【tensorflow2.0】21.Dataset实战之猫狗大战

    下边是关于猫狗大战代码的解读。

#os模块提供了很多操作系统的功能接口函数
import tensorflow as tf
import os
print(tf.__version__)

#训练集和测试集的位置，本地运行的时候记得修改路径
data_dir = './datasets'
train_cats_dir = data_dir + '/train/cats/'
train_dogs_dir = data_dir + '/train/dogs/'
test_cats_dir = data_dir + '/valid/cats/'
test_dogs_dir = data_dir + '/valid/dogs/'

# 构建训练数据集：建立训练集猫狗图片名字集合的张量
train_cat_filenames = tf.constant([train_cats_dir + filename for filename in os.listdir(train_cats_dir)])
train_dog_filenames = tf.constant([train_dogs_dir + filename for filename in os.listdir(train_dogs_dir)])
#将猫狗的训练集进行拼接
train_filenames = tf.concat([train_cat_filenames, train_dog_filenames], axis=-1)

# cat 0  dog :1 然后进行拼接
train_labels = tf.concat([
    tf.zeros(train_cat_filenames.shape, dtype=tf.int32), 
    tf.ones(train_dog_filenames.shape, dtype=tf.int32)], 
    axis=-1)

#定义解码函数
def _decode_and_resize(filename, label):
    image_string = tf.io.read_file(filename)            # 读取原始文件
    image_decoded = tf.image.decode_jpeg(image_string)  # 解码JPEG图片
    image_resized = tf.image.resize(image_decoded, [256, 256]) / 255.0 #进行归一化
    return image_resized, label

batch_size = 32
train_dataset = tf.data.Dataset.from_tensor_slices((train_filenames, train_labels))
#使用map来优化数据集传入性能，num_parallel_calls实现并行
train_dataset = train_dataset.map(
    map_func=_decode_and_resize, 
    num_parallel_calls=tf.data.experimental.AUTOTUNE)


# 取出前buffer_size个数据放入buffer，并从其中随机采样，采样后的数据用后续数据替换
train_dataset = train_dataset.shuffle(buffer_size=23000)    

train_dataset = train_dataset.repeat(count=3)

train_dataset = train_dataset.batch(batch_size)

train_dataset = train_dataset.prefetch(tf.data.experimental.AUTOTUNE)

# 构建测试数据集，步骤和上边构建训练集相同
test_cat_filenames = tf.constant([test_cats_dir + filename for filename in os.listdir(test_cats_dir)])
test_dog_filenames = tf.constant([test_dogs_dir + filename for filename in os.listdir(test_dogs_dir)])
test_filenames = tf.concat([test_cat_filenames, test_dog_filenames], axis=-1)
test_labels = tf.concat([
    tf.zeros(test_cat_filenames.shape, dtype=tf.int32), 
    tf.ones(test_dog_filenames.shape, dtype=tf.int32)], 
    axis=-1)

test_dataset = tf.data.Dataset.from_tensor_slices((test_filenames, test_labels))
test_dataset = test_dataset.map(_decode_and_resize)
test_dataset = test_dataset.batch(batch_size)

#这里我们用子类模型构建神经网络，通过结果我们可以发现测试集的效果并不理想，最主要的原因就是这里的网络结构，
#但是因为我无法使用gpu，所以没法给出其他网络的效果，大家可以自己试试别的经典分类网络如VGG16，Resnet等
class CNNModel(tf.keras.models.Model):
    def __init__(self):
        super(CNNModel, self).__init__()
        self.conv1 = tf.keras.layers.Conv2D(32, 3, activation='relu')
        self.maxpool1 = tf.keras.layers.MaxPooling2D()
        self.conv2 = tf.keras.layers.Conv2D(32, 5, activation='relu')
        self.maxpool2 = tf.keras.layers.MaxPooling2D()
        self.flatten = tf.keras.layers.Flatten()
        self.d1 = tf.keras.layers.Dense(64, activation='relu')
        self.d2 = tf.keras.layers.Dense(2, activation='softmax') #sigmoid 和softmax

    def call(self, x):
        x = self.conv1(x)
        x = self.maxpool1(x)
        x = self.conv2(x)
        x = self.maxpool2(x)       
        x = self.flatten(x)
        x = self.d1(x)
        x = self.d2(x)
        return x

learning_rate = 0.001
model = CNNModel()
#损失函数
loss_object = tf.keras.losses.SparseCategoricalCrossentropy()
#优化器
optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)
#训练集的评估函数
train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')
#测试集
test_loss = tf.keras.metrics.Mean(name='test_loss')
test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')


@tf.function
def train_step(images, labels):
    with tf.GradientTape() as tape:
        predictions = model(images)
        loss = loss_object(labels, predictions)
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))

    train_loss(loss)
    train_accuracy(labels, predictions)

def test_step(images, labels):
    predictions = model(images)
    t_loss = loss_object(labels, predictions)

    test_loss(t_loss)
    test_accuracy(labels, predictions)

EPOCHS=10
for epoch in range(EPOCHS):
    # 在下一个epoch开始时，重置评估指标
    train_loss.reset_states()
    train_accuracy.reset_states()
    test_loss.reset_states()
    test_accuracy.reset_states()

    for images, labels in train_dataset:
        train_step(images, labels)

    for test_images, test_labels in test_dataset:
        test_step(test_images, test_labels)

    template = 'Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, Test Accuracy: {}'
    print(template.format(epoch + 1,
                          train_loss.result(),
                          train_accuracy.result() * 100,
                          test_loss.result(),
                          test_accuracy.result() * 100
                         ))