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jacquesqiao
reviewed
Dec 1, 2017
| cost = tf.nn.softmax_cross_entropy_with_logits(prediction, label) | ||
| avg_cost = tf.reduce_mean(cost) | ||
| adam_optimizer = tf.train.AdamOptimizer(learning_rate=0.002) | ||
| train_op = adam_optimizer.minimizer(avg_cost) |
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minimizer => minimize?
qingqing01
reviewed
Dec 5, 2017
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| initial_state = lstm_cell.zero_state(batch_size, dtype=tf.float32) | ||
| outputs, states = rnn.static_rnn( | ||
| lstm_cell, lstm_input, dtype=tf.float32) |
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这里看着只有一层lstm，可以设置多层stacked的lstm吗？
| train_reader = paddle.batch( | ||
| paddle.reader.shuffle( | ||
| paddle.dataset.imdb.train(word_dict), | ||
| buf_size=FLAGS.batch_size * 10), |
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IMDB traing set总共25 000，buf_size设置成25000吧，增加buf_size，应该可以加速度reader。
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I don't think there is any difference, and the factor caused by buffer should be removed in our validation scripts.
qingqing01
approved these changes
Dec 5, 2017
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fix PaddlePaddle/Paddle#6129