tensorflow(三十一)：数据分割与K折交叉验证

一、数据集分割

1、训练集、测试集

 2、训练集、验证集、测试集

步骤：

（1）把训练集60K分成两部分，一部分50K，另一部分10K。

（2）组合成dataset，并打乱。

二、训练过程评估

1、训练的过程评估

其中，第二行是训练，总轮数是5，每两轮做一次评估，达到的效果好的话提前停止。

 2、在测试集上再次评估

三、K折交叉验证

（1）第一种方式：手动

解释：每一轮训练，一共有6万数据集，首先产生1到6万的随机数，然后对随机数打散，然后前五万做训练集，后一万做测试。

 （2）第二种方式：调用keras的方法。

 四、实战：数据集分割

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


def preprocess(x, y):
    """
    x is a simple image, not a batch
    """
    x = tf.cast(x, dtype=tf.float32) / 255.
    x = tf.reshape(x, [28*28])
    y = tf.cast(y, dtype=tf.int32)
    y = tf.one_hot(y, depth=10)
    return x,y


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)
ds_val = tf.data.Dataset.from_tensor_slices((x_val, y_val))
ds_val = ds_val.map(preprocess).batch(batchsz) 

sample = next(iter(db))
print(sample[0].shape, sample[1].shape)


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()




network.compile(optimizer=optimizers.Adam(lr=0.01),
        loss=tf.losses.CategoricalCrossentropy(from_logits=True),
        metrics=['accuracy']
    )

network.fit(db, epochs=5, validation_data=ds_val,
              validation_steps=2)
 
network.evaluate(ds_val)

sample = next(iter(ds_val))
x = sample[0]
y = sample[1] # one-hot
pred = network.predict(x) # [b, 10]
# convert back to number 
y = tf.argmax(y, axis=1)
pred = tf.argmax(pred, axis=1)

print(pred)
print(y)

 五、实战：交叉验证

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


def preprocess(x, y):
    """
    x is a simple image, not a batch
    """
    x = tf.cast(x, dtype=tf.float32) / 255.
    x = tf.reshape(x, [28*28])
    y = tf.cast(y, dtype=tf.int32)
    y = tf.one_hot(y, depth=10)
    return x,y


batchsz = 128
(x, y), (x_test, y_test) = datasets.mnist.load_data()
print('datasets:', x.shape, y.shape, x.min(), x.max())



idx = tf.range(60000)
idx = tf.random.shuffle(idx)
x_train, y_train = tf.gather(x, idx[:50000]), tf.gather(y, idx[:50000])
x_val, y_val = tf.gather(x, idx[-10000:]) , tf.gather(y, idx[-10000:])
print(x_train.shape, y_train.shape, x_val.shape, y_val.shape)
db_train = tf.data.Dataset.from_tensor_slices((x_train,y_train))
db_train = db_train.map(preprocess).shuffle(50000).batch(batchsz)

db_val = tf.data.Dataset.from_tensor_slices((x_val,y_val))
db_val = db_val.map(preprocess).shuffle(10000).batch(batchsz)



db_test = tf.data.Dataset.from_tensor_slices((x_test, y_test))
db_test = db_test.map(preprocess).batch(batchsz) 

sample = next(iter(db_train))
print(sample[0].shape, sample[1].shape)


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()




network.compile(optimizer=optimizers.Adam(lr=0.01),
        loss=tf.losses.CategoricalCrossentropy(from_logits=True),
        metrics=['accuracy']
    )

network.fit(db_train, epochs=6, validation_data=db_val, validation_freq=2)

print('Test performance:') 
network.evaluate(db_test)
 

sample = next(iter(db_test))
x = sample[0]
y = sample[1] # one-hot
pred = network.predict(x) # [b, 10]
# convert back to number 
y = tf.argmax(y, axis=1)
pred = tf.argmax(pred, axis=1)

print(pred)
print(y)