我要完成船舶图像的二分类任务。我现在只有图片信息，那你可以帮我给出基于tensorflow2下vgg13,每个文件所需要的代码吗。里面还要有test文件，以及draw文件。draw文件用来画出损失函数随epoch的变化曲线。并教我怎么把本地的船舶图片加载到代码中

好的，我可以帮你提供一些基于TensorFlow 2.x下的VGG13实现的代码，以及相关的test和draw文件。同时，我也会给你一些加载本地船舶图片的代码示例。请注意，这份代码仅供参考，你需要根据自己的实际情况进行修改和调整。

首先，我们需要安装TensorFlow 2.x和相关的依赖库，可以通过以下命令进行安装：

pip install tensorflow
pip install matplotlib
pip install numpy

然后，我们来看一下基于VGG13的图片分类模型的实现代码。代码如下：

import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense, Dropout
from tensorflow.keras.models import Model

def VGG13(input_shape=(224, 224, 3), num_classes=2):
    # 定义输入层
    input_layer = Input(shape=input_shape)

    # 第一段卷积层
    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(input_layer)
    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)

    # 第二段卷积层
    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)

    # 第三段卷积层
    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)
    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)

    # 第四段卷积层
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)

    # 第五段卷积层
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)

    # 全连接层
    x = Flatten()(x)
    x = Dense(4096, activation='relu', name='fc1')(x)
    x = Dropout(0.5)(x)
    x = Dense(4096, activation='relu', name='fc2')(x)
    x = Dropout(0.5)(x)
    output_layer = Dense(num_classes, activation='softmax', name='output')(x)

    # 定义模型
    model = Model(inputs=input_layer, outputs=output_layer, name='vgg13')

    # 打印模型结构
    model.summary()

    return model

接下来，我们来看一下测试代码。测试代码需要读入测试数据集，然后对测试集进行预测并计算准确率等评价指标。代码如下：

import os
import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from sklearn.metrics import classification_report

# 加载测试数据集
test_dir = './test_data'
test_datagen = ImageDataGenerator(rescale=1./255)
test_generator = test_datagen.flow_from_directory(
        test_dir,
        target_size=(224, 224),
        batch_size=32,
        class_mode='binary')

# 加载模型
model = tf.keras.models.load_model('./vgg13.h5')

# 对测试集进行预测
y_pred = model.predict(test_generator)
y_pred = np.argmax(y_pred, axis=1)

# 计算评价指标
target_names = ['class 0', 'class 1']
print(classification_report(test_generator.classes, y_pred, target_names=target_names))

最后，我们来看一下绘制损失函数随epoch的变化曲线的代码。代码如下：

import matplotlib.pyplot as plt
import numpy as np

# 加载训练日志文件
log_file = './training.log'
with open(log_file, 'r') as f:
    lines = f.readlines()

# 解析训练日志文件
epochs = []
losses = []
val_losses = []
for line in lines:
    if line.startswith('Epoch'):
        epoch = int(line.split()[1])
        epochs.append(epoch)
    elif line.startswith('loss:'):
        loss = float(line.split()[1])
        val_loss = float(line.split()[3])
        losses.append(loss)
        val_losses.append(val_loss)

# 绘制损失函数随epoch的变化曲线
plt.plot(epochs, losses, 'r', label='Training Loss')
plt.plot(epochs, val_losses, 'b', label='Validation Loss')
plt.title('Training and Validation Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.show()

至于如何将本地的船舶图片加载到代码中，你可以使用TensorFlow提供的ImageDataGenerator类来读取本地图片，具体代码如下：

from tensorflow.keras.preprocessing.image import ImageDataGenerator

# 加载本地船舶图片
data_dir = './ship_data'
batch_size = 32

train_datagen = ImageDataGenerator(
    rescale=1./255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)
train_generator = train_datagen.flow_from_directory(
    data_dir+'/train',
    target_size=(224, 224),
    batch_size=batch_size,
    class_mode='binary')

val_datagen = ImageDataGenerator(rescale=1./255)
val_generator = val_datagen.flow_from_directory(
    data_dir+'/val',
    target_size=(224, 224),
    batch_size=batch_size,
    class_mode='binary')

希望这份代码能够帮助到你，祝你在船舶图像二分类任务中取得好成绩！