best python programs
import numpy as np
import tensorflow as tf
from include.data import get_data_set
from include.model import model
test_x, test_y = get_data_set("test")
x, y, output, y_pred_cls, global_step, learning_rate = model()
_BATCH_SIZE = 128
_CLASS_SIZE = 10
_SAVE_PATH = "./tensorboard/cifar-10-v1.0.0/"
saver = tf.train.Saver()
sess = tf.Session()
try:
print("
Trying to restore last checkpoint ...")
last_chk_path = tf.train.latest_checkpoint(checkpoint_dir=_SAVE_PATH)
saver.restore(sess, save_path=last_chk_path)
print("Restored checkpoint from:", last_chk_path)
except ValueError:
print("
Failed to restore checkpoint. Initializing variables instead.")
sess.run(tf.global_variables_initializer())
def main():
i = 0
predicted_class = np.zeros(shape=len(test_x), dtype=np.int)
while i < len(test_x):
j = min(i + _BATCH_SIZE, len(test_x))
batch_xs = test_x[i:j, :]
batch_ys = test_y[i:j, :]
predicted_class[i:j] = sess.run(y_pred_cls, feed_dict={x: batch_xs, y: batch_ys})
i = j
correct = (np.argmax(test_y, axis=1) == predicted_class)
acc = correct.mean() * 100
correct_numbers = correct.sum()
print()
print("Accuracy on Test-Set: {0:.2f}% ({1} / {2})".format(acc, correct_numbers, len(test_x)))
if __name__ == "__main__":
main()
sess.close()
