質問編集履歴
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エラー内容の追加
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以下のように、dynamic_rnn()を用いて、LSTM1層を組み込んだニューラルネットの学習を行っていたのですが、これをLSTM2層、3層と増やすにはどのように書き換えればいいのでしょうか？
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単純にdynamic_rnn()とrnn_cell.BasicLSTMCell()を増やして、前のLSTM層の出力を次のLSTM層の入力に使うだけではエラーが出て動きませんでした。。
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ご存知の方、教えていただけると助かります。
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```python
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import tensorflow as tf
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p_logit = tf.matmul(f, W1) + b1
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p = tf.nn.softmax(p_logit)
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p_loss = tf.nn.softmax_cross_entropy_with_logits(logits=p_logit, labels=y)
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```
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```
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dynamic_rnn()とrnn_cell.BasicLSTMCell()を増やして、以下のようなコードに書き換えてみたのですが、エラーが出て動きませんでした。。
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```python
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import tensorflow as tf
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X = tf.placeholder(tf.float32, [None,time_steps,input_row], name='X') # Input data
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lstm_1 = tf.nn.rnn_cell.BasicLSTMCell(15)#
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lstm_2 = tf.nn.rnn_cell.BasicLSTMCell(15)#
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lstm_out_1,states_op = tf.nn.dynamic_rnn(lstm_1,X,dtype=tf.float32,time_major=False)#
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lstm_out_2,states_op = tf.nn.dynamic_rnn(lstm_2,lstm_out_1,dtype=tf.float32,time_major=False)#
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lstm_out_1_last = lstm_out_2[:,-1,:]
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f = tf.cond(train, lambda: tf.slice(lstm_out_1_last, [0, 0], [batch_size // 2, -1]), lambda: lstm_out_1_last)
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y = tf.cond(train, lambda: tf.slice(Y, [0, 0], [batch_size // 2, -1]), lambda: Y)
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W1 = weight_variable([15, 2])
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b1 = bias_variable([2])
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p_logit = tf.matmul(f, W1) + b1
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p = tf.nn.softmax(p_logit)
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p_loss = tf.nn.softmax_cross_entropy_with_logits(logits=p_logit, labels=y)
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```
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### エラー内容
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File "main.py", line 178, in <module>
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build_model()
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File "main.py", line 129, in build_model
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lstm_out_2,states_op = tf.nn.dynamic_rnn(lstm_2,lstm_out_1,dtype=tf.float32,time_major=False)#
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File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/ops/rnn.py", line 627, in dynamic_rnn
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dtype=dtype)
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File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/ops/rnn.py", line 824, in _dynamic_rnn_loop
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swap_memory=swap_memory)
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File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/ops/control_flow_ops.py", line 3224, in while_loop
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result = loop_context.BuildLoop(cond, body, loop_vars, shape_invariants)
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File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/ops/control_flow_ops.py", line 2956, in BuildLoop
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pred, body, original_loop_vars, loop_vars, shape_invariants)
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File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/ops/control_flow_ops.py", line 2893, in _BuildLoop
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body_result = body(*packed_vars_for_body)
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File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/ops/control_flow_ops.py", line 3194, in <lambda>
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body = lambda i, lv: (i + 1, orig_body(*lv))
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File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/ops/rnn.py", line 795, in _time_step
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(output, new_state) = call_cell()
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File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/ops/rnn.py", line 781, in <lambda>
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call_cell = lambda: cell(input_t, state)
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File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/ops/rnn_cell_impl.py", line 339, in __call__
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*args, **kwargs)
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File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/layers/base.py", line 699, in __call__
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self.build(input_shapes)
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File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/ops/rnn_cell_impl.py", line 588, in build
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shape=[input_depth + h_depth, 4 * self._num_units])
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File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/layers/base.py", line 546, in add_variable
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partitioner=partitioner)
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File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/training/checkpointable.py", line 436, in _add_variable_with_custom_getter
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**kwargs_for_getter)
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File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/ops/variable_scope.py", line 1317, in get_variable
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constraint=constraint)
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File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/ops/variable_scope.py", line 1079, in get_variable
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constraint=constraint)
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File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/ops/variable_scope.py", line 425, in get_variable
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constraint=constraint)
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File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/ops/variable_scope.py", line 394, in _true_getter
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use_resource=use_resource, constraint=constraint)
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File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/ops/variable_scope.py", line 733, in _get_single_variable
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name, "".join(traceback.format_list(tb))))
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ValueError: Variable rnn/basic_lstm_cell/kernel already exists, disallowed. Did you mean to set reuse=True or reuse=tf.AUTO_REUSE in VarScope? Originally defined at:
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File "main.py", line 128, in build_model
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lstm_out_1,states_op = tf.nn.dynamic_rnn(lstm_1,X,dtype=tf.float32,time_major=False)#
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File "main.py", line 178, in <module>
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build_model()
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ご存知の方、教えていただけると助かります。
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