Tensorflow FailedPreconditionError: Attempting to use uninitialized value Variable - tensorflow

Question

I follow the instruction of 'Build a Multilayer Convolutional Network' on the official website. My code is exactly the same as the code they provide on the website. [https://www.tensorflow.org/get_started/mnist/pros]
I also remember calling initializing global variables.
However, error arises.
But if I change tf.Session() to tf.InteractiveSession(), it works.
What's wrong here? Thanks in advance.
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
W_conv1 = weight_variable([5,5,1,32])
b_conv1 = bias_variable([32])
x_image = tf.reshape(x, [-1,28,28,1])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1)+b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
W_conv2 = weight_variable([5,5,32,64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2)+b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
W_fc1 = weight_variable([7*7*64,1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv = tf.matmul(h_fc1_drop, W_fc2)+b_fc2
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
for i in range(100):
batch = mnist.train.next_batch(50)
if i % 100 == 0:
train_accuracy = accuracy.eval(feed_dict={
x:batch[0], y_:batch[1], keep_prob:1})
print('step %d, training accuracy %g' % (i, train_accuracy))
train_step.run(feed_dict={
x:batch[0], y_:batch[1], keep_prob:0.5})
print('test accuracy %g' % accuracy.eval(feed_dict={
x: mnist.test.images, y_:mnist.test.labels, keep_prob: 1.0}))

Answer 1

with tf.Session() as sess:
...
print('test accuracy %g' % accuracy.eval(feed_dict={
x: mnist.test.images, y_:mnist.test.labels, keep_prob: 1.0}))
when use tf.Session You should put print method in the with block for setting sess when run eval.
For InteractiveSession it will set the default session, so you can excute eval and run with this default session.