Tensorflow学习

Tensorflow学习-MNIST数据集

Softmax

①数据集导入，keras自带的下载或者从某盘提取点击获取数据集，提取码：45yf

#导入数据集from keras.datats import mnist(x_train,y_train),(x_test,y_test)=mnist.load_data('E:/TensorFlow_mnist/MNIST_data/mnist.npz')print(x_train.shape,type(x_train))  #60000张28*28的图片print(y_train.shape,type(y_train))  #60000个标签

②图像和数据类型的转化

#将图像28*28的转换成784X_train = x_train.reshape(60000,784)X_test = x_test.reshape(10000,784)print(X_train.shape,type(X_train))print(X_test.shape,type(X_test))#将数据转换为float32X_train=X_train.astype('float32')X_test=X_test.astype('float32')#数据归一化X_train/=255X_test/=255

③统计训练数据集中各标签的数量并可视化展示

#统计训练数据中的各标签数量import numpy as npimport matplotlib.pyplot as pltlabel,count=np.unique(y_train,return_counts=True)print(label,count)#lable的可视化输出fig = plt.figure()plt.bar(label,count,width=0.7,align='center')plt.title("Label Distribution")plt.xlabel("Label")plt.ylabel("Count")plt.xticks(label)plt.ylim(0,7500)for a,b in zip(label,count):    plt.text(a,b,'%d' %b,ha='center',va='bottom',fontsize=10)    plt.show()

输出结果：

④标签编码
one-hot编码的实现

rom keras.utils import  np_utilsn_class=10print("Shape before one-hot encoding: ",y_train.shape)Y_train=np.utils.to_categorical(y_train,n_class)print("Shape after one-hot encodin公文处理g: ",Y_train.shape)Y_test = np_utils.to_categorical(y_test,n_class)print(y_train[0])print(Y_train[0])

可以看看输出为：

5 #one-hot之前的标签[0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]  #one-hot之后的标签

⑤定义神经网络
使用Keras quential model定义神经网络

from keras.utils import  np_utilsn_class=10print("Shape before one-hot encoding: ",y_train.shape)Y_train=np_utils.to_categorical(y_train,n_class)print("Shape after one-hot encoding: ",Y_train.shape)Y_test = np_utils.to_categorical(y_test,n_class)# print(y_train[0])# print(Y_train[0])from keras.models import Sequentialfrom keras.layers.core import Den,Activationmodel = Sequential()model.add(Den(公司法2014512,input_shape=(784,)))#全连接网络，512个神经元。输入784长度的向量对应输入的28*28model.add(Activation('relu'))#激活函数选择relumodel.add(Den(512))#全连接网络，512个神经元。输入的是上一层输出的数据model.add(Activation('relu'))#激活函数为relumodel.add(Den(10))model.add(Activation('softmax'))

编译模型

model.compile(loss='categorical_crosntropy',metrics=['accuracy'],optimizer='adam')#这一步之后得到了完整的数据流图

训练模型，并将指标保存到history中

history = model.fit(X_train,                    Y_train,                    batch_size=128,#每次128张图                    epochs=5,#一共训练5次60000张图，总30W图                    verbo=2,                    validation_data=(X_test,Y_test))

结果显示：

Epoch 氢氯酸1/5 - 7s - loss: 0.2156 - acc: 0.9358 - val_loss: 0.1063 - val_acc: 0.9676Epoch 2/5 - 5s - loss: 0.0797 - acc: 0.9757 - val_loss: 0.0754 - val_acc: 0.9764Epoch 3/5 - 5s - loss: 0.0496 - acc: 0.9842 - val_loss: 0.0675 - val_acc: 0.9778Epoch 4/5 - 5s - loss: 0.0345 - acc: 0.9889 - val_loss: 0.0745 - val_acc: 0.9780Epoch 5/5 - 5s - loss: 0.0249 - acc: 0.9918 - val_loss: 0.0763 - val_acc: 0.9790

⑥指标可视化展示

fig = plt.figure()plt.subplot(2,1,1)plt.plot(history.history['acc'])plt.plot(history.history['val_acc'])#测试集的准确率plt.title('Model Accuracy')plt.ylabel('accuracy')plt.xlabel('epoch')plt.legend(['train','test'],loc='lower right')plt.subplot(2,1,2)plt.plot(history.history['loss'])plt.plot(history.history['val_loss'])plt.title('Model Loss')plt.ylabel('loss')plt.xlabel('epoch')plt.legend(['train','test'],loc=loladc'upper right')plt.show()

图表展示：

⑦保存模型
keras将模型保存成HDF5文件格式

import osimport tensorflow.gfile as gfilesave_dir = "../TensorFlow_mnist/model"if gfile.Exists(save_dir):    gfile.DeleteRecursively(save_dir)gfile.MakeDirs(save_dir)model_name = 'keras_mnist.h5'model_path=os.path.join(save_dir,model_name)model.save(model_path)print('Saved trained model at %s' %model_path)

保存为如下形式

⑧加载模型

from keras.models import load_modelmnist_modle = load_model(model_path)loss_and_metrics=mnist_modle.evaluate(X_test,Y_test,verbo=2)print("Test Loss:{}".format(loss_and_metrics[0]))print("Test Accuracy:{}%".format(loss_and_metrics[1]*100))predicted_class = mni阳西大树岛st_modle.predict_calss(X_test)correct_indices = np.nonzero(predicted_class==y_test)[0]incorrect_indices=np.nonzero(predicted_class!=y_test)[0]print("Classified correctly count: {}".format(len(correct_indices)))print("Classified incorrectly count: {}".format(len(incorrect_indices)))

本文地址：https://blog.csdn.net/weixin_48905043/article/details/107664065