"元组"对象没有属性"train"

Question

我得到的答案是"tuple"对象没有属性"train"。我不能理解这个错误(我正在使用谷歌colab(。请帮助我，并尽可能详细地解释(培训部分(。我的代码在下面。提前感谢

%tensorflow_version 1.x
## loading nessecery functions and CIFAR10 dataset 
from __future__ import print_function
import tensorflow as tf
from tensorflow.keras.datasets import cifar10
tf.__version__
((train_X, train_y), (test_X, test_y)) = cifar10.load_data()
print(f"train_X: {train_X.shape}, test_X = {test_X.shape}")
cifar10 = cifar10.load_data()
# define placeholder for inputs to network
X = tf.placeholder(tf.float32, [None, 3072])/255.0   # 32x32x3
Y = tf.placeholder(tf.float32, [None, 10])
keep_prob = tf.placeholder(tf.float32)
learning_rate = 0.001
training_epochs =10
batch_size = 30
# weights & bias for nn layers
W = tf.Variable(tf.random_normal([3072, 10]))
b = tf.Variable(tf.random_normal([10]))
hypothesis = tf.matmul(X, W) + b
# define cost/loss & optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=hypothesis, labels=Y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
# initialize
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# train my model
for epoch in range(training_epochs):
avg_cost = 0
num_examples = 50000
total_batch = int(num_examples / batch_size)

我的问题在这里

for i in range(total_batch):
batch_xs, batch_ys = cifar10.train.next_batch(batch_size)
feed_dict = {X: batch_xs, Y: batch_ys}
c, _ = sess.run([cost, optimizer], feed_dict=feed_dict)
avg_cost += c / total_batch
print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost))
print('Learning Finished!')

# Test model and check accuracy
correct_prediction = tf.equal(tf.argmax(hypothesis, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print('Accuracy:', sess.run(accuracy, feed_dict={X: cifar10.test.images, Y: cifar10.test.labels}))

Answer 1

您正试图从cifar10.load_data()产生的元组中访问train属性。您已经在上一步中正确加载了数据：

(x_train, y_train), (x_test, y_test) = cifar10.load_data()

cifar10.load_data()是一个数据加载器，它返回数据集的训练集和测试集。

如果你想实现一个next_batch方法来完成上面的工作，你需要定义一个自定义的助手类，这是非常常用的东西。下面是一个例子。