我遇到了tensorFlow的问题。

import tensorflow as tf
import input_data

learning_rate = 0.01
training_epochs = 25
batch_size = 100
display_step = 1

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

# tensorflow graph input
X = tf.placeholder('float', [None, 784]) # mnist data image of shape 28 * 28 = 784
Y = tf.placeholder('float', [None, 10]) # 0-9 digits recognition = > 10 classes

# set model weights
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))

# Our hypothesis
activation = tf.add(tf.matmul(X, W),b)  # Softmax

# Cost function: cross entropy
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=activation, logits=Y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)  # Gradient Descen

出现以下错误：
ValueError：没有为任何变量提供渐变，请检查您的图形中变量['Tensor（“Variable/read：0”，shape=（784，10），dtype= float 32;'，'Tensor（“Variable_1/read：0”，shape=（10，），dtype= float 32;']和损失Tensor（“Mean：0”，shape=（），dtype= float 32）之间不支持渐变的运算符。