اشکال در پرینت دقت دیتاست mnist با تنسورفلو

Question

با سلام. من کدی رو برای آشنایی با تنسورفلو نوشتم.اما وقتی می خواهم دقت روی نمونه های تست رو نشون بده کار اجرا رو متوقف میکنه.دلیلش رو اکه میدونید لفا راهنایی کنید. اینم کدمه

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data/', one_hot=True)

mnist.train.images.reshape(-1,28,28,1).shape

graph=tf.Graph()
with graph.as_default():
    batch_size = 128
    keep_prob=.5  #probability of dropout
    image_size=28
    num_labels=10
    patch_size=5
    num_channel=1    #grayscale
    depth=16


    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')


    # input data, we use placeholders that will be fed
    tf_train_dataset = tf.placeholder(tf.float32, shape=(batch_size, image_size, image_size, num_channel))
    tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))
    tf_valid_dataset = tf.constant(mnist.validation.images.reshape(-1, 28, 28, 1))
    tf_test_dataset = tf.constant(mnist.test.images.reshape(-1, 28, 28, 1))
    keep_prob = tf.placeholder(tf.float32)
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])

def model(data):
    h_conv1 = tf.nn.relu(conv2d(data, W_conv1) + b_conv1)
    h_pool1 = max_pool_2x2(h_conv1)
    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
    h_pool2 = max_pool_2x2(h_conv2)
    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)
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
    return tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

# training computations
logits = model(tf_train_dataset)
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels= tf_train_labels))

# Optimizer
optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)

# Prediction for the training validation and test data
train_prediction = tf.nn.softmax(logits)
valid_prediction = tf.nn.softmax(model(tf_valid_dataset))
test_prediction = tf.nn.softmax(model(tf_test_dataset))

def accuracy(predictions, labels):
    return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1)) / predictions.shape[0])

num_steps = 8001
with tf.Session(graph=graph, config=tf.ConfigProto(log_device_placement=True)) as sess:
    #tf.initialize_all_variables().run()
    sess.run(tf.global_variables_initializer())
    print("Initialized")
    for step in range(num_steps):
        batch_data, batch_labels = mnist.train.next_batch(batch_size)
        # reshape
        batch_data = batch_data.reshape(-1, 28, 28, 1)
        feed_dict = {tf_train_dataset: batch_data, tf_train_labels: batch_labels, keep_prob: 0.5}
        _, l, predictions = sess.run([optimizer, loss, train_prediction], feed_dict=feed_dict)
        if (step % 500 == 0):
            print("minibatch loss at step %d: %f" % (step, l))
            print("minibatch accuracy: %.1f%%" % accuracy(predictions, batch_labels))
            print("validation accuracy: %.1f%%" % accuracy(valid_prediction.eval
                                (feed_dict={keep_prob: 1}),mnist.validation.labels))
            print("Test accuracy: %.1f%%" % accuracy(test_prediction.eval
                                (feed_dict={keep_prob: 1}),mnist.test.labels))