.binary)
plt.show()
'''
print 'Neural Network predicted', classification[0]
print 'Real label is:', np.argmax(mnist.test.labels[num])


#2: Using our model to classify MNIST digit from a custom image:

# create an an array where we can store 1 picture
images = np.zeros((1,784))
# and the correct values
correct_vals = np.zeros((1,10))

# read the image
gray = cv2.imread("my_digit.png", 0 ) #0=cv2.CV_LOAD_IMAGE_GRAYSCALE #must be .png!

# rescale it
gray = cv2.resize(255-gray, (28, 28))

# save the processed images
cv2.imwrite("my_grayscale_digit.png", gray)
"""
all images in the training set have an range from 0-1
and not from 0-255 so we divide our flatten images
(a one dimensional vector with our 784 pixels)
to use the same 0-1 based range
"""
flatten = gray.flatten() / 255.0
"""
we need to store the flatten image and generate
the correct_vals array
correct_val for a digit (9) would be
[0,0,0,0,0,0,0,0,0,1]
"""
images[0] = flatten


my_classification = sess.run(tf.argmax(y, 1), feed_dict={x: [images[0]]})

"""
we want to run the prediction and the accuracy function
using our generated arrays (images and correct_vals)
"""
print 'Neural Network predicted', my_classification[0], "for your digit"

为了进一步的图像调理(数字应该是完全黑暗的白色背景)和更好的神经网络训练(accuracy>91%)，请检查高级TensorFlow教程或我已经提到的第二教程。

Answer 2

我建议在基本MNIST教程网站上看一看TensorFlow。看起来，您定义了一些函数，该函数生成所需的输出类型，然后运行会话，将此评估函数传递给它(下面是correct_prediction)，以及包含所需参数的字典(下面是x和y_ )。

如果您已经定义并培训了一些接受输入x的网络，根据您的输入生成响应y，并且您知道您对测试集y_的预期响应，那么您可以打印出对测试集的每个响应，如下所示：

correct_prediction = tf.equal(y, y_)  % Check whether your prediction is correct
print(sess.run(correct_prediction, feed_dict={x: test_images, y_: test_labels}))

这只是对本教程中所做工作的一个修改，在这个过程中，他们不是试图打印每个响应，而是确定正确响应的百分比。还请注意，本教程使用一个热门向量作为预测y和实际值y_，因此为了返回相关的数字，必须找到这些向量的哪个索引等于tf.argmax(y, 1)的索引。

编辑

通常，如果在图中定义了什么，则可以在运行图形时稍后输出。假设您将在输出逻辑上确定softmax函数的结果定义为：

graph = tf.Graph()
with graph.as_default():
  ...
  prediction = tf.nn.softmax(logits)
  ...

然后，您可以在运行时输出以下内容：

with tf.Session(graph=graph) as sess:
  ...
  feed_dict = { ... }  # define your feed dictionary
  pred = sess.run([prediction], feed_dict=feed_dict)
  # do stuff with your prediction vector

Answer 3

下面的示例不是针对mnist教程的，而是一个简单的XOR示例。注意train()和test()方法。我们在全球范围内声明和保持的是权重、偏差和会话。在测试方法中，我们重新定义输入的形状，并重用我们在训练中改进的相同的权重&偏差(和会话)。

import tensorflow as tf

#parameters for the net
w1 = tf.Variable(tf.random_uniform(shape=[2,2], minval=-1, maxval=1, name='weights1'))
w2 = tf.Variable(tf.random_uniform(shape=[2,1], minval=-1, maxval=1, name='weights2'))

#biases
b1 = tf.Variable(tf.zeros([2]), name='bias1')
b2 = tf.Variable(tf.zeros([1]), name='bias2')

#tensorflow session
sess = tf.Session()


def train():

    #placeholders for the traning inputs (4 inputs with 2 features each) and outputs (4 outputs which have a value of 0 or 1)
    x = tf.placeholder(tf.float32, [4, 2], name='x-inputs')
    y = tf.placeholder(tf.float32, [4, 1], name='y-inputs')

    #set up the model calculations
    temp = tf.sigmoid(tf.matmul(x, w1) + b1)
    output = tf.sigmoid(tf.matmul(temp, w2) + b2)

    #cost function is avg error over training samples
    cost = tf.reduce_mean(((y * tf.log(output)) + ((1 - y) * tf.log(1.0 - output))) * -1)

    #training step is gradient descent
    train_step = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(cost)

    #declare training data
    training_x = [[0,1], [0,0], [1,0], [1,1]]
    training_y = [[1], [0], [1], [0]]

    #init session
    init = tf.initialize_all_variables()
    sess.run(init)

    #training
    for i in range(100000):
        sess.run(train_step, feed_dict={x:training_x, y:training_y})

        if i % 1000 == 0:
            print (i, sess.run(cost, feed_dict={x:training_x, y:training_y}))

    print '\ntraining done\n'


def test(inputs):
    #redefine the shape of the input to a single unit with 2 features
    xtest = tf.placeholder(tf.float32, [1, 2], name='x-inputs')

    #redefine the model in terms of that new input shape
    temp = tf.sigmoid(tf.matmul(xtest, w1) + b1)
    output = tf.sigmoid(tf.matmul(temp, w2) + b2)

    print (inputs, sess.run(output, feed_dict={xtest:[inputs]})[0, 0] >= 0.5)


train()

test([0,1])
test([0,0])
test([1,1])
test([1,0])