import tensorflow as tf import tensorflow.examples.tutorials.mnist.input_data as input_data import matplotlib.pyplot as plt #读入数据---------------------------------------------------------------------- mnist input_data.read_data_sets(MNIST_data/,one_hotTrue)#label为one-hot-encoding #查看读入的数据格式------------------------------------------------------------- print(train,mnist.train.num_examples, ,validation,mnist.validation.num_examples, ,test,mnist.test.num_examples) print(train images:,mnist.train.images.shape, labels:,mnist.train.labels.shape) #查看label的值 mnist.train.labels[0] #函数将读入的数据图像显示 def plot_image(image): #上面读入的图像是一维的用reshape转成矩阵输出 plt.imshow(image.reshape(28,28),cmapbinary) plt.show() plot_image(mnist.train.images[0]) #函数输出10张图像带标签label和预测值prediction def plot_images_labels_prediction(images, labels, prediction, idx, num10): figplt.gcf() fig.set_size_inches(12,14) if num25: num25 for i in range(0, num): ax plt.subplot(5,5,1i) ax.imshow(np.reshape(images[idx],(28,28)),cmapbinary) title labelstr(np.argmax(labels[idx])) if len(prediction)0: title,predictionstr(prediction[idx]) ax.set_title(title, fontsize10) ax.set_xticks([]);ax.set_yticks([]) idx1 plt.show() plot_images_labels_prediction(mnist.validation.images, mnist.validation.labels,[],0) #构建MLP模型--------------------------------------------------------------------- #自定义layer def layer(output_dim, input_dim, inputs, activationNone): W tf.Variable(tf.random_normal([input_dim, output_dim])) b tf.Variable(tf.random_normal([1, output_dim])) WXb tf.matmul(inputs,W)b if activation is None: outputs WXb else: outputs activation(WXb) return outputs #两个隐藏层h1、h2一个输入x一个输出y_predict x tf.placeholder(float,[None, 784]) h1 layer(output_dim1000,input_dim784,inputsx,activationtf.nn.relu) h2 layer(output_dim1000,input_dim1000,inputsh1,activationtf.nn.relu) y_predict layer(output_dim10,input_dim1000,inputsh2,activationNone) #定义标签值 y_label tf.placeholder(float,[None,10]) #损失函数 loss_function tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(logitsy_predict,labelsy_label)) #优化器使loss最小化学习率为0.001 optimizer tf.train.AdamOptimizer(learning_rate0.001).minimize(loss_function) #预测结果的正确性 correct_prediction tf.equal(tf.argmax(y_label,1),tf.argmax(y_predict,1)) #计算精度cast():转换值的类型,reduce_mean():计算平均值 accuracy tf.reduce_mean(tf.cast(correct_prediction,float)) #开始训练--------------------------------------------------------------------- trainEpochs 15 #epoch batchSize 100 totalBatchs int(len(mnist.train.images)/batchSize) #一个周期总的批次 loss_list[];epoch_list[];accuracy_list[] #记录训练过程的loss,epoch,accuracy from time import time startTime time() sess tf.Session() sess.run(tf.global_variables_initializer()) for epoch in range(trainEpochs): for i in range(totalBatchs): batch_x, batch_y mnist.train.next_batch(batchSize) #读取下一个批次的数据循环读取 sess.run(optimizer, feed_dict{x:batch_x,y_label:batch_y}) loss, acc sess.run([loss_function,accuracy],feed_dict\ {x:mnist.validation.images,y_label:mnist.validation.labels}) epoch_list.append(epoch) loss_list.append(loss) accuracy_list.append(acc) print(Train Epoch:, %02d%(epoch1), Loss,\ {:.9f}.format(loss), Accuary,acc) duration time()-startTime print(Train Finished takes:, duration) #训练结果显示-------------------------------------------------------------------- %matplotlib inline fig plt.gcf()#获取当前的figure图 fig.set_size_inches(4,2) plt.plot(epoch_list, loss_list, labelloss) plt.ylabel(loss) plt.xlabel(epoch) plt.legend([loss], locupper right) plt.plot(epoch_list,accuracy_list,labelaccuracy) fig plt.gcf() fig.set_size_inches(4,2) plt.ylim(0.8,1)#设置y轴范围 plt.ylabel(accuracy) plt.xlabel(epoch) plt.legend([accuarcy], locupper right) #评估------------------------------------------------------------------------------ #测试集test准确率 print(Accuracy:, sess.run(accuracy,feed_dict\ {x:mnist.test.images,y_label:mnist.test.labels})) #预测test prediction_result sess.run(tf.argmax(y_predict,1),feed_dict{x:mnist.test.images}) #显示真实值和预测值及图像 plot_images_labels_prediction(mnist.test.images,mnist.test.labels,prediction_result,0)