当我试图用tensorflow完成softmax回归时，出现了一些问题如下：
tensorflow.python.framework.errors_impl.InvalidArgumentError：您必须使用dtype float [[Node：Placeholder_1 = Placeholderdtype = DT_FLOAT，shape = []，_ device =“/ job：localhost / replica）为占位符张量'Placeholder_1'提供值： 0 /任务：0 / CPU：0" ]]
从上面的描述中，我理解问题是参数类型错误 . 但在我的代码中，我的数据类型与占位符相同 .
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
m = input_data.read_data_sets("MNIST_data/", one_hot=True)
sess = tf.InteractiveSession()
x = tf.placeholder(tf.float32, [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(tf.float32, [None, 10])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
tf.global_variables_initializer()
for i in range(1000):
batch_xs, batch_ys = m.train.next_batch(100)
train_step.run({x: batch_xs, y: batch_ys})
correct_prediction = tf.equal(tf.arg_max(y, 1), tf.arg_max(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(accuracy.eval({x: m.test.images, y: m.test.labels}))
I think the problem is caused by the type of batch_xs(float32) and batch_ys(float32).
关于如何解决这个问题的任何建议？
1 回答
问题是由于您将
y而不是y_传递到accuracy.eval调用的feed_dict中 .这样，您将覆盖
y的值，并且不使用占位符y_.只需将行更改为