tensorflow.keras metrics应用

冷不防 2022-09-08 00:09 111阅读 0赞

# tensorflow.keras metrics应用 #

> 不使用metrics实现的博客参考：

![image-20210804195738825][]

import  tensorflow as tf
    from    tensorflow.keras import datasets, layers, optimizers, Sequential, metrics

更改数据类型并归一化

def preprocess(x, y):
    
        x = tf.cast(x, dtype=tf.float32) / 255.
        y = tf.cast(y, dtype=tf.int32)
    
        return x,y

加载数据集并进行预处理

batchsz = 128
    (x, y), (x_val, y_val) = datasets.mnist.load_data()
    print('datasets:', x.shape, y.shape, x.min(), x.max())
    
    db = tf.data.Dataset.from_tensor_slices((x,y))
    db = db.map(preprocess).shuffle(60000).batch(batchsz).repeat(10)
    
    ds_val = tf.data.Dataset.from_tensor_slices((x_val, y_val))
    ds_val = ds_val.map(preprocess).batch(batchsz)

datasets: (60000, 28, 28) (60000,) 0 255

构建模型

network = Sequential([layers.Dense(256, activation='relu'),
                         layers.Dense(128, activation='relu'),
                         layers.Dense(64, activation='relu'),
                         layers.Dense(32, activation='relu'),
                         layers.Dense(10)])
    network.build(input_shape=(None, 28*28))
    network.summary() # 打印模型详细信息
    
    # 优化器
    optimizer = optimizers.Adam(lr=0.01)

Model: "sequential"
    _________________________________________________________________
    Layer (type)                 Output Shape              Param #   
    =================================================================
    dense (Dense)                multiple                  200960    
    _________________________________________________________________
    dense_1 (Dense)              multiple                  32896     
    _________________________________________________________________
    dense_2 (Dense)              multiple                  8256      
    _________________________________________________________________
    dense_3 (Dense)              multiple                  2080      
    _________________________________________________________________
    dense_4 (Dense)              multiple                  330       
    =================================================================
    Total params: 244,522
    Trainable params: 244,522
    Non-trainable params: 0
    _________________________________________________________________

模型训练并应用metrics

# 使用metrics计算准确率和loss的均值
    acc_meter = metrics.Accuracy()
    loss_meter = metrics.Mean()
    
    
    for step, (x,y) in enumerate(db):
    
        with tf.GradientTape() as tape:
            # [b, 28, 28] => [b, 784]
            x = tf.reshape(x, (-1, 28*28))
            # [b, 784] => [b, 10]
            out = network(x)
            # [b] => [b, 10]
            y_onehot = tf.one_hot(y, depth=10) 
            # [b]
            # 使用交叉熵损失
            # from_logits=True确保数据稳定性 out必须是网络直接输出的结果，不经过激活函数
            loss = tf.reduce_mean(tf.losses.categorical_crossentropy(y_onehot, out, from_logits=True))
            
            # 添加loss
            loss_meter.update_state(loss)
    
     
        # 梯度更新 反向传播
        grads = tape.gradient(loss, network.trainable_variables)
        optimizer.apply_gradients(zip(grads, network.trainable_variables))
    
    
        if step % 100 == 0:
    
            print(step, 'loss_meter:',loss_meter.result().numpy(),' loss:', loss,) 
            # 下一轮计算时应清零
            loss_meter.reset_states()
    
    
        # evaluate
        if step % 500 == 0:
            total, total_correct = 0., 0
            # 使用前清空历史状态
            acc_meter.reset_states()
    
            for step1, (x, y) in enumerate(ds_val): 
                # [b, 28, 28] => [b, 784]
                x = tf.reshape(x, (-1, 28*28))
                # [b, 784] => [b, 10]
                out = network(x) 
    
    
                # [b, 10] => [b] 
                pred = tf.argmax(out, axis=1) 
                pred = tf.cast(pred, dtype=tf.int32)
                
                
                # 方法一
                # bool type 
                correct = tf.equal(pred, y)
                # bool tensor => int tensor => numpy
                total_correct += tf.reduce_sum(tf.cast(correct, dtype=tf.int32)).numpy()
                total += x.shape[0]
                
                
                # 方法二
                acc_meter.update_state(y, pred)
    
    
            print(step, 'Evaluate Acc:', total_correct/total, acc_meter.result().numpy())

0 loss_meter: 0.0147538865   loss: tf.Tensor(0.0147538865, shape=(), dtype=float32)
    78 Evaluate Acc: 0.9743 0.9743
    100 loss_meter: 0.04805827   loss: tf.Tensor(0.041749448, shape=(), dtype=float32)
    200 loss_meter: 0.053051163   loss: tf.Tensor(0.0131300185, shape=(), dtype=float32)
    300 loss_meter: 0.07365899   loss: tf.Tensor(0.0278976, shape=(), dtype=float32)
    400 loss_meter: 0.07007911   loss: tf.Tensor(0.05961611, shape=(), dtype=float32)
    500 loss_meter: 0.059413455   loss: tf.Tensor(0.04578472, shape=(), dtype=float32)
    78 Evaluate Acc: 0.976 0.976
    600 loss_meter: 0.045514174   loss: tf.Tensor(0.1006662, shape=(), dtype=float32)
    700 loss_meter: 0.05224053   loss: tf.Tensor(0.061094068, shape=(), dtype=float32)
    800 loss_meter: 0.06696898   loss: tf.Tensor(0.08473669, shape=(), dtype=float32)
    900 loss_meter: 0.06490257   loss: tf.Tensor(0.05812662, shape=(), dtype=float32)
    1000 loss_meter: 0.056545332   loss: tf.Tensor(0.10347018, shape=(), dtype=float32)
    78 Evaluate Acc: 0.9745 0.9745
    1100 loss_meter: 0.0515905   loss: tf.Tensor(0.045466803, shape=(), dtype=float32)
    1200 loss_meter: 0.06225908   loss: tf.Tensor(0.046285823, shape=(), dtype=float32)
    1300 loss_meter: 0.057779107   loss: tf.Tensor(0.05366286, shape=(), dtype=float32)
    1400 loss_meter: 0.06661249   loss: tf.Tensor(0.10940219, shape=(), dtype=float32)
    1500 loss_meter: 0.059498344   loss: tf.Tensor(0.05784896, shape=(), dtype=float32)
    78 Evaluate Acc: 0.974 0.974
    1600 loss_meter: 0.06252271   loss: tf.Tensor(0.0287047, shape=(), dtype=float32)
    1700 loss_meter: 0.060016934   loss: tf.Tensor(0.006867729, shape=(), dtype=float32)
    1800 loss_meter: 0.05751593   loss: tf.Tensor(0.13693535, shape=(), dtype=float32)

[image-20210804195738825]: /images/20220829/798f485f429e4f8498026458cfad010a.png