用卷积搭建五分类的EfficientNet代码，加上准确率和损失率可视化，并给出每一步的解释

以下是用卷积搭建五分类的EfficientNet代码，加上准确率和损失率可视化，并给出每一步的解释。

import tensorflow as tf
from tensorflow.keras.layers import Conv2D, BatchNormalization, Activation, GlobalAveragePooling2D, Dense, Dropout
from tensorflow.keras.models import Model

# 定义EfficientNet网络结构
def EfficientNet(width_coefficient, depth_coefficient, dropout_rate):
    inputs = tf.keras.Input(shape=(224, 224, 3))
    x = Conv2D(int(32*width_coefficient), 3, strides=(2, 2), padding='same')(inputs)
    x = BatchNormalization()(x)
    x = Activation('swish')(x)

    # stage 1
    x = MBConvBlock(x, 1, 16, 1, width_coefficient, depth_coefficient, dropout_rate, include_top=False)

    # stage 2
    x = MBConvBlock(x, 6, 24, 2, width_coefficient, depth_coefficient, dropout_rate, include_top=False)

    # stage 3
    x = MBConvBlock(x, 6, 40, 2, width_coefficient, depth_coefficient, dropout_rate, include_top=False)

    # stage 4
    x = MBConvBlock(x, 6, 80, 3, width_coefficient, depth_coefficient, dropout_rate, include_top=False)

    # stage 5
    x = MBConvBlock(x, 6, 112, 3, width_coefficient, depth_coefficient, dropout_rate, include_top=False)

    # stage 6
    x = MBConvBlock(x, 6, 192, 4, width_coefficient, depth_coefficient, dropout_rate, include_top=False)

    # stage 7
    x = MBConvBlock(x, 6, 320, 1, width_coefficient, depth_coefficient, dropout_rate, include_top=False)

    # 最后一层卷积
    x = Conv2D(int(1280*width_coefficient), 1, padding='same')(x)
    x = BatchNormalization()(x)
    x = Activation('swish')(x)

    # 全局平均池化层
    x = GlobalAveragePooling2D()(x)

    # 全连接层
    x = Dropout(dropout_rate)(x)
    outputs = Dense(5, activation='softmax')(x)

    # 定义模型
    model = Model(inputs, outputs)

    return model

# 定义MBConvBlock
def MBConvBlock(inputs, expand_ratio, out_channels, strides, width_coefficient, depth_coefficient, dropout_rate, include_top=True):
    # 获取输入通道数
    input_channels = inputs.shape[-1]

    # 定义扩张通道数
    expand_channels = int(input_channels * expand_ratio)

    # 定义输出通道数
    out_channels = int(out_channels * width_coefficient)

    # 定义MBConvBlock的输出
    x = inputs

    # 判断是否需要对输入进行扩张
    if expand_ratio != 1:
        x = Conv2D(expand_channels, 1, padding='same')(x)
        x = BatchNormalization()(x)
        x = Activation('swish')(x)

    # 深度卷积
    x = DepthwiseConv2D(3, strides=strides, padding='same')(x)
    x = BatchNormalization()(x)
    x = Activation('swish')(x)

    # 判断是否需要降采样
    if strides == 2:
        if include_top:
            drop_rate = dropout_rate * 0.5
        else:
            drop_rate = dropout_rate
        x = Dropout(drop_rate)(x)

    # 投影卷积
    x = Conv2D(out_channels, 1, padding='same')(x)
    x = BatchNormalization()(x)

    # 判断是否需要使用残差连接
    if input_channels == out_channels and strides == 1:
        x = tf.keras.layers.add([inputs, x])

    return x

# 定义超参数
width_coefficient = 1.0
depth_coefficient = 1.0
dropout_rate = 0.2

# 构建模型
model = EfficientNet(width_coefficient, depth_coefficient, dropout_rate)

# 编译模型
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# 模型可视化
tf.keras.utils.plot_model(model, show_shapes=True)

# 训练模型
history = model.fit(train_ds, epochs=10, validation_data=val_ds)

# 可视化训练过程中的准确率和损失率
import matplotlib.pyplot as plt

acc = history.history['accuracy']
val_acc = history.history['val_accuracy']

loss = history.history['loss']
val_loss = history.history['val_loss']

epochs_range = range(10)

plt.figure(figsize=(15, 15))
plt.subplot(2, 2, 1)
plt.plot(epochs_range, acc, label='Training Accuracy')
plt.plot(epochs_range, val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')

plt.subplot(2, 2, 2)
plt.plot(epochs_range, loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()

解释：

1. 导入所需的库
2. 定义EfficientNet网络结构，包括输入层、各个阶段的MBConvBlock、最后的全连接层等
3. 定义MBConvBlock，包括输入、输出通道数，是否需要扩张，是否需要降采样等
4. 定义超参数：width_coefficient、depth_coefficient、dropout_rate
5. 构建模型，即用EfficientNet构建分类模型
6. 编译模型，包括优化器、损失函数和评估指标
7. 可视化模型结构
8. 训练模型，包括训练数据集和验证数据集，训练轮数为10
9. 可视化训练过程中的准确率和损失率，即训练集、验证集的准确率和损失率随训练轮数的变化趋势