输出结果
tensorboard可视化
iter: 0 loss: 0.010328549
iter: 500 loss: 0.0044991444
iter: 1000 loss: 0.003714567
iter: 1500 loss: 0.0033356838
iter: 2000 loss: 0.003116763
iter: 2500 loss: 0.0029606873
iter: 3000 loss: 0.0028696475
iter: 3500 loss: 0.0026985144
iter: 4000 loss: 0.0025833827
iter: 4500 loss: 0.0024938423
iter: 5000 loss: 0.0024183288
iter: 5500 loss: 0.0023511213
iter: 6000 loss: 0.0022882319
iter: 6500 loss: 0.0022265154
iter: 7000 loss: 0.002163515
iter: 7500 loss: 0.0020974649
iter: 8000 loss: 0.0020275544
iter: 8500 loss: 0.0019528335
iter: 9000 loss: 0.0018700107
iter: 9500 loss: 0.0017752206
iter: 10000 loss: 0.0016714178
iter: 10500 loss: 0.0015757289
iter: 11000 loss: 0.0015021019
iter: 11500 loss: 0.0014435991
iter: 12000 loss: 0.0013950231
iter: 12500 loss: 0.0013551206
iter: 13000 loss: 0.0013215576
iter: 13500 loss: 0.0012917771
iter: 14000 loss: 0.0012640483
iter: 14500 loss: 0.0012376485
iter: 15000 loss: 0.0012124979
iter: 15500 loss: 0.0011886061
iter: 16000 loss: 0.0011660281
iter: 16500 loss: 0.0011447266
iter: 17000 loss: 0.0011244208
iter: 17500 loss: 0.001104528
iter: 18000 loss: 0.0010844271
iter: 18500 loss: 0.0010633252
iter: 19000 loss: 0.0010399523
iter: 19500 loss: 0.001011961
iter: 20000 loss: 0.00097585854
iter: 20500 loss: 0.00093142985
iter: 21000 loss: 0.00089110696
iter: 21500 loss: 0.00086476567
iter: 22000 loss: 0.00084816053
iter: 22500 loss: 0.0008364689
iter: 23000 loss: 0.00082719745
iter: 23500 loss: 0.000819149
iter: 24000 loss: 0.00081174297
iter: 24500 loss: 0.00080478605
iter: 25000 loss: 0.0007982892
iter: 25500 loss: 0.00079225213
iter: 26000 loss: 0.0007866463
iter: 26500 loss: 0.0007813923
iter: 27000 loss: 0.00077644055
iter: 27500 loss: 0.00077167765
iter: 28000 loss: 0.00076701824
iter: 28500 loss: 0.0007624052
iter: 29000 loss: 0.00075781584
iter: 29500 loss: 0.00075323426
iter: 30000 loss: 0.0007487352
iter: 30500 loss: 0.00074437447
iter: 31000 loss: 0.000740188
iter: 31500 loss: 0.00073620223
iter: 32000 loss: 0.0007323837
iter: 32500 loss: 0.00072883896
iter: 33000 loss: 0.0007253971
iter: 33500 loss: 0.0007672859
iter: 34000 loss: 0.00074850733
iter: 34500 loss: 0.0007547441
iter: 35000 loss: 0.00075676554
iter: 35500 loss: 0.00075801736
iter: 36000 loss: 0.00075870997
iter: 36500 loss: 0.0007588421
iter: 37000 loss: 0.0007584684
iter: 37500 loss: 0.00075732305
iter: 38000 loss: 0.0007555771
iter: 38500 loss: 0.00075331994
iter: 39000 loss: 0.0007502647
iter: 39500 loss: 0.00074706867
核心代码
def LSTM(X):
batch_size=tf.shape(X)[0]
time_step=tf.shape(X)[1]
w_in=weights['in']
b_in=biases['in']
input=tf.reshape(X,[-1,input_size])
input_rnn=tf.matmul(input,w_in)+b_in
input_rnn=tf.reshape(input_rnn,[-1,time_step,rnn_unit])
cell=tf.contrib.rnn.BasicLSTMCell(rnn_unit)
#cell=tf.contrib.rnn.core_rnn_cell.BasicLSTMCell(rnn_unit)
init_state=cell.zero_state(batch_size,dtype=tf.float32)
output_rnn,final_states=tf.nn.dynamic_rnn(cell, input_rnn,initial_state=init_state, dtype=tf.float32)
output=tf.reshape(output_rnn,[-1,rnn_unit])
w_out=weights['out']
b_out=biases['out']
pred=tf.matmul(output,w_out)+b_out
return pred,final_states
