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در برنامه MNIST For ML Beginners به صورت زیر:




from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
import tensorflow as tf
sess = tf.InteractiveSession()

x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10])

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

sess.run(tf.global_variables_initializer())

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

cross_entropy = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))

train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

for _ in range(1000):
  batch = mnist.train.next_batch(100)
  train_step.run(feed_dict={x: batch[0], y_: batch[1]})

correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

print(accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels}))



اگر بخواهیم به جای دادگان فوق تصاویر مثلا 3*20*20 را جایگزین کنیم:


تعریف داده آموزش training _data  با برچسب train_label به صورت زیر:

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
try:
    from scipy import misc
except ImportError:
    !pip install scipy
    from scipy import misc

training_size = 9
img_size = 20*20*3
training_data = np.empty(shape=(training_size, img_size))

import glob
i = 0
for filename in glob.glob('D:/Minutia/*.jpg'):
    image = misc.imread(filename)
    training_data[i] = image.reshape(-1)
    i+=1
print(training_data[0].shape)

a = [0, 0, 0,1,1,1,2,2,2]
train_label = tf.one_hot(a,3)
sess = tf.Session()
sess.run(train_label)



test_size = 3
img_size = 20*20*3
test_data = np.empty(shape=(test_size, img_size))

import glob
i = 0
for filename in glob.glob('D:/Wrinkle/*.jpg'):
    image = misc.imread(filename)
    test_data[i] = image.reshape(-1)
    i+=1
print(test_data[0].shape)

a = [0, 1,2]
test_label = tf.one_hot(a,3)
sess = tf.Session()
sess.run(test_label)


x = tf.placeholder(tf.float32, shape=[None, 400])
y_ = tf.placeholder(tf.float32, shape=[None, 3])

W = tf.Variable(tf.zeros([400,3]))
b = tf.Variable(tf.zeros([3]))

sess.run(tf.global_variables_initializer())

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





cross_entropy = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))

train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

for _ in range(1000):
  batch = training_data.next_batch(100)
  train_step.run(feed_dict={x: batch[0], y_: batch[1]})

correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

print(accuracy.eval(feed_dict={x: test_data, y_: test_label}))




آیا برنامه فوق درست است؟من موقع اجرا با خطای زیر مواجه می شم...

 'numpy.ndarray' object has no attribute 'next_batch'