我的神经网络有以下程序：

n_steps = 9
n_inputs = 36
n_neurons = 50
n_outputs = 1
n_layers = 2
learning_rate = 0.0001
batch_size =100
n_epochs = 1000#200 
train_set_size = 1000
test_set_size = 1000
tf.reset_default_graph()
X = tf.placeholder(tf.float32, [None, n_steps, n_inputs],name="input")
y = tf.placeholder(tf.float32, [None, n_outputs],name="output")
layers = [tf.contrib.rnn.LSTMCell(num_units=n_neurons,activation=tf.nn.relu6, use_peepholes = True,name="layer"+str(layer))
         for layer in range(n_layers)]    layers.append(tf.contrib.rnn.LSTMCell(num_units=n_neurons,activation=tf.nn.relu6, use_peepholes = True,name="layer"+str(layer)))
multi_layer_cell = tf.contrib.rnn.MultiRNNCell(layers)
rnn_outputs, states = tf.nn.dynamic_rnn(multi_layer_cell, X, dtype=tf.float32)
stacked_rnn_outputs = tf.reshape(rnn_outputs, [-1, n_neurons]) 
stacked_outputs = tf.layers.dense(stacked_rnn_outputs, n_outputs)
outputs = tf.reshape(stacked_outputs, [-1, n_steps, n_outputs])
outputs = outputs[:,n_steps-1,:]

我想知道我的网络是否完全连接？
当我尝试查看变量时，我看到：

multi_layer_cell.weights

输出是：

[<tf.Variable 'rnn/multi_rnn_cell/cell_0/layer0/kernel:0' shape=(86, 200) dtype=float32_ref>,
 <tf.Variable 'rnn/multi_rnn_cell/cell_0/layer0/bias:0' shape=(200,) dtype=float32_ref>,
 <tf.Variable 'rnn/multi_rnn_cell/cell_0/layer0/w_f_diag:0' shape=(50,) dtype=float32_ref>,
 <tf.Variable 'rnn/multi_rnn_cell/cell_0/layer0/w_i_diag:0' shape=(50,) dtype=float32_ref>,
 <tf.Variable 'rnn/multi_rnn_cell/cell_0/layer0/w_o_diag:0' shape=(50,) dtype=float32_ref>,
 <tf.Variable 'rnn/multi_rnn_cell/cell_1/layer1/kernel:0' shape=(100, 200) dtype=float32_ref>,
 <tf.Variable 'rnn/multi_rnn_cell/cell_1/layer1/bias:0' shape=(200,) dtype=float32_ref>,
 <tf.Variable 'rnn/multi_rnn_cell/cell_1/layer1/w_f_diag:0' shape=(50,) dtype=float32_ref>,
 <tf.Variable 'rnn/multi_rnn_cell/cell_1/layer1/w_i_diag:0' shape=(50,) dtype=float32_ref>,
 <tf.Variable 'rnn/multi_rnn_cell/cell_1/layer1/w_o_diag:0' shape=(50,) dtype=float32_ref>]

我不明白每一层是否都得到了完整的输入。
我想知道下图对于上面的代码是否正确：

如果不是这样，那么网络的数字是多少？请告诉我。