keras实现resnet对四种天气分类，并绘制图像

好的，我可以为您提供大致的代码实现，但由于我无法进行代码编写和图像绘制，因此以下代码仅供参考。

首先，需要导入必要的库和模块：

import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.models import Model
from keras.layers import Input, Conv2D, BatchNormalization, Activation, MaxPooling2D, Dense, Flatten, Add
from keras.utils import np_utils
from keras.datasets import weather_classification

然后，读取和预处理数据集：

(x_train, y_train), (x_test, y_test) = weather_classification.load_data()

# 将像素值缩放到0-1之间
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.

# 将标签进行One-Hot编码
y_train = np_utils.to_categorical(y_train, 4)
y_test = np_utils.to_categorical(y_test, 4)

接下来，定义ResNet网络：

def Conv2d_BN(x, nb_filter, kernel_size, strides=(1,1), padding='same', name=None):
    if name is not None:
        bn_name = name + '_bn'
        conv_name = name + '_conv'
    else:
        bn_name = None
        conv_name = None

    x = Conv2D(nb_filter, kernel_size, padding=padding, strides=strides, activation='relu', name=conv_name)(x)
    x = BatchNormalization(axis=3, name=bn_name)(x)
    return x

def identity_block(input_tensor, nb_filter, kernel_size, name=None):
    if name is not None:
        conv_name_base = name + '_branch'
    else:
        conv_name_base = None

    x = Conv2d_BN(input_tensor, nb_filter=nb_filter, kernel_size=kernel_size, name=conv_name_base + '2a')
    x = Conv2d_BN(x, nb_filter=nb_filter, kernel_size=kernel_size, name=conv_name_base + '2b')
    x = Add()([x, input_tensor])
    x = Activation('relu')(x)
    return x

def conv_block(input_tensor, nb_filter, kernel_size, strides=(2, 2), name=None):
    if name is not None:
        conv_name_base = name + '_branch'
    else:
        conv_name_base = None

    x = Conv2d_BN(input_tensor, nb_filter=nb_filter, kernel_size=kernel_size, strides=strides, name=conv_name_base + '2a')
    x = Conv2d_BN(x, nb_filter=nb_filter, kernel_size=kernel_size, name=conv_name_base + '2b')
    shortcut = Conv2d_BN(input_tensor, nb_filter=nb_filter, kernel_size=1, strides=strides, name=conv_name_base + '1')
    x = Add()([x, shortcut])
    x = Activation('relu')(x)
    return x

def ResNet(input_shape, nb_classes):
    input_tensor = Input(shape=input_shape)
    x = Conv2d_BN(input_tensor, nb_filter=64, kernel_size=(7, 7), strides=(2, 2), padding='same', name='conv1')
    x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)

    x = conv_block(x, nb_filter=64, kernel_size=(3, 3), strides=(1, 1), name='conv2_1')
    x = identity_block(x, nb_filter=64, kernel_size=(3, 3), name='conv2_2')
    x = identity_block(x, nb_filter=64, kernel_size=(3, 3), name='conv2_3')

    x = conv_block(x, nb_filter=128, kernel_size=(3, 3), name='conv3_1')
    x = identity_block(x, nb_filter=128, kernel_size=(3, 3), name='conv3_2')
    x = identity_block(x, nb_filter=128, kernel_size=(3, 3), name='conv3_3')
    x = identity_block(x, nb_filter=128, kernel_size=(3, 3), name='conv3_4')

    x = conv_block(x, nb_filter=256, kernel_size=(3, 3), name='conv4_1')
    x = identity_block(x, nb_filter=256, kernel_size=(3, 3), name='conv4_2')
    x = identity_block(x, nb_filter=256, kernel_size=(3, 3), name='conv4_3')
    x = identity_block(x, nb_filter=256, kernel_size=(3, 3), name='conv4_4')
    x = identity_block(x, nb_filter=256, kernel_size=(3, 3), name='conv4_5')
    x = identity_block(x, nb_filter=256, kernel_size=(3, 3), name='conv4_6')

    x = conv_block(x, nb_filter=512, kernel_size=(3, 3), name='conv5_1')
    x = identity_block(x, nb_filter=512, kernel_size=(3, 3), name='conv5_2')
    x = identity_block(x, nb_filter=512, kernel_size=(3, 3), name='conv5_3')
    x = Flatten()(x)
    x = Dense(nb_classes, activation='softmax', name='fc')(x)

    model = Model(input_tensor, x)
    return model

然后，编译和训练网络：

model = ResNet(input_shape=(64, 64, 3), nb_classes=4)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()

history = model.fit(x_train, y_train, batch_size=64, epochs=20, validation_data=(x_test, y_test))

最后，评估和测试网络，并绘制图像：

# 评估网络
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

# 绘制训练和测试曲线
plt.figure(figsize=(10, 4))
plt.subplot(1, 2, 1)
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.subplot(1, 2, 2)
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()

# 绘制混淆矩阵
from sklearn.metrics import confusion_matrix
import itertools

def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print('Normalized confusion matrix')
    else:
        print('Confusion matrix, without normalization')

    print(cm)

    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)

    fmt = '.2f' if normalize else 'd'
    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, format(cm[i, j], fmt),
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")

    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')

y_pred = model.predict(x_test)
y_pred_classes = np.argmax(y_pred, axis=1)
y_true = np.argmax(y_test, axis=1)
confusion_mtx = confusion_matrix(y_true, y_pred_classes)
plot_confusion_matrix(confusion_mtx, classes=['sunny', 'cloudy', 'rainy', 'snowy'])

希望以上代码能够帮助您完成ResNet网络对四种天气的分类任务，并绘制相应的图像。