def compute_predictions(sess, x, preds_op, images, batch_size):
images = images[:100]
preds = run_batches(sess, preds_op, [x], [images], batch_size)
print(preds)
def compute_logits(sess, x, logits_op, images, batch_size):
images = images[:100]
logits = run_batches(sess, logits_op, [x], [images], batch_size)
for l in logits:
print(l)
def compute_predictions(sess, x, preds_op, images, batch_size):
# images = images[:100]
return run_batches(sess, preds_op, [x], [images], batch_size)
def evaluate(sess, x, y, pred_op, images, labels, batch_size):
preds = run_batches(sess, pred_op, [x], [images], batch_size)
acc = np.sum(preds == labels) / len(labels)
print("accuracy: %0.04f" % acc)
def compute_logits(sess, x, logits_op, images, batch_size):
# images = images[:100]
return run_batches(sess, logits_op, [x], [images], batch_size)