活动地址:CSDN21天学习挑战赛
参考文章:https://mtyjkh.blog.csdn.net/article/details/116992196
如果没有则可不设置,则可以直接使用CPU。
import tensorflow as tf
# gpus = tf.config.list_pysical_devices("GPU")
# if gpus:
# gpu0 = gpus[0] # 如果有多个gpu,仅使用下标为0的那个
# 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)
import tensorflow as tf
from tensorflow.keras import datasets, layers, models
import matplotlib.pyplot as plt
# 下载数据集
(train_images, train_labels), (test_images, test_labels) = datasets.fashion_mnist.load_data()
加载数据集会返回四个Numpy数组:
图像是28 * 的Numpy数组,像素值介于0-255。标签是整数数组,介于0-9,。这些标签对应于图像所代表的服装类型:
标签 | 类 | 标签 | 类 |
---|---|---|---|
0 | T恤/上衣 | 1 | 裤子 |
2 | 套头衫 | 3 | 连衣裙 |
4 | 外套 | 5 | 凉鞋 |
6 | 衬衫 | 7 | 运动鞋 |
8 | 包 | 9 | 短靴 |
# 将像素的值标准化至0-1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
# 输出:((60000, 28, 28), (10000, 28, 28), (60000,), (10000,))
# 调整数据到我们需要的格式
train_images = train_images.reshape((60000, 28, 28, 1))
test_images = test_images.reshape((10000, 28, 28, 1))
train_images.shape, test_images.shape, train_labels.shape, test_labels.shape
# 输出:((60000, 28, 28, 1), (10000, 28, 28, 1), (60000,), (10000,))
class_names = ["T-shirt/top", "Trouser", "Pullover", "Dress", "Coat", "Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"]
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]])
plt.show()
卷积神经网络(CNN)的输入是张量(Tensor)形式的(image_height, image_width, color_channels),包含了图像高度、宽度及颜色信息。不需要输入batch_size。color_channels为(R,G,B)分别对应RGB三个颜色的通道(color channel)。
在示例中的CNN输入是fashion_mnist数据集中的图片,形状是(28,28,1)即灰度图像。我们需要在声明第一层时将形状赋值给参数input_shape。
model = models.Sequential([
layers.Conv2D(32, (3, 3), activation='relu', input_shape=[28, 28, 1]), # 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 ,3*3卷积核
layers.MaxPooling2D((2, 2)), # 池化层2, 2*2采样
layers.Conv2D(64, (3, 3), activation='relu'), # 卷积层3 ,3*3卷积核
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,输出预期效果
])
# 打印网络结构
model.summary()
在准备对模型进行训练之前,还需要再对其进行一些设置。以下内容是在模型的编译步骤中添加的:
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
history = model.fit(train_images, train_labels, epochs=10,
validation_data=(test_images, test_labels))
# 训练10轮
预测结果是一个包含10个数组的数组。代表10种不同类型的服装的“置信度”。
plt.imshow(test_images[1])
import numpy as np
pre = model.predict(test_images)
print(class_names[np.argmax(pre[1])])
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)