我已经编写了代码来执行深度学习的隐藏层方法。每个隐藏层都会分析输入数据，并将其传递给其他隐藏层，直到分析后的数据出现。
我可以创建任意数量的隐藏层。但是，如果我想创建50个隐藏层，那将需要很长时间和精力。因此，我考虑使用循环功能来节省时间和精力。然而，由于我是编程新手，这对我来说很困难。
这是程序代码：
import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("/tmp/data/", one_hot=True)n_nodes_hl1 = 500n_nodes_hl2 = 500n_nodes_hl3 = 500n_classes = 10batch_size = 100# height * widthx = tf.placeholder('float',[None, 784])y = tf.placeholder('float')def neural_network_model(data): # (input_data * wehights) + biases hidden_1_layer = {'weight' :tf.Variable(tf.random_normal([784, n_nodes_hl1])), 'biases' :tf.Variable(tf.random_normal([n_nodes_hl1]))} hidden_2_layer = {'weight' :tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])), 'biases' :tf.Variable(tf.random_normal([n_nodes_hl2]))} hidden_3_layer = {'weight' :tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])), 'biases' :tf.Variable(tf.random_normal([n_nodes_hl3]))} output_layer = {'weight' :tf.Variable(tf.random_normal([n_nodes_hl3, n_classes])), 'biases' :tf.Variable(tf.random_normal([n_classes]))} # # (input_data * wehights) + biases l1 = tf.add(tf.matmul(data, hidden_1_layer['weight']), hidden_1_layer['biases']) l1 = tf.nn.relu(l1) l2 = tf.add(tf.matmul(l1, hidden_2_layer['weight']), hidden_2_layer['biases']) l2 = tf.nn.relu(l2) l3 = tf.add(tf.matmul(l2, hidden_3_layer['weight']), hidden_3_layer['biases']) l3 = tf.nn.relu(l3) output = tf.matmul(l3, output_layer['weight']) + output_layer['biases'] return outputdef train_neural_network(x): prediction = neural_network_model(x) cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(prediction,y) ) optimizer = tf.train.AdamOptimizer().minimize(cost) hm_epochs = 20 with tf.Session() as sess: sess.run(tf.initialize_all_variables()) for epoch in range(hm_epochs): epoch_loss = 0 for _ in range(int(mnist.train.num_examples/batch_size)): epoch_x, epoch_y = mnist.train.next_batch(batch_size) _, c = sess.run([optimizer, cost], feed_dict = {x: epoch_x, y: epoch_y}) epoch_loss += c print('Epoch', epoch, 'completed out of', hm_epochs, 'loss:', epoch_loss) correct = tf.equal(tf.argmax(prediction,1), tf.argmax(y,1)) accuracy = tf.reduce_mean(tf.cast(correct, 'float')) print('accuracy:', accuracy.eval({x:mnist.test.images, y:mnist.test.labels}))train_neural_network(x)以下是我需要循环的部分：
def neural_network_model(data): # (input_data * wehights) + biases hidden_1_layer = {'weight' :tf.Variable(tf.random_normal([784, n_nodes_hl1])), 'biases' :tf.Variable(tf.random_normal([n_nodes_hl1]))} hidden_2_layer = {'weight' :tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])), 'biases' :tf.Variable(tf.random_normal([n_nodes_hl2]))} hidden_3_layer = {'weight' :tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])), 'biases' :tf.Variable(tf.random_normal([n_nodes_hl3]))} output_layer = {'weight' :tf.Variable(tf.random_normal([n_nodes_hl3, n_classes])), 'biases' :tf.Variable(tf.random_normal([n_classes]))} # # (input_data * wehights) + biases l1 = tf.add(tf.matmul(data, hidden_1_layer['weight']), hidden_1_layer['biases']) l1 = tf.nn.relu(l1) l2 = tf.add(tf.matmul(l1, hidden_2_layer['weight']), hidden_2_layer['biases']) l2 = tf.nn.relu(l2) l3 = tf.add(tf.matmul(l2, hidden_3_layer['weight']), hidden_3_layer['biases']) l3 = tf.nn.relu(l3) output = tf.matmul(l3, output_layer['weight']) + output_layer['biases'] return output回答：
我想你想要一个类似下面的方法来创建单个隐藏层：
def make_hidden(input_num, hidden_num): return {'weight' :tf.Variable(tf.random_normal([input_num, hidden_num])), 'biases' :tf.Variable(tf.random_normal([hidden_num]))}输出层可以像你之前那样创建。
def make_output(hidden_num, output_classes): return {'weight' :tf.Variable(tf.random_normal([hidden_num, n_classes])), 'biases' :tf.Variable(tf.random_normal([n_classes]))}然后，你有一个列表来存储每一层的节点数，从输入层开始，到最后一个隐藏层结束：
n_nodes = [0, 784, 500, 500, 500] # |___ 虚拟值，以便n_nodes[i]和n_nodes[i+1]存储第i个隐藏层的输入和隐藏节点数 # （基于1的索引），因为layers[0]是输入层。然后你的neural_network_model可以简化如下：
def neural_network_model(data, n_nodes): layers = []*len(n_nodes) layers[0] = data for i in range(1, len(n_nodes)-1): hidden_i = make_hidden(n_nodes[i], n_nodes[i+1]) layers[i] = tf.add(tf.matmul(layers[i-1], hidden_i['weight']), hidden_i['biases']) layers[i] = tf.nn.relu(layers[i]) output_layer = make_output(n_nodes[-1], n_classes) output = tf.matmul(layers[-1], output_layer['weight']) + output_layer['biases'] return output你可能还需要一些小的修改来使代码正常工作。我希望你能理解循环隐藏层的要点。
