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@JoshVarty
Last active Mar 14, 2018
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import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
test_images = np.reshape(mnist.test.images, (-1, 28, 28, 1))
test_labels = mnist.test.labels
graph = tf.Graph()
with tf.Session(graph=graph) as session:
saver = tf.train.import_meta_graph('/tmp/vggnet/vgg_net.ckpt.meta') #Create a saver based on a saved graph
saver.restore(session, '/tmp/vggnet/vgg_net.ckpt') #Restore the values to this graph
input = graph.get_tensor_by_name("input:0") #Get access to the input node
labels = graph.get_tensor_by_name("labels:0") #Get access to the labels node
batch_size = 100
num_test_batches = int(len(test_images) / 100)
total_accuracy = 0
total_cost = 0
for step in range(num_test_batches):
offset = (step * batch_size) % (test_labels.shape[0] - batch_size)
batch_images = test_images[offset:(offset + batch_size)]
batch_labels = test_labels[offset:(offset + batch_size)]
feed_dict = {input: batch_images, labels: batch_labels}
c, acc = session.run(['cost:0', 'accuracy:0'], feed_dict=feed_dict) #Note: We pass in strings 'cost:0' and 'accuracy:0'
total_cost = total_cost + c
total_accuracy = total_accuracy + acc
print("Test Cost: ", total_cost / num_test_batches)
print("Test accuracy: ", total_accuracy * 100.0 / num_test_batches, "%")
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