串联连接多个神经网络的方式（非并行）

Question

I wonder there is any way to connect multiple NN as a series in tensorflow. For example, input features to DNN structure, and get the result values for input data of RNN structure.

Example code:

import tensorflow as tf
import numpy as np

a = 50 #batch_size
b = 60 #sequence in RNN
c = 40 #features
d = 6  #label classes
rnn_size = b
x_data = np.random.rand(a,b,c)
y_data = np.random.randint(0,high=d,size=[a,1])

tf.reset_default_graph()
X = tf.placeholder(tf.float32, shape=[None,b,c])
Y = tf.placeholder(tf.float32, shape=[None,d])
X = tf.transpose(X, (1,0,2))
X = tf.reshape(X, (-1,c))
X = tf.split(X, b)
hidden_units = [40,20,10]

#DNN Structure
dnn = []
for i in range(len(hidden_units)):
    if i == 0:
        T = X
    else:
        T = dnn[-1]
    dnn.append(tf.layers.dense(T, hidden_units[i], activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer()))

# RNN Structure
rnn = {'w': tf.Variable(tf.random_normal([rnn_size, d], stddev = 0.01), dtype=tf.float32),
       'b': tf.Variable(tf.random_normal([d], stddev = 0.01), dtype=tf.float32)}
cell = tf.nn.rnn_cell.BasicLSTMCell(rnn_size)
outputs, states = tf.contrib.rnn.static_rnn(cell, dnn[-1], dtype=tf.float32)
output = tf.matmul(outputs[-1], rnn['w'])+rnn['b']

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=Y,logits=output))
optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
correct = tf.equal(tf.argmax(output,1),tf.argmax(cost,1))
acc = tf.reduce_mean(tf.cast(correct, tf.float32))

# Run Session
sess = tf.Session()
sess.run([tf.global_variables_initializer(), tf.local_variables_initializer()])
_, c = sess.run([optimizer, cost],feed_dict={X: x_data, Y: tf.Session().run(tf.one_hot(y_data), d)})
print('Accuracy: ', sess.run(acc, feed_dict={X: x_data, Y: tf.Session().run(tf.one_hot(y_data), d)}))

When I run this code, there is an error raised:

File "C:\Anaconda3\Lib\site-packages\tensorflow\python\layers\core.py", line 250, in dense
dtype=inputs.dtype.base_dtype,

AttributeError: 'list' object has no attribute 'dtype'

it seems to be related with type of 'dnn[-1]'

Is there a connective function or data type controller for the connection of the neural networks?

Answer 1

I've solved the problem, finally. The reason of error was little bit ambiguous but X was recognized by 'list' after running 'tf.split', finitely... After I generate a list of DNN Structures which as a length of the sequence, as following: seq = [] for i in range(b): ###dnn structure for i-th array of split### seq.append(dnn structure)

and tuned some codes, then the whole code worked well. Thanks for an attention :)