TF1.0

    科技2025-05-02  43

    逐句

    loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( logits=logits, labels=Y)) tf.nn.softmax_cross_entropy_with_logits(logits, labels, name=None) 第一个参数logits:就是神经网络最后一层的输出,如果有batch的话,它的大小就是[batchsize,num_classes],单样本的话,大小就是num_classes 第二个参数labels:实际的标签,大小同上参考: TensorFlow tf.nn.softmax_cross_entropy_with_logits的用法 batch_x, batch_y = mnist.train.next_batch(batch_size) mnist.train.next_batch是专门用于由tensorflow提供的MNIST教程的函数。它的工作原理是在开始时将训练图像和标签对随机化,并在每次调用该函数时选择每个随后的batch_size张图像。一旦到达末尾,图像标签对将再次随机分配,并重复该过程。仅在使用所有可用对后,才重新组合和重复整个数据集。 print("Epoch:", '%04d' % (epoch+1), "cost={:.9f}".format(avg_cost)) %04d 四位 参考: Python–格式化输出%s和%d{:.9f} 小数点后九位 参考: Python中 {:.0f} 格式化输出 print("Accuracy:", accuracy.eval({X: mnist.test.images, Y: mnist.test.labels})) f.Tensor.eval(feed_dict=None, session=None): 作用: 在一个Seesion里面“评估”tensor的值(其实就是计算),首先执行之前的所有必要的操作来产生这个计算这个tensor需要的输入,然后通过这些输入产生这个tensor。在激发tensor.eval()这个函数之前,tensor的图必须已经投入到session里面,或者一个默认的session是有效的,或者显式指定session. 参数: feed_dict:一个字典,用来表示tensor被feed的值(联系placeholder一起看) session:(可选) 用来计算(evaluate)这个tensor的session.要是没有指定的话,那么就会使用默认的session。 返回: 表示“计算”结果值的numpy ndarray 参考: TensorFlow中.eval()函数理解

    过程

    数据集加载处理,参数设置图输入,图w,b设置建立,构建模型multilayer_perceptron定义损失函数和优化器,初始化session会话,训练,测试

    代码

    """ Multilayer Perceptron. A Multilayer Perceptron (Neural Network) implementation example using TensorFlow library. This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/). Links: [MNIST Dataset](http://yann.lecun.com/exdb/mnist/). Author: Aymeric Damien Project: https://github.com/aymericdamien/TensorFlow-Examples/ """ # ------------------------------------------------------------------ # # THIS EXAMPLE HAS BEEN RENAMED 'neural_network.py', FOR SIMPLICITY. # # ------------------------------------------------------------------ from __future__ import print_function # Import MNIST data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) import tensorflow as tf # Parameters learning_rate = 0.001 training_epochs = 15 batch_size = 100 display_step = 1 # Network Parameters n_hidden_1 = 256 # 1st layer number of neurons n_hidden_2 = 256 # 2nd layer number of neurons n_input = 784 # MNIST data input (img shape: 28*28) n_classes = 10 # MNIST total classes (0-9 digits) # tf Graph input X = tf.placeholder("float", [None, n_input]) Y = tf.placeholder("float", [None, n_classes]) # Store layers weight & bias weights = { 'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])), 'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])), } biases = { 'b1': tf.Variable(tf.random_normal([n_hidden_1])), 'b2': tf.Variable(tf.random_normal([n_hidden_2])), 'out': tf.Variable(tf.random_normal([n_classes])) } # Create model def multilayer_perceptron(x): # Hidden fully connected layer with 256 neurons layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1']) # Hidden fully connected layer with 256 neurons layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2']) # Output fully connected layer with a neuron for each class out_layer = tf.matmul(layer_2, weights['out']) + biases['out']) return out_layer # Construct model logits = multilayer_perceptron(X) # Define loss and optimizer loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( logits=logits, labels=Y)) optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) train_op = optimizer.minimize(loss_op) # Initializing the variables init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) # Training cycle for epoch in range(training_epochs): avg_cost = 0. total_batch = int(mnist.train.num_examples/batch_size) #总数 / 一次 = 共多少次 # Loop over all batches for i in range(total_batch): batch_x, batch_y = mnist.train.next_batch(batch_size) # Run optimization op (backprop) and cost op (to get loss value) _, c = sess.run([train_op, loss_op], feed_dict={X: batch_x, Y: batch_y}) # Compute average loss avg_cost += c / total_batch # Display logs per epoch step if epoch % display_step == 0: print("Epoch:", '%04d' % (epoch+1), "cost={:.9f}".format(avg_cost)) print("Optimization Finished!") ##############################????????????for???????????????############################# # Test model pred = tf.nn.softmax(logits) # Apply softmax to logits correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(Y, 1)) # Calculate accuracy accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) print("Accuracy:", accuracy.eval({X: mnist.test.images, Y: mnist.test.labels}))
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