基于tensorflow中的keras编写代码，两个文件夹中的所有图片数据1. 编写数据加载函数； 2. 根据任务要求对数据集进行划分； 3. 数据增强； 4. 构建深度学习模型和损失函数； 5. 编写模型训练相关代码，完成模型训练 6. 使用可视化库 Matplotlib 对训练过程进行可视化展示，如损失值变化、准确率变化、召回率、ROC曲线等。 7. 将训练好的模型保存

好的，下面给您提供基于tensorflow中的keras编写代码的具体实现：

1. 编写数据加载函数

我们可以使用keras中的ImageDataGenerator来读取数据集，代码如下：

from tensorflow.keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(
    rescale=1./255, # 像素值缩放到 [0, 1] 区间
    shear_range=0.2, # 随机错切变换
    zoom_range=0.2, # 随机缩放
    horizontal_flip=True) # 随机水平翻转

test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
    'train/',
    target_size=(150, 150),
    batch_size=32,
    class_mode='binary')

validation_generator = test_datagen.flow_from_directory(
    'validation/',
    target_size=(150, 150),
    batch_size=32,
    class_mode='binary')

2. 根据任务要求对数据集进行划分

我们可以将数据集划分为训练集、验证集和测试集，代码如下：

import os, shutil

original_dataset_dir = 'original/'

base_dir = 'data/'
os.mkdir(base_dir)

train_dir = os.path.join(base_dir, 'train/')
os.mkdir(train_dir)

validation_dir = os.path.join(base_dir, 'validation/')
os.mkdir(validation_dir)

test_dir = os.path.join(base_dir, 'test/')
os.mkdir(test_dir)

train_cats_dir = os.path.join(train_dir, 'cats/')
os.mkdir(train_cats_dir)

train_dogs_dir = os.path.join(train_dir, 'dogs/')
os.mkdir(train_dogs_dir)

validation_cats_dir = os.path.join(validation_dir, 'cats/')
os.mkdir(validation_cats_dir)

validation_dogs_dir = os.path.join(validation_dir, 'dogs/')
os.mkdir(validation_dogs_dir)

test_cats_dir = os.path.join(test_dir, 'cats/')
os.mkdir(test_cats_dir)

test_dogs_dir = os.path.join(test_dir, 'dogs/')
os.mkdir(test_dogs_dir)

fnames = ['cat.{}.jpg'.format(i) for i in range(1000)]
for fname in fnames:
    src = os.path.join(original_dataset_dir, fname)
    dst = os.path.join(train_cats_dir, fname)
    shutil.copyfile(src, dst)

fnames = ['cat.{}.jpg'.format(i) for i in range(1000, 1500)]
for fname in fnames:
    src = os.path.join(original_dataset_dir, fname)
    dst = os.path.join(validation_cats_dir, fname)
    shutil.copyfile(src, dst)

fnames = ['cat.{}.jpg'.format(i) for i in range(1500, 2000)]
for fname in fnames:
    src = os.path.join(original_dataset_dir, fname)
    dst = os.path.join(test_cats_dir, fname)
    shutil.copyfile(src, dst)

fnames = ['dog.{}.jpg'.format(i) for i in range(1000)]
for fname in fnames:
    src = os.path.join(original_dataset_dir, fname)
    dst = os.path.join(train_dogs_dir, fname)
    shutil.copyfile(src, dst)

fnames = ['dog.{}.jpg'.format(i) for i in range(1000, 1500)]
for fname in fnames:
    src = os.path.join(original_dataset_dir, fname)
    dst = os.path.join(validation_dogs_dir, fname)
    shutil.copyfile(src, dst)

fnames = ['dog.{}.jpg'.format(i) for i in range(1500, 2000)]
for fname in fnames:
    src = os.path.join(original_dataset_dir, fname)
    dst = os.path.join(test_dogs_dir, fname)
    shutil.copyfile(src, dst)

3. 数据增强

我们可以使用ImageDataGenerator来进行数据增强，代码如下：

datagen = ImageDataGenerator(
    rotation_range=40,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    fill_mode='nearest')

from tensorflow.keras.preprocessing import image

fnames = [os.path.join(train_cats_dir, fname) for fname in os.listdir(train_cats_dir)]

img_path = fnames[3]

img = image.load_img(img_path, target_size=(150, 150))

x = image.img_to_array(img)

x = x.reshape((1,) + x.shape)

i = 0
for batch in datagen.flow(x, batch_size=1):
    plt.figure(i)
    imgplot = plt.imshow(image.array_to_img(batch[0]))
    i += 1
    if i % 4 == 0:
        break

plt.show()

4. 构建深度学习模型和损失函数

我们可以使用keras中的Sequential模型来构建深度学习模型，代码如下：

from tensorflow.keras import layers
from tensorflow.keras import models

model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu',
                        input_shape=(150, 150, 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(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dropout(0.5))
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))

model.summary()

model.compile(loss='binary_crossentropy',
              optimizer=optimizers.RMSprop(lr=1e-4),
              metrics=['acc'])

5. 编写模型训练相关代码，完成模型训练

我们可以使用fit_generator来进行模型训练，代码如下：

history = model.fit_generator(
    train_generator,
    steps_per_epoch=100,
    epochs=100,
    validation_data=validation_generator,
    validation_steps=50)

model.save('cats_and_dogs_small_1.h5')

6. 使用可视化库 Matplotlib 对训练过程进行可视化展示

我们可以使用Matplotlib对训练过程进行可视化展示，代码如下：

import matplotlib.pyplot as plt

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

epochs = range(len(acc))

plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()

plt.figure()

plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()

plt.show()

7. 将训练好的模型保存

我们可以使用save来将训练好的模型保存到本地，代码如下：

model.save('cats_and_dogs_small_1.h5')