TensorFlow培訓圖片

我最近學習了TensorFlow，想將我的圖片導入TensorFlow進行培訓，但是我被卡住了。下面是我的代碼TensorFlow培訓圖片

import tensorflow as tf 

tf.device(0) 

def read_and_decode(filename): 
    filename_queue = tf.train.string_input_producer([filename]) 
    reader = tf.TFRecordReader() 
    _, serialized_example = reader.read(filename_queue) 
    features = tf.parse_single_example(
      serialized_example, 
      features={ 
       'label': tf.FixedLenFeature([], tf.int64), 
       'img_raw': tf.FixedLenFeature([], tf.string), 
      }) 
    img = tf.decode_raw(features['img_raw'], tf.uint8) 
    img = tf.reshape(img, [100, 100, 3]) 
    img = tf.cast(img, tf.float32) * (1./255) - 0.5 
    lbl = tf.cast(features['label'], tf.int32) 
    return img, lbl 

image, label = read_and_decode('/Users/Cody/PycharmProjects/TensorFlowStartUp/train.tfrecords') 
img_batch, label_batch = tf.train.shuffle_batch([image, label], 
               batch_size=5, capacity=5, 
               min_after_dequeue=2) 

x = tf.placeholder(tf.float32, [None, 30000]) 
y_actual = tf.placeholder(tf.float32, shape=[None, 8]) 
W = tf.Variable(tf.zeros([30000,8])) 
b = tf.Variable(tf.zeros([8])) 
y_predict = tf.nn.softmax(tf.matmul(x,W) + b) 
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_actual*tf.log(y_predict),reduction_indices=1)) 
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy) 

correct_prediction = tf.equal(tf.argmax(y_predict,1), tf.argmax(y_actual,1)) 
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) 

init = tf.global_variables_initializer() 
with tf.Session() as sess: 
    sess.run(init) 
    for i in range(100): 
     batch_xs = image 
     batch_ys = label 
     sess.run(train_step, feed_dict={x: batch_xs, y_actual: batch_ys}) 
     if(i%10==0): 
      print "accuracy:",sess.run(accuracy, feed_dict={x: image, y_actual: label})

當我運行代碼，我得到了錯誤的味精如下：

Traceback (most recent call last): File "/home/hadoop/PycharmProjects/TensorFlow/Test.py", line 43, in sess.run(train_step, feed_dict={x: batch_xs, y_actual: batch_ys}) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 767, in run run_metadata_ptr) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 925, in _run raise TypeError('The value of a feed cannot be a tf.Tensor object. ' TypeError: The value of a feed cannot be a tf.Tensor object. Acceptable feed values include Python scalars, strings, lists, or numpy ndarrays.

我不知道如何把我的代碼的權利。

x = tf.placeholder(tf.float32, [None, 30000]) 
y_actual = tf.placeholder(tf.float32, shape=[None, 8]) 
W = tf.Variable(tf.zeros([30000,8])) 
b = tf.Variable(tf.zeros([8]))

爲X，y_actual，W，B 我應該怎樣輸入我的情況？

非常感謝您的幫助

來源

2017-03-28 iamcodylee

而是創建一個佔位符，你應該使用'label'和'image'直接，因爲這些都是張量的值。 –

您能告訴我如何編輯我的代碼以使其運行？我不知道如何編輯代碼，因爲我只是按照文檔做 – iamcodylee

你image和label變量張量
你不能給張值在你喂
你的飼料必須是一個常規的Python數據結構一樣numpy的陣列，int等
您正在使用圖像，因此image變量必須是形狀爲[None, 3000]的凹凸陣列，您在輸入張量x = tf.placeholder(tf.float32, [None, 30000])中定義了該陣列。
您label變量也必須形狀[None, 8]的numpy的數組，你在輸入張量定義x = tf.placeholder(tf.float32, [None, 8])
在你的代碼，你想使你的image變量從張成numpy的陣列，試試這個：batch_xs = sess.run(image) - 你也想通過您的張可變label成numpy的陣列，試試這個：batch_ys = sess.run(label)

來源

2017-12-30 23:08:54 Jai

TensorFlow培訓圖片

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