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最新推荐文章于 2025-06-14 22:22:09 发布

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文为「365天深度学习训练营」内部文章
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1.设置GPU

import tensorflow as tf
gpus = tf.config.list_physical_devices("GPU")

if gpus:
    gpu0 = gpus[0] #如果有多个GPU，仅使用第0个GPU
    tf.config.experimental.set_memory_growth(gpu0, True) #设置GPU显存用量按需使用
    tf.config.set_visible_devices([gpu0],"GPU")

2.导入数据

import tensorflow as tf
from tensorflow.keras import datasets, layers, models
import matplotlib.pyplot as plt

(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()

Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
170498071/170498071 [==============================] - 747s 4us/step

# 将像素的值标准化至0到1的区间内。
train_images, test_images = train_images / 255.0, test_images / 255.0

train_images.shape,test_images.shape,train_labels.shape,test_labels.shape

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

class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer','dog', 'frog', 'horse', 'ship', 'truck']

plt.figure(figsize=(20,10))
for i in range(20):
    plt.subplot(5,10,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(train_images[i], cmap=plt.cm.binary)
    plt.xlabel(class_names[train_labels[i][0]])
plt.show()

model = models.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)), #卷积层1，卷积核3*3
    layers.MaxPooling2D((2, 2)),                   #池化层1，2*2采样
    layers.Conv2D(64, (3, 3), activation='relu'),  #卷积层2，卷积核3*3
    layers.MaxPooling2D((2, 2)),                   #池化层2，2*2采样
    layers.Conv2D(64, (3, 3), activation='relu'),  #卷积层3，卷积核3*3
    
    layers.Flatten(),                      #Flatten层，连接卷积层与全连接层
    layers.Dense(64, activation='relu'),   #全连接层，特征进一步提取
    layers.Dense(10)                       #输出层，输出预期结果
])

model.summary()  # 打印网络结构

WARNING:tensorflow:From D:\dl\envs\pytorch_gpu\lib\site-packages\keras\src\backend.py:873: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.

WARNING:tensorflow:From D:\dl\envs\pytorch_gpu\lib\site-packages\keras\src\layers\pooling\max_pooling2d.py:161: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 30, 30, 32)        896       
                                                                 
 max_pooling2d (MaxPooling2  (None, 15, 15, 32)        0         
 D)                                                              
                                                                 
 conv2d_1 (Conv2D)           (None, 13, 13, 64)        18496     
                                                                 
 max_pooling2d_1 (MaxPoolin  (None, 6, 6, 64)          0         
 g2D)                                                            
                                                                 
 conv2d_2 (Conv2D)           (None, 4, 4, 64)          36928     
                                                                 
 flatten (Flatten)           (None, 1024)              0         
                                                                 
 dense (Dense)               (None, 64)                65600     
                                                                 
 dense_1 (Dense)             (None, 10)                650       
                                                                 
=================================================================
Total params: 122570 (478.79 KB)
Trainable params: 122570 (478.79 KB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

model.compile(optimizer='adam',

loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),

metrics=['accuracy'])

WARNING:tensorflow:From D:\dl\envs\pytorch_gpu\lib\site-packages\keras\src\optimizers\__init__.py:309: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.

history = model.fit(train_images, train_labels, epochs=10, 
                    validation_data=(test_images, test_labels))

Epoch 1/10
WARNING:tensorflow:From D:\dl\envs\pytorch_gpu\lib\site-packages\keras\src\utils\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.

WARNING:tensorflow:From D:\dl\envs\pytorch_gpu\lib\site-packages\keras\src\engine\base_layer_utils.py:384: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.

1563/1563 [==============================] - 7s 4ms/step - loss: 1.5403 - accuracy: 0.4390 - val_loss: 1.2682 - val_accuracy: 0.5355
Epoch 2/10
1563/1563 [==============================] - 6s 4ms/step - loss: 1.1611 - accuracy: 0.5908 - val_loss: 1.1016 - val_accuracy: 0.6100
Epoch 3/10
1563/1563 [==============================] - 6s 4ms/step - loss: 1.0091 - accuracy: 0.6465 - val_loss: 0.9856 - val_accuracy: 0.6553
Epoch 4/10
1563/1563 [==============================] - 6s 4ms/step - loss: 0.9082 - accuracy: 0.6832 - val_loss: 0.9478 - val_accuracy: 0.6706
Epoch 5/10
1563/1563 [==============================] - 6s 4ms/step - loss: 0.8336 - accuracy: 0.7077 - val_loss: 0.8741 - val_accuracy: 0.6900
Epoch 6/10
1563/1563 [==============================] - 7s 4ms/step - loss: 0.7710 - accuracy: 0.7303 - val_loss: 0.9035 - val_accuracy: 0.6874
Epoch 7/10
1563/1563 [==============================] - 7s 4ms/step - loss: 0.7231 - accuracy: 0.7475 - val_loss: 0.8902 - val_accuracy: 0.6953
Epoch 8/10
1563/1563 [==============================] - 7s 4ms/step - loss: 0.6778 - accuracy: 0.7614 - val_loss: 0.8532 - val_accuracy: 0.7081
Epoch 9/10
1563/1563 [==============================] - 7s 4ms/step - loss: 0.6401 - accuracy: 0.7744 - val_loss: 0.8627 - val_accuracy: 0.7107
Epoch 10/10
1563/1563 [==============================] - 7s 4ms/step - loss: 0.5973 - accuracy: 0.7918 - val_loss: 0.8672 - val_accuracy: 0.7102

```
plt.imshow(test_images[1])
```

import numpy as np

pre = model.predict(test_images)
print(class_names[np.argmax(pre[1])])

313/313 [==============================] - 1s 2ms/step
ship

import matplotlib.pyplot as plt

plt.plot(history.history['accuracy'], label='accuracy')
plt.plot(history.history['val_accuracy'], label = 'val_accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.ylim([0.5, 1])
plt.legend(loc='lower right')
plt.show()

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

313/313 - 1s - loss: 0.8672 - accuracy: 0.7102 - 562ms/epoch - 2ms/step

```
print(test_acc)
```
```
0.7102000117301941
```
心得体会
构建CNN网络后深入了解了CNN的整个网络架构了解了划分数据集的相关代码更加深入了解了深度学习下一周会继续坚持