# summary_writer = tf.summary.FileWriter(os.path.join(model_hotel_path, 'plot_loss'), graph=sess.graph)
# summary_writer2 = tf.summary.FileWriter(os.path.join(model_hotel_path, 'plot_accuracy'), graph=sess.graph)
summary_writer = tf.summary.FileWriter(model_hotel_path, graph=sess.graph)
else:
summary_writer = tf.summary.FileWriter(model_fruit_path, graph=sess.graph)
# for each in tf.all_variables():
# print('each var', each)
# print('encoder_inputs_embedded',sess.run(encoder_inputs_embedded))
# summary_writer = tf.summary.FileWriter(model_path, graph=sess.graph)
for e in range(numEpochs):
print("----- Epoch {}/{} -----".format(e + 1, numEpochs))
batches = getBatches(trainingSamples, batch_size_flag)
for nextBatch in tqdm(batches, desc="Training"):
# for i in range(1):
# nextBatch=batches[-1]
batch_xs, batch_ys = nextBatch.encoder_inputs, nextBatch.decoder_targets
# 最后一个batch大小只有100，但是看到的是300所有有问题了；遇到最后一个batch时
# print('batch_xs, batch_ys shape',np.array(batch_xs).shape, np.array(batch_ys).shape)
# print('batch_xs, batch_ys\n',batch_xs,'\n',batch_ys)
'''
[[3, 3, 797, 1380, 3, 3, 2146, 3, 19740, 3, 230, 3035, 3, 10, 10, 3, 3, 734, 3, 2674, 111, 3, 308, 3058, 3, 10, 3, 3, 308, 3058, 3, 11, 3, 198, 3, 3, 320, 553, 3, 3, 3, 3, 3, 299, 3, 9737, 12852, 961, 3593, 11021, 1773, 3, 8590, 3, 2836, 3, 3, 433, 3, 12852, 2836, 3, 6096, 3, 8590, 3, 2836, 3, 9745, 580, 3, 3, 4587, 3, 641, 3, 3, 3, 25, 3, 3, 4743, 762, 3, 3, 129, 3, 7166, 7934, 3, 3, 1149, 3, 3, 3, 3, 9737, 12852, 961, 3593, 11021, 1773, 3, 8590, 3, 2836, 3, 266, 32, 3, 3, 2761, 3, 2707, 3, 3, 299, 3, 9737, 12852, 961, 3593, 11021, 1773, 3, 8590, 3, 2836, 3, 3, 433, 3, 198, 3, 3, 3, 4252, 3, 3, 1213, 710, 3, 9737, 12852, 961, 3593, 11021, 1773, 3, 8590, 3, 2836, 3, 308, 3, 9737, 12852, 961, 3593, 11021, 1773, 3, 8590, 3, 2836, 3, 3, 233, 3, 11, 3035, 3, 12852, 2836, 3, 6096, 3, 8590, 3, 2836, 3, 25, 3, 36, 3, 3, 10235, 3, 3, 2761], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3579, 3, 10, 3, 32, 2418, 3, 3, 23360, 3, 1084, 3, 3, 3, 8364, 3, 159, 32, 3, 961, 3, 32, 2418, 3, 291, 9848, 595, 3, 3, 3, 3, 7183, 5217, 3, 42, 3, 37, 3, 3, 3, 36, 3, 1677, 25, 3, 11694, 797, 3, 159, 32, 3, 2563, 1115, 3, 961, 3, 32, 2418, 3, 291, 9848, 3, 595, 3, 641, 3, 3, 32, 2418, 3, 3, 23360, 3, 11, 3035, 3, 641, 1115, 3, 320], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 11981, 3, 3, 2764, 433, 3, 2761, 3, 3, 1655, 2574, 3, 23388, 710, 3, 436, 3, 344, 3, 3, 13, 3, 3, 1711, 3, 215, 3, 10, 3, 159, 32], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 44, 3, 3, 3, 3, 3, 1630, 48, 3, 1084, 550, 3, 35, 3473, 3, 3, 3, 1634, 3, 3056, 3, 3, 141, 3, 4366, 21349, 3, 931, 695, 3, 3, 13886, 3, 3, 8364, 308, 3058, 3, 737, 3, 3, 339, 3, 643, 3693, 3, 1148, 10069, 3082, 3, 419, 3, 168, 3, 38, 3, 4753, 5630, 3, 1148, 10069, 3082, 3, 9737, 12852, 961, 3593, 11021, 1773, 3, 8590, 3, 2836, 3, 419, 3, 3, 38, 1148, 3, 32, 858, 3, 695, 3, 3822]]
[[1], [0], [0], [1]]
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 10285, 4987, 3, 3, 3, 3, 1616, 3, 3, 10270, 3, 11, 3035, 3, 3, 17273, 3, 17475, 1037, 3, 3, 92, 3, 13811, 6099, 3, 2563, 3, 3, 308, 3058, 3, 3, 2616, 3, 78, 3, 3, 10285, 4987, 3, 838, 3056, 3, 385, 3, 4198, 3, 3, 32, 3, 3, 10285, 4987, 3, 13, 3, 1616, 3, 159, 32, 3, 201, 797, 3, 3579, 1115, 3, 78, 3, 14604, 3, 3, 32, 3, 797, 2146, 3, 3874, 3874, 3, 10285, 4987, 3, 299, 3, 36, 3, 3, 4533, 3, 31, 3, 12033, 12387, 3, 3, 3, 3, 3, 838, 3, 664, 320, 3, 3, 3, 3, 3, 3, 3, 20108, 1859, 3, 37, 3, 3, 38, 31, 3, 3, 4324, 11437, 3, 1213, 2358, 3, 3, 3, 3, 3854, 1213, 3, 308, 3058], [3, 3, 95, 3, 3, 22, 3464, 3, 308, 3058, 3, 76, 3, 3, 797, 4151, 3, 11, 3, 3, 3, 3, 641, 149, 3, 3, 6164, 3, 3, 2486, 3, 579, 3, 3, 3, 3, 3, 3, 373, 3, 3, 3, 3, 111, 3, 3, 424, 3, 3, 233, 3, 24035, 215, 3, 1148, 3291, 3, 35, 3, 159, 32, 3, 797, 32, 3, 419, 3, 3, 3, 32, 711, 3, 23793, 3, 3, 266, 3, 9737, 12852, 961, 3593, 11021, 1773, 3, 8590, 3, 2836, 3, 3, 3, 3, 159, 32, 3, 159, 3, 111, 3, 3, 3, 3, 3, 11, 3, 3, 3, 3, 3, 159, 32, 3, 3, 3, 3, 11439, 3, 3, 2662, 1558, 3, 11439, 3, 3, 2662, 1558, 3, 3, 284, 3, 9745, 436, 3, 1037, 22, 3, 3, 2761, 3, 664, 320, 3, 159, 32, 3, 233, 3, 3, 3, 433, 3, 643, 3693, 3, 308, 3058, 3, 32, 3, 266, 3, 9737, 12852, 961, 3593, 11021, 1773, 3, 8590, 3, 2836, 3, 12852, 2836, 3, 6096, 3, 8590, 3, 2836, 3, 10285, 4987, 3, 44, 3, 3, 23793, 3, 3, 266, 3, 9737, 12852, 961, 3593, 11021, 1773, 3, 8590, 3, 2836, 3, 12852, 2836, 3, 6096, 3, 8590, 3, 2836, 3, 3, 342, 3, 3, 3, 3, 198, 3, 3, 3, 5273, 3, 3, 3, 3, 641, 3, 3], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 11, 3, 641, 149, 3, 1148, 3, 3, 1148, 32, 3, 3, 6398, 3, 3, 1092, 1334, 3, 1018, 547, 3, 99, 3, 1894, 3610, 3, 159, 32, 3, 451, 94, 3, 550, 3, 1655, 2574, 3, 1092, 1334, 3, 3, 3, 3, 3, 7719, 3, 4704, 1845, 3, 37, 3, 11, 3035, 3, 3, 6170, 3, 100, 3, 4479, 3, 3, 37, 3, 1092, 1334, 158, 3, 308, 3058, 3, 134, 3, 3, 1711, 3, 32, 2418, 3, 22182, 5296, 3, 78, 3, 3, 3, 4425, 3, 3, 3, 3, 1237, 3], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 2506, 3, 7293, 10885, 3, 3, 3, 3, 32, 3, 10033, 6132, 3, 10, 3, 44, 1380, 3, 22, 3464, 3, 1054, 3, 3, 3, 424, 3, 22, 3464, 3, 32, 3, 10033, 6132, 3, 22, 3464, 3, 22951, 550, 3, 1436, 1115, 3, 170]]
[1, 0, 1, 0]
# summary_writer = tf.summary.FileWriter(os.path.join(model_hotel_path, 'plot_loss'), graph=sess.graph)
# summary_writer2 = tf.summary.FileWriter(os.path.join(model_hotel_path, 'plot_accuracy'), graph=sess.graph)
summary_writer = tf.summary.FileWriter(model_hotel_path, graph=sess.graph)
else:
summary_writer = tf.summary.FileWriter(model_fruit_path, graph=sess.graph)
# for each in tf.all_variables():
# print('each var', each)
# print('encoder_inputs_embedded',sess.run(encoder_inputs_embedded))
# summary_writer = tf.summary.FileWriter(model_path, graph=sess.graph)
for e in range(numEpochs):
print("----- Epoch {}/{} -----".format(e + 1, numEpochs))
batches = getBatches(trainingSamples, batch_size_flag,training_flag=True)
for nextBatch in tqdm(batches, desc="Training"):
# for i in range(1):
# nextBatch=batches[-1]
batch_xs, batch_ys,batch_inputs_length = nextBatch.encoder_inputs, nextBatch.decoder_targets,nextBatch.encoder_inputs_length
# 最后一个batch大小只有100，但是看到的是300所有有问题了；遇到最后一个batch时
print('nextBatchs.encoder_inputs_length',nextBatch.encoder_inputs_length)
print([len(each) for each in batch_xs])
'''
'''