“tuple”对象没有属性“train”

Question

我得到的答案是“元组”对象没有属性“火车”。我无法理解这个错误（我正在使用谷歌colab）。请帮助我，并尽可能解释更多细节（培训部分）。我的代码在下面。预先非常感谢

%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

您正在尝试访问train由 产生的元组中的属性cifar10.load_data()。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。如果您想实现next_batch如上所述的方法，则需要定义一个自定义帮助器类，这是非常常用的。