次のコードで空欄になっている行に入る適切な選択肢を 1 ~ 3 から選び、深層 RNN を実装してください．
import tensorflow as tf
import numpy as np
learning_rate = 0.001
X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])
y = tf.placeholder(tf.int32, [None])
n_neurons = 100
n_layers = 3
# Q1：3 層の RNN レイヤを定義してください．
layers = ##########
[Q1の選択肢]
1.
[tf.nn.stastic_rnn(num_units=n_neurons, activation=tf.nn.relu)
for layer in range(n_layers)]
2.
[tf.nn.rnn_cell.BasicRNNCell(num_units=n_neurons, activation=tf.nn.relu)
for layer in range(n_layers)]
3.
[tf.nn.rnn_cell.BasicRNNCell(num_units=n_neurons, activation=tf.nn.relu)
for layer in range(n_outputs)]
# Q2：MultiRNNCell() 関数を用いて layers の分だけ層を積み重ねてください．
multi_layer_cell = ##########
outputs, states = tf.nn.dynamic_rnn(multi_layer_cell, X, dtype=tf.float32)
[Q2の選択肢]
1. tf.nn.rnn_cell.MultiRNNCell(layers)
2. tf.nn.rnn_cell.MultiRNNCell(n_layers)
3. tf.nn.rnn_cell.MultiRNNCell(learning_rate)