这是对神经网络的非数学介绍。我强烈建议深入研究它背后的数学 - 因为它可以提供全面的理解。但在Python中编写这个代码并检查输出是非常棒的。我相信你会明白为什么有这么多的深度学习炒作! 目标 :使用手写数字数据集(MNIST),其中包含60,000个手写数字示例和分类(0-9)。将它传递到神经网络来预测正确的数字。 MNIST数据 第1步 :使用tensorflow导入MNIST数据集import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets('/tmp/data',one_hot=True) 第2步:创建3个隐藏层,每个层包含1000个节点,并将类的数量初始化为10(这是数字的数量,即0-9)。此外,创建两个占位符变量x和y,它们将存储tensorflow图中的值。此外,将批量大小设置为100--尽管我们可以完全处理MNIST数据集,但我们将在100个不同的批次中进行优化。#Three hidden layers n_nodes_hl1 = 1000 n_nodes_hl2 = 1000 n_nodes_hl3 = 1000 #Number of classes n_classes = 10 #Will go through 100 features at a time batch_size = 100 #Placeholder variables (height * width) #These are placeholders for some values in the graph x = tf.placeholder('float',[None,784]) y = tf.placeholder('float') 第3步:神经网络建模
def neural_network_model(data): #Define weights and biases with their dimensions hidden_1_layer = {'weights' : tf.Variable(tf.random_normal([784,n_nodes_hl1])), 'biases' : tf.Variable(tf.random_normal([n_nodes_hl1]))} #bias is used to make some neurons fire even if all inputs is 0 hidden_2_layer = {'weights' : tf.Variable(tf.random_normal([n_nodes_hl1,n_nodes_hl2])), 'biases' : tf.Variable(tf.random_normal([n_nodes_hl2]))} hidden_3_layer = {'weights' : tf.Variable(tf.random_normal([n_nodes_hl2,n_nodes_hl3])), 'biases' : tf.Variable(tf.random_normal([n_nodes_hl3]))} output_layer = {'weights' : tf.Variable(tf.random_normal([n_nodes_hl3,n_classes])), 'biases' : tf.Variable(tf.random_normal([n_classes]))} # Layer values =(input_data*weights) + biases l1 = tf.add(tf.matmul(data,hidden_1_layer['weights']),hidden_1_layer['biases']) l1 = tf.nn.relu(l1) l2 = tf.add(tf.matmul(l1,hidden_2_layer['weights']),hidden_2_layer['biases']) l2 = tf.nn.relu(l2) l3 = tf.add(tf.matmul(l2,hidden_3_layer['weights']),hidden_3_layer['biases']) l3 = tf.nn.relu(l3) output = tf.matmul(l3,output_layer['weights'])+ output_layer['biases'] return output 第4步:现在我们已经建模了我们的神经网络,让我们来运行它!
def train_neural_network(x): prediction = neural_network_model(x) #Cost function is cross entropy with logits cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction,labels=y)) #Choose the optimizer optimizer = tf.train.AdamOptimizer().minimize(cost) #Cycles feed forward + backprop hm_epochs = 10 with tf.Session() as sess: sess.run(tf.global_variables_initializer()) #Training the network 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})) 第5步 :运行你的神经网络!train_neural_network(x) 输出:我们将在每个阶段中完成时间的数量以及损失函数的值。此外,该网络的精度高达95.93% |
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