# saver.restore(sess, ckpt_path) # sess.graph.as_default() # import numpy as np # in_m = sess.run(terminals['in_matr']) # np.savetxt("in_m.txt", in_m, delimiter="\t") # out_m = sess.run(terminals['out_matr']) # np.savetxt("out_m.txt", out_m, delimiter="\t") # sys.exit() for e in range(epochs): # batch = reader.next_batch() # first_batch = batch for ind, batch in enumerate(reader.batches()): in_words, out_words, labels = batch _, batch_count = sess.run([train_, adder_], { in_words_: in_words, out_words_: out_words, labels_: labels }) if batch_count % 1000 == 0: # in_words, out_words, labels = first_batch loss_val, summary, _ = sess.run([loss_, saveloss_, final_], { in_words_: in_words, out_words_: out_words, labels_: labels