这节课，我们来学习如何用GPU训练模型，快的起飞
以及接触卷积神经网络并用keras搭建一个卷积神经网络做一个图片分类
下一节是一个卷积神经网络的项目，冲冲冲

import tensorflow as tf
tf.__version__,tf.config.list_physical_devices('GPU') # 查看能否使用gpu

('2.6.0', [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')])

使用Gpu训练模型，无需更改任何代码

!nvidia-smi # 查看gpu信息

Mon Oct  4 09:19:51 2021       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 470.74       Driver Version: 460.32.03    CUDA Version: 11.2     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Tesla K80           Off  | 00000000:00:04.0 Off |                    0 |
| N/A   37C    P8    28W / 149W |      3MiB / 11441MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

一、数据集

1.下载数据集

# 下载数据集
cifar10 = tf.keras.datasets.cifar10
(x_train,y_trian),(x_test,y_test) = cifar10.load_data()

x_train.shape,y_trian.shape,x_test.shape,y_test.shape

((50000, 32, 32, 3), (50000, 1), (10000, 32, 32, 3), (10000, 1))

2.归一化

x_train = x_train.astype(float) / 255.0
x_test = x_test.astype(float) / 255.0

• 这里并没有进行独热编码，后面解释，其实就是换了一个损失函数。

3.查看图片所属分类(更好了解数据集，并无其余作用)

import matplotlib.pyplot as plt

def show_label(img_ndarray,y_ndarray):
  # 展示图片
  plt.imshow(img_ndarray)
  # 类别
  classes=('plane','car','bird','cat','deer','dog','frog','horse','ship','truck')
  plt.title(f'label:{classes[y_ndarray[0]]}')
  plt.show()

show_label(x_train[0],y_trian[0])

二、模型

def create_model():
  model = tf.keras.models.Sequential()
  # 卷积层一 同时也充当输入层
  model.add(tf.keras.layers.Conv2D(filters=32, # 输出通道数
                  kernel_size=(3,3), # 卷积核(权重矩阵大小,3*3/5*5)
                  input_shape=(32,32,3),# 卷积层作为输入层的时候可以接受三维数据,其余位置只能处理二维数据
                  activation='relu',
                  padding='same' # 输入和输出形状保持一致，卷积层不影响图片大小，但影响图片个数
                  ))
  # 防止过拟合
  model.add(tf.keras.layers.Dropout(rate=0.3)) # 30%的参数不会参与优化
  # 池化层一
  model.add(tf.keras.layers.MaxPooling2D(pool_size=(2,2))) # 池化窗口大小，会改变图像的大小，变为16*16
  # 卷积层二
  model.add(tf.keras.layers.Conv2D(filters=64, # 输出通道数
                  kernel_size=(3,3), # 卷积核(权重矩阵大小,3*3/5*5)
                  activation='relu',
                  padding='same' # 输入和输出形状保持一致，卷积层不影响图片大小，但影响图片个数
                  ))
  # 防止过拟合
  model.add(tf.keras.layers.Dropout(rate=0.3)) # 30%的参数不会参与优化
  # 池化层二
  model.add(tf.keras.layers.MaxPooling2D(pool_size=(2,2))) # 池化窗口大小，会改变图像的大小，变为8*8
  # ————————————————图像最终是64*8*8————————————————

  # 平坦层，因为全连接层只能接收一维数据
  model.add(tf.keras.layers.Flatten())
  # 全连接层
  model.add(tf.keras.layers.Dense(units=128,activation='relu',kernel_initializer='normal'))
  # 输出层
  model.add(tf.keras.layers.Dense(units=10,activation='softmax',kernel_initializer='normal'))
  return model

model = create_model()

model.summary()

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 32, 32, 32)        896       
_________________________________________________________________
dropout (Dropout)            (None, 32, 32, 32)        0         
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 16, 16, 32)        0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 16, 16, 64)        18496     
_________________________________________________________________
dropout_1 (Dropout)          (None, 16, 16, 64)        0         
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 8, 8, 64)          0         
_________________________________________________________________
flatten (Flatten)            (None, 4096)              0         
_________________________________________________________________
dense (Dense)                (None, 128)               524416    
_________________________________________________________________
dense_1 (Dense)              (None, 10)                1290      
=================================================================
Total params: 545,098
Trainable params: 545,098
Non-trainable params: 0
_________________________________________________________________

三、模型训练

1.训练

trian_epochs = 20 
batch_size = 50

model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
      loss='sparse_categorical_crossentropy',# 无序对标签进行独热编码的损失函数
      metrics = ['accuracy'],
      )

train_history = model.fit(x_train,y_trian,validation_split=0.2,epochs=trian_epochs,verbose=2,batch_size=batch_size)

Epoch 1/20
800/800 - 8s - loss: 1.5125 - accuracy: 0.4525 - val_loss: 1.3550 - val_accuracy: 0.5539
Epoch 2/20
800/800 - 6s - loss: 1.1463 - accuracy: 0.5940 - val_loss: 1.2083 - val_accuracy: 0.6048
Epoch 3/20
800/800 - 6s - loss: 0.9975 - accuracy: 0.6472 - val_loss: 1.0934 - val_accuracy: 0.6406
Epoch 4/20
800/800 - 6s - loss: 0.8972 - accuracy: 0.6850 - val_loss: 1.0181 - val_accuracy: 0.6730
Epoch 5/20
800/800 - 6s - loss: 0.8191 - accuracy: 0.7134 - val_loss: 0.9545 - val_accuracy: 0.6891
Epoch 6/20
800/800 - 6s - loss: 0.7512 - accuracy: 0.7358 - val_loss: 0.9383 - val_accuracy: 0.6938
Epoch 7/20
800/800 - 6s - loss: 0.6895 - accuracy: 0.7555 - val_loss: 0.9067 - val_accuracy: 0.6962
Epoch 8/20
800/800 - 6s - loss: 0.6358 - accuracy: 0.7755 - val_loss: 0.8834 - val_accuracy: 0.7043
Epoch 9/20
800/800 - 6s - loss: 0.5869 - accuracy: 0.7907 - val_loss: 0.8854 - val_accuracy: 0.6961
Epoch 10/20
800/800 - 6s - loss: 0.5375 - accuracy: 0.8096 - val_loss: 0.8673 - val_accuracy: 0.7052
Epoch 11/20
800/800 - 6s - loss: 0.4881 - accuracy: 0.8240 - val_loss: 0.8950 - val_accuracy: 0.6957
Epoch 12/20
800/800 - 6s - loss: 0.4540 - accuracy: 0.8375 - val_loss: 0.8588 - val_accuracy: 0.7065
Epoch 13/20
800/800 - 6s - loss: 0.4181 - accuracy: 0.8489 - val_loss: 0.8873 - val_accuracy: 0.6994
Epoch 14/20
800/800 - 6s - loss: 0.3888 - accuracy: 0.8598 - val_loss: 0.8799 - val_accuracy: 0.7038
Epoch 15/20
800/800 - 6s - loss: 0.3578 - accuracy: 0.8716 - val_loss: 0.8731 - val_accuracy: 0.7071
Epoch 16/20
800/800 - 6s - loss: 0.3334 - accuracy: 0.8801 - val_loss: 0.9532 - val_accuracy: 0.6874
Epoch 17/20
800/800 - 6s - loss: 0.3074 - accuracy: 0.8903 - val_loss: 0.9313 - val_accuracy: 0.6975
Epoch 18/20
800/800 - 6s - loss: 0.2800 - accuracy: 0.8999 - val_loss: 0.9566 - val_accuracy: 0.7015
Epoch 19/20
800/800 - 6s - loss: 0.2724 - accuracy: 0.9024 - val_loss: 0.9659 - val_accuracy: 0.6975
Epoch 20/20
800/800 - 6s - loss: 0.2618 - accuracy: 0.9052 - val_loss: 0.9652 - val_accuracy: 0.7044

train_history.history.keys()

dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])

2.可视化训练过程

def show_train_history(train_history,train_metrics,val_metrics):
  plt.plot(train_history[train_metrics])
  plt.plot(train_history[val_metrics])
  plt.title('Trian History')
  plt.ylabel(train_metrics)
  plt.xlabel('epoch')
  plt.legend(['trian','validation'],loc='upper left')
  plt.show()

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

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

3.评估模型

test_loss,test_acc = model.evaluate(x_test,y_test,verbose=2)
test_loss,test_acc

313/313 - 1s - loss: 0.9629 - accuracy: 0.7017





(0.9629493951797485, 0.70169997215271)

四、预测

1.预测

prd = model.predict(x_test)
predict = tf.argmax(prd,axis=1).numpy()

int(y_test[0]),predict[0]

(3, 3)

2.可视化预测结果

# 定义可视化函数
import numpy as np
import matplotlib.pyplot as plt
classes=('plane','car','bird','cat','deer','dog','frog','horse','ship','truck')
def plot_image_labels_prediction(images, # 图像列表
                 labels, # 标签列表
                 preds, # 预测值列表
                 index=0, # 从第index个开始
                 num=10 # 缺省一次显示10幅
                 ):
  fig = plt.gcf() # 获取当前图表
  fig.set_size_inches(12,6) # 1英寸等于2.54cm
  if num > 10:
    num = 10 # 最多显示10个子图
  for i in range(0,num):
    ax = plt.subplot(2,5,i+1)
    ax.imshow(images[index]) # 显示第index个图像
    title = str(i)+','+classes[labels[index][0]] # 构建该图上要显示的title信息
    if len(preds) > 0:
      title += ',predict='+str(classes[preds[index]])

    ax.set_title(title,fontsize=10) # 显示图上的title信息
    ax.set_xticks([]) # 不显示坐标轴
    ax.set_yticks([])
    index = index + 1
  plt.show()

plot_image_labels_prediction(x_test,y_test,predict)

好好生活，好好coding。