# tensorflow实验五—–MNIST手写数字识别

## 数据获取

``````mnist = tf.keras.datasets.mnist
``````

## 划分验证集

``````total_num = len(train_images)
valid_split = 0.2
train_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_images
test_y = test_labels
``````

``````valid_x.shape
``````

## 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)
``````

``````train_x[1]
``````

## 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)
``````
``````train_y
``````

## 构建模型

``````def model(x,w,b):
pred = tf.matmul(x,w) + b
return tf.nn.softmax(pred)
``````

## 定义模型变量

``````W = tf.Variable(tf.random.normal([784,10],mean=0.0,stddev=1.0,dtype=tf.float32))

B = tf.Variable(tf.zeros([10]),dtype = tf.float32)
``````

## 定义损失函数

``````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_)
``````

## 定义训练超参数

``````training_epochs = 20
batch_size = 50
learning_rate = 0.001
``````

## 定义梯度计算函数

``````def grad(x,y,w,b):
loss_ = loss(x,y,w,b)
``````

## 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):
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

``````total_step = int(train_num/batch_size)

loss_list_train = []
loss_list_valid = []
acc_list_train = []
acc_list_valid = []

for epoch in range (training_epochs):
for step in range(total_step):
xs = train_x[step*batch_size:(step+1)*batch_size]
ys = train_y[step*batch_size:(step+1)*batch_size]

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_lacc={:.4f}".format(epoch+1,loss_train,acc_train,loss_valid,acc_valid))
``````

## 显示训练过程数据

``````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)
``````

## 评估模型

``````acc_test = accuracy(test_x,test_y,W,B).numpy
print("Test accuracy:",acc_test)
``````

## 模型应用与可视化

``````def predict(x,w,b):
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 plt
import numpy as np
def plot_images_labels_prediction(images,
labels,
preds,
index=0,
num=10):
fig = plt.gcf()
fig.set_size_inches(10,4)
if num > 10:
num = 10
for i in range(0,num):
ax = plt.subplot(2,5,i+1)

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)
``````

THE END