class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'] plt.figure(figsize=(10,10))什么意思
时间: 2023-06-03 20:03:20 浏览: 62
这段代码是在使用Python中的matplotlib库绘制一个10x10的图像,其中每个小方格代表了class_names中的一个类别,分别为航空器、汽车、鸟类、猫、鹿、狗、青蛙、马、船舶和卡车。这个图像可能是用于可视化分类模型的输出结果。
相关问题
from keras.datasets import cifar10 import matplotlib.pyplot as plt from keras.layers import Conv2D, MaxPooling2D from keras.utils import np_utils from keras.models import Sequential from keras.layers import Dense,Dropout,Flatten (train_image,train_label),(test_image,test_label)=cifar10.load_data() dict={0:'airplane',1:'automobile',2:'bird',3:'cat',4:'deer',5:'dog',6:'frog',7:'horse',8:'ship',9:'truck'} for i in range(0,12): plt.subplot(3,4,i+1) plt.imshow(train_image[i]) plt.title(dict[train_label[i,0]],fontsize=8) #plt.show() #步骤二:数据预处理 Train_image=train_image.astype('float32')/255 Test_image=test_image.astype('float32')/255 Train_Onehot=np_utils.to_categorical(train_label) Train_Onehot=np_utils.to_categorical(test_label) #步骤三:建立模型 model=Sequential() model.add(Conv2D(filters=32, kernel_size=(3,3), input_shape=(32,32,3), padding='same', activation='relu', )) model.add(Dropout(0.25)) model.add(MaxPooling2D( pool_size=(2,2))) model.add(Conv2D(filters=64, kernel_size=(3,3), padding='same', activation='relu', )) #添加dropout,避免过拟合 model.add(Dropout(0.25)) #添加池化层2 model.add(MaxPooling2D(pool_size=(2,2))) #添加平坦层 model.add(Flatten()) #添加dropout model.add(Dropout(0.25)) #添加隐藏层 model.add(Dense(1024,activation='relu')) #添加dropout model.add(Dropout(0.25)) #输出层 model.add(Dense(units=10,activation='softmax')) #打印模型 print(model.summary()) #步骤四:模型训练 model.compile( optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'],) #训练模型 #epoch:训练5个周期 #batch_size:每一批次128项数据 #verbose=2:显示训练过程 #validation_split=0.2. model.fit(x=Train_image, y=Train_Onehot, batch_size=128, epochs=10, verbose=2, validation_split=0.2, ) model.save('cifar10.h5')出现了Input arrays should have the same number of samples as target arrays. Found 50000 input samples and 10000 target samples.错误,我应该怎么改
在这段代码中,似乎有一个问题,就是在数据预处理的步骤中,将测试集的目标变量也转换为了one-hot编码,导致训练集和测试集的目标变量数量不匹配。为了解决这个问题,你需要将以下两行代码中的 "Train_Onehot" 改为 "Test_Onehot":
```
Train_Onehot=np_utils.to_categorical(train_label)
Train_Onehot=np_utils.to_categorical(test_label)
```
修改为:
```
Train_Onehot=np_utils.to_categorical(train_label)
Test_Onehot=np_utils.to_categorical(test_label)
```
这样就可以将测试集的目标变量正确地转换为one-hot编码,从而避免 "Input arrays should have the same number of samples as target arrays." 的错误。
用tensorflow对cifar数据进行分类,并对分类结果按类别继续分类,展示某类最终结果
好的,以下是基于 TensorFlow 的 CIFAR-10 数据集分类代码,同时包含了对分类结果按类别继续分类和展示某类最终结果的功能:
```python
import tensorflow as tf
from tensorflow.keras import datasets, layers, models
import matplotlib.pyplot as plt
import numpy as np
# 加载 CIFAR-10 数据集
(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()
# 将像素值缩放到 0-1 范围内
train_images, test_images = train_images / 255.0, test_images / 255.0
# 类别标签
class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
'dog', 'frog', 'horse', 'ship', 'truck']
# 创建模型
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10))
# 编译模型
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
# 训练模型
history = model.fit(train_images, train_labels, epochs=10,
validation_data=(test_images, test_labels))
# 评估模型
test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
print('\nTest accuracy:', test_acc)
# 对分类结果按类别继续分类
pred_labels = np.argmax(model.predict(test_images), axis=1)
class_indices = [np.where(pred_labels == i)[0] for i in range(len(class_names))]
# 展示某类最终结果
class_name = 'cat' # 想要查看的类别名称
class_index = class_names.index(class_name)
selected_indices = class_indices[class_index][:10] # 选择前10个样本
selected_images = test_images[selected_indices]
# 预测选定的样本
predictions = model.predict(selected_images)
predicted_labels = np.argmax(predictions, axis=1)
# 显示选定的样本及其预测结果
plt.figure(figsize=(10, 10))
for i in range(len(selected_images)):
plt.subplot(5, 5, i+1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.imshow(selected_images[i], cmap=plt.cm.binary)
plt.xlabel(class_names[predicted_labels[i]])
plt.show()
```
代码中的注释已经解释了各个部分的功能,其中 `class_indices` 是一个列表,包含了对分类结果按类别继续分类后,每个类别的样本在 `test_images` 中的下标。我们可以通过 `class_names.index(class_name)` 来获取某个类别名称在 `class_names` 中的下标,然后再从 `class_indices` 中获取该类别的样本下标,并从中选出前10个样本进行展示。