tensorflow实验六------MNIST手写数字识别进阶 多层神经网络与应用

载入数据

import tensorflow as tfimport numpy as npimport matplotlib.pyplot as plt%matplotlib inlineprint(tf.__version__)

mnist = tf.keras.datasets.mnist(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

划分数据集

total_num = len(train_images)valid_split = 0.2train_num = int(total_num*(1-valid_split))train_x = train_images[:train_num]train_y = train_labels[:train_num]valid_x =  train_images[train_num:]valid_y = train_labels[train_num:]test_x = test_imagestest_y = test_labels

数据塑形

train_x = train_x.reshape(-1,784)valid_x = valid_x.reshape(-1,784)test_x = test_x.reshape(-1,784)

特征数据归一化

train_x = tf.cast(train_x/255.0,tf.float32)valid_x = tf.cast(valid_x/255.0,tf.float32)test_x = tf.cast(test_x/255.0,tf.float32)

标签数据独热编码

train_y = tf.one_hot(train_y,depth=10)valid_y = tf.one_hot(valid_y,depth=10)test_y = tf.one_hot(test_y,depth=10)

构建模型

Input_Dim = 784H1_NN = 64W1 = tf.Variable(tf.random.normal([Input_Dim,H1_NN],mean=0.0,stddev=1.0,dtype=tf.float32))B1 = tf.Variable(tf.zeros([H1_NN]),dtype = tf.float32)

创建待优化变量

H2_NN = 32W2 = tf.Variable(tf.random.normal([H1_NN,H2_NN],mean=0.0,stddev=1.0,dtype=tf.float32))B2 = tf.Variable(tf.zeros([H2_NN]),dtype = tf.float32)

Output_Dim = 10W3 = tf.Variable(tf.random.normal([H2_NN,Output_Dim],mean=0.0,stddev=1.0,dtype=tf.float32))B3= tf.Variable(tf.zeros([Output_Dim]),dtype = tf.float32)

W = [W1,W2,W3]B = [B1,B2,B3]

定义模型前向计算

def model(x, w, b):    x = tf.matmul(x, w[0]) + b[0]    x = tf.nn.relu(x)    x = tf.matmul(x, w[1]) + b[1]    x = tf.nn.relu(x)    x = tf.matmul(x, w[2]) + b[2]    pred = tf.nn.softmax(x)    return pred

定义损失函数

定义交叉熵损失函数

def loss(x, y, w, b):    pred = model(x, w, b)    loss_ = tf.keras.losses.categorical_crossentropy(y_true=y, y_pred=pred)    return tf.reduce_mean(loss_)

设置训练超参数

training_epochs = 20batch_size = 50learning_rate= 0.01

定义梯度计算函数

def grad(x, y, w, b):    var_list = w + b;    with tf.GradientTape() as tape: loss_ = loss(x, y, w, b)    return tape.gradient(loss_,var_list)

选择优化器

optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)

定义准确率

def accuracy(x,y,w,b):#定义准确模型    pred = model(x,w,b)    correct_prediction = tf.equal(tf.argmax(pred,1),tf.argmax(y,1))    return tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

训练模型

steps = int(train_num/batch_size)#总次数loss_list_train = []#定义函数loss_list_valid = []acc_list_train = []acc_list_valid = []for epoch in range (training_epochs):#循环    for step in range(steps): xs = train_x[step*batch_size:(step+1)*batch_size]#训练模型的计算 ys = train_y[step*batch_size:(step+1)*batch_size]  grads = grad(xs,ys,W,B)#梯度计算 optimizer.apply_gradients(zip(grads, W+B)) loss_train = loss(train_x,train_y,W,B).numpy()    loss_valid = loss(valid_x,valid_y,W,B).numpy()    acc_train = accuracy(train_x,train_y,W,B).numpy()    acc_valid = accuracy(valid_x,valid_y,W,B).numpy()    loss_list_train.append(loss_train)    loss_list_valid.append(loss_valid)    acc_list_train.append(acc_train)    acc_list_valid.append(acc_valid)    print("epoch={:3d},train_loss={:.4f},train_acc={:.4f},val_loss={:.4f},val_acc={:.4f}".format(epoch+1,loss_train,acc_train,loss_valid,acc_valid))

plt.xlabel("Epochs")plt.ylabel("Loss")plt.plot(loss_list_train,'blue',label="Train Loss")plt.plot(loss_list_valid,'red',label='Valid Loss')plt.legend(loc=1)

plt.xlabel("Epochs")plt.ylabel("Accuracy")plt.plot(acc_list_train,'blue',label="Train Loss")plt.plot(acc_list_valid,'red',label='Valid Loss')plt.legend(loc=1)

acc_test = accuracy(test_x,test_y,W,B).numpyprint("Test accuracy:",acc_test)

def predict(x,w,b):#定义预测模型    pred = model(x,w,b)    result = tf.argmax(pred,1).numpy()    return result

pred_test=predict(test_x,W,B)

pred_test[0]

import matplotlib.pyplot as pltimport numpy as npdef plot_images_labels_prediction(images,     labels,     preds,     index=0,     num=10):    fig = plt.gcf() #获取当前的图表    fig.set_size_inches(10,4)    if num > 10: num = 10   #最多显示十个子图    for i in range(0,num): ax = plt.subplot(2,5,i+1)  #获取当前要处理的子图  ax.imshow(np.reshape(images[index],(28,28)),cmap='binary')  title = "label=" + str(labels[index]) if len(preds)>0:     title +=",predict=" + str(labels[index])      ax.set_title(title,fontsize=10) ax.set_xticks([]); ax.set_yticks([]) index = index + 1    plt.show()

plot_images_labels_prediction(test_images,test_labels,pred_test,10,10)

train_images = train_images / 255.0test_images = test_images / 255.0

train_labels_ohe = tf.one_hot(train_labels,depth = 10).numpy()test_labels_ohe = tf.one_hot(test_labels, depth = 10).numpy()

新建一个序列模型

model = tf.keras.models.Sequential()

添加输入层

model.add(tf.keras.layers.Flatten(input_shape=(28, 28)))

添加隐藏层

model.add(tf.keras.layers.Dense(units = 64,   kernel_initializer = 'normal',   activation = 'relu'))

model.add(tf.keras.layers.Dense(units = 32,   kernel_initializer = 'normal',   activation = 'relu'))

添加输出层

model.add(tf.keras.layers.Dense(10,activation = 'softmax'))

模型摘要

model.summary()

定义训练模式

model.compile(optimizer = 'adam',      loss = 'categorical_crossentropy',      metrics = ['accuracy'])

设置训练参数

train_epochs = 10batch_size =30

模型训练

train_history=model.fit(train_images, train_labels_ohe,  validation_split = 0.2,  epochs = train_epochs,  batch_size = batch_size,  verbose = 2)

训练过程指标数据

train_history.history

训练过程指标可视化

import matplotlib.pyplot as pltdef show_train_history(train_history,train_metric,val_metric):    plt.plot(train_history.history[train_metric])    plt.plot(train_history.history[val_metric])    plt.title('Train History')    plt.ylabel(train_metric)    plt.xlabel('Epoch')    plt.legend(['train','validation'],loc='upper left')    plt.show()

show_train_history(train_history,'loss','val_loss')

show_train_history(train_history,'accuracy','val_accuracy')

评估模型

test_loss,test_acc = model.evaluate(test_images,test_labels_ohe,verbose = 2)

模型的度量指标

yy=model.evaluate(test_images,test_labels_ohe,verbose=2)

yy

model.metrics_names

应用模型

test_pred = model.predict(test_images)

test_pred.shape

np.argmax(test_pred[0])

应用模型

test_pred = model.predict_classes(test_images)

test_pred[0]

test_labels[0]