dynamic_rnn 源码 https://github.com/tensorflow/tensorflow/blob/9590c4c32dd4346ea5c35673336f5912c6072bf2/tensorflow/contrib/recurrent/python/ops/functional_rnn.py
lstm_dynamic_rnn 1 2 3 4 5 6 7 import tensorflow as tfimport numpy as npfrom tensorflow.python.framework import opsops.reset_default_graph() tf.__version__
'1.12.0'
1 inputs_tensor = tf.constant(np.random.random(size=(3 , 4 , 5 )), dtype=tf.float32)
1 lstm_cell = tf.nn.rnn_cell.LSTMCell(num_units=6 )
1 output, state = tf.nn.dynamic_rnn(cell=lstm_cell, inputs=inputs_tensor, dtype=tf.float32)
<tf.Tensor 'rnn/transpose_1:0' shape=(3, 4, 6) dtype=float32>
LSTMStateTuple(c=<tf.Tensor 'rnn/while/Exit_3:0' shape=(3, 6) dtype=float32>, h=<tf.Tensor 'rnn/while/Exit_4:0' shape=(3, 6) dtype=float32>)
1 2 sess = tf.Session() sess.run(tf.global_variables_initializer())
array([[[ 0.08616676, 0.00701726, 0.02770402, 0.06492911,
0.0510945 , 0.00969434],
[ 0.1320394 , 0.00555411, 0.00191859, 0.08497527,
0.07742812, 0.05351048],
[ 0.22318284, 0.00047223, 0.00479356, -0.07421347,
0.10626906, 0.04756515],
[ 0.2620672 , 0.00858223, -0.00066401, -0.05911936,
0.12364249, 0.03462988]],
[[ 0.06325776, -0.01175407, 0.04182662, 0.02187428,
0.07014529, -0.01354775],
[ 0.1437409 , 0.01115061, 0.03309705, 0.00872881,
0.1256691 , -0.01869454],
[ 0.1477679 , 0.0219029 , -0.00088758, -0.04972449,
0.10450882, -0.00912069],
[ 0.24747844, -0.02095445, 0.08351423, -0.00707687,
0.0659603 , -0.01971625]],
[[ 0.08307187, 0.00345412, 0.02962245, 0.06890179,
0.03142677, 0.01744366],
[ 0.1346873 , -0.05403996, 0.08703925, -0.00265777,
0.04009543, -0.0071087 ],
[ 0.14429979, -0.04602255, 0.04806704, -0.01820353,
0.09431574, 0.00280121],
[ 0.22852188, -0.05865859, 0.04669214, -0.07948805,
0.06424228, 0.03090438]]], dtype=float32)
array([[ 0.2620672 , 0.00858223, -0.00066401, -0.05911936, 0.12364249,
0.03462988],
[ 0.24747844, -0.02095445, 0.08351423, -0.00707687, 0.0659603 ,
-0.01971625],
[ 0.22852188, -0.05865859, 0.04669214, -0.07948805, 0.06424228,
0.03090438]], dtype=float32)
array([[ 0.5363698 , 0.02536838, -0.00174306, -0.0865218 , 0.2190451 ,
0.06242979],
[ 0.45945585, -0.04819317, 0.1650849 , -0.01152224, 0.12156412,
-0.03720763],
[ 0.40406716, -0.15820494, 0.11020815, -0.12061047, 0.11127482,
0.0575163 ]], dtype=float32)
gru_dynamic_rnn 1 2 3 4 5 6 7 import tensorflow as tfimport numpy as npfrom tensorflow.python.framework import opsops.reset_default_graph() tf.__version__
'1.12.0'
1 inputs_tensor = tf.constant(np.random.random(size=(3 , 4 , 5 )), dtype=tf.float32)
1 gru_cell = tf.nn.rnn_cell.GRUCell(num_units=6 )
1 output, state = tf.nn.dynamic_rnn(cell=gru_cell, inputs=inputs_tensor, dtype=tf.float32)
<tf.Tensor 'rnn/transpose_1:0' shape=(3, 4, 6) dtype=float32>
<tf.Tensor 'rnn/while/Exit_3:0' shape=(3, 6) dtype=float32>
1 2 sess = tf.Session() sess.run(tf.global_variables_initializer())
array([[[ 0.10332259, -0.11011579, 0.02266729, 0.0007517 ,
0.03512604, 0.04907914],
[ 0.20385078, -0.23856235, 0.1248817 , -0.08083571,
0.11375216, 0.1124958 ],
[ 0.3623113 , -0.32147223, 0.0005917 , 0.05736844,
0.19632787, 0.17149314],
[ 0.3957892 , -0.25175768, 0.13963164, -0.01752499,
0.19986765, 0.20010342]],
[[ 0.09015381, -0.01318468, 0.04039504, 0.00195809,
-0.03166562, -0.00577726],
[ 0.1832312 , -0.00861337, 0.07086013, -0.05410649,
-0.11791474, -0.12356216],
[ 0.262054 , -0.21013457, 0.05953048, 0.00513211,
0.00651281, -0.02495475],
[ 0.37156823, -0.19515839, 0.18966836, -0.04400685,
0.0111442 , -0.03699975]],
[[ 0.11565635, -0.11679434, -0.06442562, 0.05251991,
-0.01245365, -0.00727599],
[ 0.17201838, -0.24277222, -0.07361332, -0.03645991,
0.10429659, -0.00199607],
[ 0.26178688, -0.21496084, -0.05825588, -0.05373647,
0.09104574, -0.04549351],
[ 0.3539365 , -0.20948091, -0.02887814, -0.00197285,
0.06114171, -0.05429474]]], dtype=float32)
array([[ 0.3957892 , -0.25175768, 0.13963164, -0.01752499, 0.19986765,
0.20010342],
[ 0.37156823, -0.19515839, 0.18966836, -0.04400685, 0.0111442 ,
-0.03699975],
[ 0.3539365 , -0.20948091, -0.02887814, -0.00197285, 0.06114171,
-0.05429474]], dtype=float32)
Multi_lstm_dynamic_rnn 1 2 3 4 5 6 import tensorflow as tfimport numpy as npfrom tensorflow.python.framework import opsops.reset_default_graph() tf.__version__
'1.12.0'
1 inputs_tensor = tf.constant(np.random.random(size=(3 , 4 , 5 )), dtype=tf.float32)
1 2 3 lstm_cell_units_list = [32 ,16 ,8 ] lstm_cells = [tf.nn.rnn_cell.LSTMCell(num_units=unit) for unit in lstm_cell_units_list] multi_lstm_cells = tf.nn.rnn_cell.MultiRNNCell(lstm_cells)
1 outputs, states = tf.nn.dynamic_rnn(cell=multi_lstm_cells, inputs=inputs_tensor, dtype=tf.float32)
<tf.Tensor 'rnn/transpose_1:0' shape=(3, 4, 8) dtype=float32>
(LSTMStateTuple(c=<tf.Tensor 'rnn/while/Exit_3:0' shape=(3, 32) dtype=float32>, h=<tf.Tensor 'rnn/while/Exit_4:0' shape=(3, 32) dtype=float32>),
LSTMStateTuple(c=<tf.Tensor 'rnn/while/Exit_5:0' shape=(3, 16) dtype=float32>, h=<tf.Tensor 'rnn/while/Exit_6:0' shape=(3, 16) dtype=float32>),
LSTMStateTuple(c=<tf.Tensor 'rnn/while/Exit_7:0' shape=(3, 8) dtype=float32>, h=<tf.Tensor 'rnn/while/Exit_8:0' shape=(3, 8) dtype=float32>))
1 2 sess = tf.Session() sess.run(tf.global_variables_initializer())
array([[[-1.61404535e-03, -5.39962610e-04, 5.39611437e-06,
2.28725166e-05, 9.44484840e-04, -4.58947802e-03,
1.46846834e-03, -7.76402245e-04],
[-3.09936469e-03, -4.27109393e-04, 1.62538933e-03,
-3.19648359e-04, 1.73521251e-03, -1.22727510e-02,
4.70003067e-03, -2.35606125e-03],
[-2.51045800e-03, -7.78585789e-04, 2.94276653e-03,
7.99274276e-05, 2.68396758e-03, -2.12856531e-02,
9.49617289e-03, -2.14548036e-03],
[ 1.16307237e-04, -4.06709267e-04, 4.33099223e-03,
1.24737364e-03, 3.62196262e-03, -3.07264701e-02,
1.57848410e-02, -7.23684439e-04]],
[[-3.23332497e-05, 2.67408788e-04, 7.24235782e-04,
-2.50815327e-04, 2.83148460e-04, -2.27145315e-03,
1.13080570e-03, -1.57606765e-03],
[ 2.05371980e-04, -1.17544514e-04, 7.81793962e-04,
1.08315377e-04, 1.33984850e-03, -7.61773949e-03,
3.80030414e-03, -2.82652606e-03],
[ 8.20874877e-04, -1.12650392e-03, -8.80288571e-05,
1.86283619e-03, 2.56655412e-03, -1.47406263e-02,
7.87232257e-03, -2.76046596e-03],
[ 2.21824460e-03, -2.74996134e-03, -1.48778886e-03,
5.04738186e-03, 3.63758323e-03, -2.35401765e-02,
1.35801937e-02, -2.54129106e-03]],
[[-8.64092377e-04, -3.04833520e-04, -1.56841183e-04,
-1.28708722e-04, 9.67342989e-04, -2.75324681e-03,
8.15562380e-04, -6.81574922e-04],
[-2.73647415e-03, -1.36391085e-03, -5.19481488e-04,
-4.36288465e-05, 2.77121575e-03, -9.15816333e-03,
2.90221907e-03, -5.05329692e-04],
[-3.75988754e-03, -3.24360654e-03, -1.28453283e-03,
4.84427328e-05, 5.25222160e-03, -1.69832408e-02,
5.55129535e-03, -2.57142878e-04],
[-4.63895453e-03, -5.13906591e-03, -1.28147972e-03,
4.88204561e-04, 7.37427827e-03, -2.69612260e-02,
9.37340409e-03, -1.25891145e-03]]], dtype=float32)
array([[ 0.00011631, -0.00040671, 0.00433099, 0.00124737, 0.00362196,
-0.03072647, 0.01578484, -0.00072368],
[ 0.00221824, -0.00274996, -0.00148779, 0.00504738, 0.00363758,
-0.02354018, 0.01358019, -0.00254129],
[-0.00463895, -0.00513907, -0.00128148, 0.0004882 , 0.00737428,
-0.02696123, 0.0093734 , -0.00125891]], dtype=float32)
array([[ 0.00022726, -0.00082744, 0.00844097, 0.00248186, 0.00721051,
-0.06110654, 0.03194349, -0.00143018],
[ 0.00437538, -0.00561603, -0.00291193, 0.01000925, 0.00718385,
-0.04696855, 0.02727731, -0.00508054],
[-0.00914405, -0.01050362, -0.00249997, 0.00097318, 0.01467903,
-0.05430028, 0.01892203, -0.00251437]], dtype=float32)
Multi_gru_dynamic_rnn 1 2 3 4 5 6 import tensorflow as tfimport numpy as npfrom tensorflow.python.framework import opsops.reset_default_graph() tf.__version__
'1.12.0'
1 inputs_tensor = tf.constant(np.random.random(size=(3 , 4 , 5 )), dtype=tf.float32)
1 2 3 gru_cell_units_list = [32 ,16 ,8 ] gru_cells = [tf.nn.rnn_cell.GRUCell(num_units=unit) for unit in gru_cell_units_list] multi_gru_cells = tf.nn.rnn_cell.MultiRNNCell(gru_cells)
1 outputs, states = tf.nn.dynamic_rnn(cell=multi_gru_cells, inputs=inputs_tensor, dtype=tf.float32)
<tf.Tensor 'rnn/transpose_1:0' shape=(3, 4, 8) dtype=float32>
(<tf.Tensor 'rnn/while/Exit_3:0' shape=(3, 32) dtype=float32>,
<tf.Tensor 'rnn/while/Exit_4:0' shape=(3, 16) dtype=float32>,
<tf.Tensor 'rnn/while/Exit_5:0' shape=(3, 8) dtype=float32>)
1 2 sess = tf.Session() sess.run(tf.global_variables_initializer())
array([[[-3.0689167e-03, 4.7762650e-03, -5.6350869e-03, 7.3930359e-04,
2.7776770e-03, 1.6547712e-03, 4.0468639e-03, -1.6071725e-03],
[-9.6203415e-03, 1.0396119e-02, -1.2028635e-02, -7.5696781e-04,
9.5890947e-03, 3.0168635e-03, 9.1733877e-03, -6.7313079e-04],
[-1.6855957e-02, 2.2866743e-02, -2.2569295e-02, -3.3538719e-03,
2.0547308e-02, -3.6199810e-05, 1.4878229e-02, 8.4475791e-03],
[-2.5817569e-02, 4.0700834e-02, -3.5192616e-02, -8.7473392e-03,
3.5584368e-02, -9.0519777e-03, 2.4247481e-02, 2.3476347e-02]],
[[-3.0218370e-03, 6.4162901e-03, -6.9783311e-03, 3.8428712e-04,
5.0610453e-03, -6.1881670e-04, 3.7080410e-04, 5.2686040e-03],
[-1.0377670e-02, 1.5972832e-02, -1.6679505e-02, -9.3015225e-04,
1.7201036e-02, -1.5078825e-03, 1.3457731e-03, 1.2340306e-02],
[-2.2288060e-02, 2.7055936e-02, -2.7431497e-02, -4.4479482e-03,
3.2678001e-02, -2.9535883e-03, 7.6549058e-03, 1.8373087e-02],
[-3.6172852e-02, 4.3594681e-02, -4.2179834e-02, -3.7945234e-03,
4.7429617e-02, -2.6138786e-03, 1.7336009e-02, 2.5696296e-02]],
[[-7.6424627e-04, 7.8961477e-03, -7.1330541e-03, 1.9744530e-03,
4.7649448e-03, -3.9919489e-04, -1.7578194e-04, 3.7013867e-03],
[-1.6916599e-03, 2.1197245e-02, -1.7833594e-02, 1.4740386e-03,
1.3453390e-02, -5.6854654e-03, 1.9273567e-03, 1.2227280e-02],
[-5.0865025e-03, 3.9313547e-02, -3.0530766e-02, -3.3563119e-05,
2.9243957e-02, -1.4064199e-02, 4.2982912e-03, 2.5105122e-02],
[-1.1633213e-02, 6.1902620e-02, -4.5463178e-02, -2.4870001e-03,
5.1334824e-02, -2.5908694e-02, 7.0450706e-03, 4.3221094e-02]]],
dtype=float32)
array([[-0.02581757, 0.04070083, -0.03519262, -0.00874734, 0.03558437,
-0.00905198, 0.02424748, 0.02347635],
[-0.03617285, 0.04359468, -0.04217983, -0.00379452, 0.04742962,
-0.00261388, 0.01733601, 0.0256963 ],
[-0.01163321, 0.06190262, -0.04546318, -0.002487 , 0.05133482,
-0.02590869, 0.00704507, 0.04322109]], dtype=float32)
Multi_lstm_bidirectional_dynamic_rnn 1 2 3 4 5 6 7 import tensorflow as tfimport numpy as npfrom tensorflow.python.framework import opsops.reset_default_graph() tf.__version__
'1.12.0'
1 inputs_tensor = tf.constant(np.random.random(size=(3 , 4 , 5 )), dtype=tf.float32)
1 2 3 4 5 6 cell_fw_units_list = [32 ,16 ,8 ] cell_bw_units_list = [33 ,17 ,9 ] cell_fw = [tf.nn.rnn_cell.LSTMCell(num_units=unit) for unit in cell_fw_units_list] cell_bw = [tf.nn.rnn_cell.LSTMCell(num_units=unit) for unit in cell_bw_units_list] lstm_forward = tf.nn.rnn_cell.MultiRNNCell(cells=cell_fw) lstm_backword = tf.nn.rnn_cell.MultiRNNCell(cells=cell_bw)
1 2 outputs, states = tf.nn.bidirectional_dynamic_rnn( cell_fw=lstm_forward,cell_bw=lstm_backword,inputs=inputs_tensor,dtype=tf.float32)
(<tf.Tensor 'bidirectional_rnn/fw/fw/transpose_1:0' shape=(3, 4, 8) dtype=float32>,
<tf.Tensor 'ReverseV2:0' shape=(3, 4, 9) dtype=float32>)
((LSTMStateTuple(c=<tf.Tensor 'bidirectional_rnn/fw/fw/while/Exit_3:0' shape=(3, 32) dtype=float32>, h=<tf.Tensor 'bidirectional_rnn/fw/fw/while/Exit_4:0' shape=(3, 32) dtype=float32>),
LSTMStateTuple(c=<tf.Tensor 'bidirectional_rnn/fw/fw/while/Exit_5:0' shape=(3, 16) dtype=float32>, h=<tf.Tensor 'bidirectional_rnn/fw/fw/while/Exit_6:0' shape=(3, 16) dtype=float32>),
LSTMStateTuple(c=<tf.Tensor 'bidirectional_rnn/fw/fw/while/Exit_7:0' shape=(3, 8) dtype=float32>, h=<tf.Tensor 'bidirectional_rnn/fw/fw/while/Exit_8:0' shape=(3, 8) dtype=float32>)),
(LSTMStateTuple(c=<tf.Tensor 'bidirectional_rnn/bw/bw/while/Exit_3:0' shape=(3, 33) dtype=float32>, h=<tf.Tensor 'bidirectional_rnn/bw/bw/while/Exit_4:0' shape=(3, 33) dtype=float32>),
LSTMStateTuple(c=<tf.Tensor 'bidirectional_rnn/bw/bw/while/Exit_5:0' shape=(3, 17) dtype=float32>, h=<tf.Tensor 'bidirectional_rnn/bw/bw/while/Exit_6:0' shape=(3, 17) dtype=float32>),
LSTMStateTuple(c=<tf.Tensor 'bidirectional_rnn/bw/bw/while/Exit_7:0' shape=(3, 9) dtype=float32>, h=<tf.Tensor 'bidirectional_rnn/bw/bw/while/Exit_8:0' shape=(3, 9) dtype=float32>)))
<tf.Tensor 'bidirectional_rnn/fw/fw/transpose_1:0' shape=(3, 4, 8) dtype=float32>
<tf.Tensor 'ReverseV2:0' shape=(3, 4, 9) dtype=float32>
(LSTMStateTuple(c=<tf.Tensor 'bidirectional_rnn/fw/fw/while/Exit_3:0' shape=(3, 32) dtype=float32>, h=<tf.Tensor 'bidirectional_rnn/fw/fw/while/Exit_4:0' shape=(3, 32) dtype=float32>),
LSTMStateTuple(c=<tf.Tensor 'bidirectional_rnn/fw/fw/while/Exit_5:0' shape=(3, 16) dtype=float32>, h=<tf.Tensor 'bidirectional_rnn/fw/fw/while/Exit_6:0' shape=(3, 16) dtype=float32>),
LSTMStateTuple(c=<tf.Tensor 'bidirectional_rnn/fw/fw/while/Exit_7:0' shape=(3, 8) dtype=float32>, h=<tf.Tensor 'bidirectional_rnn/fw/fw/while/Exit_8:0' shape=(3, 8) dtype=float32>))
(LSTMStateTuple(c=<tf.Tensor 'bidirectional_rnn/bw/bw/while/Exit_3:0' shape=(3, 33) dtype=float32>, h=<tf.Tensor 'bidirectional_rnn/bw/bw/while/Exit_4:0' shape=(3, 33) dtype=float32>),
LSTMStateTuple(c=<tf.Tensor 'bidirectional_rnn/bw/bw/while/Exit_5:0' shape=(3, 17) dtype=float32>, h=<tf.Tensor 'bidirectional_rnn/bw/bw/while/Exit_6:0' shape=(3, 17) dtype=float32>),
LSTMStateTuple(c=<tf.Tensor 'bidirectional_rnn/bw/bw/while/Exit_7:0' shape=(3, 9) dtype=float32>, h=<tf.Tensor 'bidirectional_rnn/bw/bw/while/Exit_8:0' shape=(3, 9) dtype=float32>))
1 2 sess = tf.Session() sess.run(tf.global_variables_initializer())
array([[[-0.00076688, -0.00200841, -0.00236855, 0.00039911,
-0.00145523, -0.00153653, -0.00134254, 0.00106617],
[-0.00454114, -0.00330769, -0.00736781, 0.00173002,
-0.0032972 , -0.00345388, -0.00293425, 0.00292132],
[-0.01027342, -0.00316362, -0.01381279, 0.00462259,
-0.00577486, -0.00438865, -0.00475066, 0.00504041],
[-0.01815201, -0.00150841, -0.0213333 , 0.00920272,
-0.00753702, -0.0049286 , -0.00753725, 0.00764989]],
[[-0.00051021, -0.00109504, -0.00235562, 0.00033509,
-0.00184875, -0.00063042, -0.00011131, 0.00110563],
[-0.00254402, -0.00287243, -0.00706615, 0.00204808,
-0.00484681, -0.00206984, -0.0014233 , 0.00308458],
[-0.00708036, -0.00366582, -0.01411008, 0.00544437,
-0.0080783 , -0.0034703 , -0.00328208, 0.00583236],
[-0.01305352, -0.00295205, -0.02222473, 0.01060574,
-0.01145888, -0.00383543, -0.0055557 , 0.0088768 ]],
[[-0.00123942, 0.00074371, -0.00094162, 0.00037477,
-0.00032651, 0.0001554 , 0.00046975, 0.00046324],
[-0.00429494, 0.00162228, -0.00351843, 0.00242352,
-0.00078484, -0.00083089, -0.00064292, 0.00222571],
[-0.00847503, 0.00332979, -0.00786664, 0.00663501,
-0.00156324, -0.00165966, -0.00228085, 0.00515618],
[-0.01371686, 0.00522377, -0.01415662, 0.01267656,
-0.00229844, -0.00248059, -0.00501328, 0.00901466]]],
dtype=float32)
1 sess.run(states[0 ][2 ].h)
array([[-0.01815201, -0.00150841, -0.0213333 , 0.00920272, -0.00753702,
-0.0049286 , -0.00753725, 0.00764989],
[-0.01305352, -0.00295205, -0.02222473, 0.01060574, -0.01145888,
-0.00383543, -0.0055557 , 0.0088768 ],
[-0.01371686, 0.00522377, -0.01415662, 0.01267656, -0.00229844,
-0.00248059, -0.00501328, 0.00901466]], dtype=float32)
1 sess.run(states[0 ][2 ].c)
array([[-0.03619757, -0.00299038, -0.04237926, 0.01859355, -0.01471011,
-0.00991257, -0.01535283, 0.01467649],
[-0.02600891, -0.00582871, -0.04436516, 0.02145906, -0.0222083 ,
-0.00767388, -0.01128497, 0.01682222],
[-0.02723665, 0.0103671 , -0.0282324 , 0.02551894, -0.00446999,
-0.00497827, -0.01015829, 0.01708832]], dtype=float32)
Multi_gru_bidirectional_dynamic_rnn 1 2 3 4 5 6 7 import tensorflow as tfimport numpy as npfrom tensorflow.python.framework import opsops.reset_default_graph() tf.__version__
'1.12.0'
1 inputs_tensor = tf.constant(np.random.random(size=(3 , 4 , 5 )), dtype=tf.float32)
1 2 3 4 5 6 cell_fw_units_list = [32 ,16 ,8 ] cell_bw_units_list = [33 ,17 ,9 ] cell_fw = [tf.nn.rnn_cell.GRUCell(num_units=unit) for unit in cell_fw_units_list] cell_bw = [tf.nn.rnn_cell.GRUCell(num_units=unit) for unit in cell_bw_units_list] gru_forward = tf.nn.rnn_cell.MultiRNNCell(cells=cell_fw) gru_backword = tf.nn.rnn_cell.MultiRNNCell(cells=cell_bw)
1 2 outputs, states = tf.nn.bidirectional_dynamic_rnn( cell_fw=lstm_forward,cell_bw=lstm_backword,inputs=inputs_tensor,dtype=tf.float32)
(<tf.Tensor 'bidirectional_rnn/fw/fw/transpose_1:0' shape=(3, 4, 8) dtype=float32>,
<tf.Tensor 'ReverseV2:0' shape=(3, 4, 9) dtype=float32>)
((<tf.Tensor 'bidirectional_rnn/fw/fw/while/Exit_3:0' shape=(3, 32) dtype=float32>,
<tf.Tensor 'bidirectional_rnn/fw/fw/while/Exit_4:0' shape=(3, 16) dtype=float32>,
<tf.Tensor 'bidirectional_rnn/fw/fw/while/Exit_5:0' shape=(3, 8) dtype=float32>),
(<tf.Tensor 'bidirectional_rnn/bw/bw/while/Exit_3:0' shape=(3, 33) dtype=float32>,
<tf.Tensor 'bidirectional_rnn/bw/bw/while/Exit_4:0' shape=(3, 17) dtype=float32>,
<tf.Tensor 'bidirectional_rnn/bw/bw/while/Exit_5:0' shape=(3, 9) dtype=float32>))
<tf.Tensor 'bidirectional_rnn/fw/fw/transpose_1:0' shape=(3, 4, 8) dtype=float32>
<tf.Tensor 'ReverseV2:0' shape=(3, 4, 9) dtype=float32>
(<tf.Tensor 'bidirectional_rnn/fw/fw/while/Exit_3:0' shape=(3, 32) dtype=float32>,
<tf.Tensor 'bidirectional_rnn/fw/fw/while/Exit_4:0' shape=(3, 16) dtype=float32>,
<tf.Tensor 'bidirectional_rnn/fw/fw/while/Exit_5:0' shape=(3, 8) dtype=float32>)
(<tf.Tensor 'bidirectional_rnn/bw/bw/while/Exit_3:0' shape=(3, 33) dtype=float32>,
<tf.Tensor 'bidirectional_rnn/bw/bw/while/Exit_4:0' shape=(3, 17) dtype=float32>,
<tf.Tensor 'bidirectional_rnn/bw/bw/while/Exit_5:0' shape=(3, 9) dtype=float32>)
1 2 sess = tf.Session() sess.run(tf.global_variables_initializer())
array([[[-6.90058898e-03, -2.82262132e-04, 2.41189310e-03,
-1.32219959e-03, -1.17578653e-04, -1.02065690e-03,
-3.85798234e-03, -6.75282069e-03],
[-1.89461559e-02, -1.18712685e-03, 8.09784234e-03,
-5.38685592e-04, -2.89135100e-03, -5.51631721e-03,
-1.24855377e-02, -1.74822416e-02],
[-3.18835154e-02, -3.26077780e-03, 1.75229590e-02,
2.73799687e-03, -9.39253345e-03, -1.30872596e-02,
-2.38550343e-02, -3.05617414e-02],
[-4.67147976e-02, -6.17155526e-03, 2.97614653e-02,
6.51888642e-03, -1.87356286e-02, -2.50062142e-02,
-3.67445275e-02, -4.68713641e-02]],
[[-8.41161050e-03, -2.88979820e-04, 3.92245734e-03,
2.17063329e-03, -5.09262085e-04, -2.77552451e-03,
-5.37689496e-03, -3.95025592e-03],
[-2.47462429e-02, -1.29592582e-03, 1.10453246e-02,
8.48548859e-03, -2.65943818e-03, -1.01975258e-02,
-1.82780307e-02, -1.22013604e-02],
[-3.99502814e-02, -3.65575845e-03, 1.86709110e-02,
1.63780842e-02, -8.04547779e-03, -2.07924694e-02,
-3.48701701e-02, -2.20657662e-02],
[-5.38205951e-02, -7.08078314e-03, 2.95224246e-02,
2.77038664e-02, -1.74766369e-02, -3.49066183e-02,
-5.22553027e-02, -3.21215130e-02]],
[[-4.29279124e-03, -1.64932735e-05, 5.42476214e-03,
4.06091474e-03, -3.88002302e-03, -1.09710405e-03,
-2.73891282e-03, -1.10962777e-03],
[-1.24491388e-02, -5.43807575e-04, 1.43955573e-02,
8.08995403e-03, -1.06180515e-02, -3.86995194e-03,
-8.71607196e-03, -7.86763430e-03],
[-2.33881716e-02, -1.67222926e-03, 2.73847207e-02,
1.45246573e-02, -2.10378524e-02, -1.14183174e-02,
-1.76941082e-02, -1.70471650e-02],
[-3.55363414e-02, -3.23894341e-03, 4.07814831e-02,
2.15009488e-02, -3.38361487e-02, -2.32368447e-02,
-3.00667081e-02, -2.97910050e-02]]], dtype=float32)
array([[-0.0467148 , -0.00617156, 0.02976147, 0.00651889, -0.01873563,
-0.02500621, -0.03674453, -0.04687136],
[-0.0538206 , -0.00708078, 0.02952242, 0.02770387, -0.01747664,
-0.03490662, -0.0522553 , -0.03212151],
[-0.03553634, -0.00323894, 0.04078148, 0.02150095, -0.03383615,
-0.02323684, -0.03006671, -0.029791 ]], dtype=float32)
