tensorflow2.0|TensorFlow 从入门到精通（10）—— GPU模型训练和卷积神经网络与应用神经网络|深度学习|tensorflow

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 Oct4 09:19:51 2021 +-----------------------------------------------------------------------------+ | NVIDIA-SMI 470.74Driver Version: 460.32.03CUDA Version: 11.2| |-------------------------------+----------------------+----------------------+ | GPUNamePersistence-M| Bus-IdDisp.A | Volatile Uncorr. ECC | | FanTempPerfPwr:Usage/Cap|Memory-Usage | GPU-UtilCompute M. | |||MIG M. | |===============================+======================+======================| |0Tesla K80Off| 00000000:00:04.0 Off |0 | | N/A37CP828W / 149W |3MiB / 11441MiB |0%Default | |||N/A | +-------------------------------+----------------------+----------------------++-----------------------------------------------------------------------------+ | Processes:| |GPUGICIPIDTypeProcess nameGPU Memory | |IDIDUsage| |=============================================================================| |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 pltdef 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])

tensorflow2.0|TensorFlow 从入门到精通（10）—— GPU模型训练和卷积神经网络与应用

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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 ShapeParam # ================================================================= 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')

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

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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()