Tensorflow暑期实践——基于单个神经元的手写数字识别(全部代码)
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# coding: utf-8 import tensorflow as tf import os os.environ["CUDA_VISIBLE_DEVICES"] = "-1" print(tf.__version__) print(tf.test.is_gpu_available()) from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) tf.reset_default_graph() #清除default graph和不断增加的节点 x = tf.placeholder(tf.float32, [None, 784]) # mnist 中每张图片共有28*28=784个像素点 y = tf.placeholder(tf.float32, [None, 10]) # 0-9 一共10个数字=> 10 个类别 norm = tf.random_normal([100]) #生成100个随机数 with tf.Session() as sess: norm_data=norm.eval() print(norm_data[:10]) #打印前10个随机数 import matplotlib.pyplot as plt plt.hist(norm_data) plt.show() W = tf.Variable(tf.random_normal([784, 10])) b = tf.Variable(tf.zeros([10])) forward=tf.matmul(x, W) + b # 前向输出 tf.summary.histogram(‘forward‘,forward)#将前向输出值以直方图显示 pred = tf.nn.softmax(forward) # Softmax分类 train_epochs = 30 batch_size = 100 total_batch= int(mnist.train.num_examples/batch_size) display_step = 1 learning_rate=0.01 loss_function = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1)) # 交叉熵 tf.summary.scalar(‘loss‘, loss_function)#将损失以标量显示 optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss_function) #梯度下降 # 检查预测类别tf.argmax(pred, 1)与实际类别tf.argmax(y, 1)的匹配情况 correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) # 准确率 accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) # 将布尔值转化为浮点数,并计算平均值 tf.summary.scalar(‘accuracy‘, accuracy)#将准确率以标量显示 sess = tf.Session() #声明会话 init = tf.global_variables_initializer() # 变量初始化 sess.run(init) merged_summary_op = tf.summary.merge_all()#合并所有summary writer = tf.summary.FileWriter(‘log/mnist_single_neuron‘, sess.graph) #创建写入符 # 开始训练 for epoch in range(train_epochs ): for batch in range(total_batch): xs, ys = mnist.train.next_batch(batch_size)# 读取批次数据 sess.run(optimizer,feed_dict={x: xs,y: ys}) # 执行批次训练 #生成summary summary_str = sess.run(merged_summary_op,feed_dict={x: xs,y: ys}) writer.add_summary(summary_str, epoch)#将summary 写入文件 #total_batch个批次训练完成后,使用验证数据计算误差与准确率 loss,acc = sess.run([loss_function,accuracy], feed_dict={x: mnist.validation.images, y: mnist.validation.labels}) # 打印训练过程中的详细信息 if (epoch+1) % display_step == 0: print("Train Epoch:", ‘%02d‘ % (epoch+1), "Loss=", "{:.9f}".format(loss)," Accuracy=","{:.4f}".format(acc)) print("Train Finished!") print("Test Accuracy:", sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels})) prediction_result=sess.run(tf.argmax(pred,1), # 由于pred预测结果是one-hot编码格式,所以需要转换为0~9数字 feed_dict={x: mnist.test.images }) prediction_result[0:10] #查看预测结果中的前10项 import matplotlib.pyplot as plt import numpy as np def plot_images_labels_prediction(images,labels, prediction,idx,num=10): fig = plt.gcf() fig.set_size_inches(10, 12) if num>25: num=25 for i in range(0, num): ax=plt.subplot(5,5, 1+i) ax.imshow(np.reshape(images[idx],(28, 28)), cmap=‘binary‘) title= "label=" +str(np.argmax(labels[idx])) if len(prediction)>0: title+=",predict="+str(prediction[idx]) ax.set_title(title,fontsize=10) ax.set_xticks([]);ax.set_yticks([]) idx+=1 plt.show() plot_images_labels_prediction(mnist.test.images, mnist.test.labels, prediction_result,0) # 预测结果 plot_images_labels_prediction(mnist.test.images, mnist.test.labels, prediction_result,10,25)
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