def __call__(self, sess, epoch, iteration, model, loss):
if iteration == 0 and epoch % self.at_every_epoch == 0:
total = 0
correct = 0
for values in self.batcher:
total += len(values[-1])
feed_dict = {}
for i in range(0, len(self.placeholders)):
feed_dict[self.placeholders[i]] = values[i]
truth = np.argmax(values[-1], 1)
predicted = sess.run(tf.arg_max(tf.nn.softmax(model), 1),
feed_dict=feed_dict)
correct += sum(truth == predicted)
acc = float(correct) / total
self.update_summary(sess, iteration, ACCURACY_TRACE_TAG, acc)
print("Epoch " + str(epoch) +
"\tAcc " + str(acc) +
"\tCorrect " + str(correct) + "\tTotal " + str(total))
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