網路模型的交叉驗證

資料集的內容一般分為：trainset、valset和testset，將獲取的mnist資料集的(x,y)部分拆分為[x_train, y_train]（訓練資料）和[x_val, y_val]（評測資料）

#把[x, y]拆分成[x_train, y_train]和[x_val, y_val]
idx = tf.range(60000)
idx = tf.random.shuffle(idx)
x_train, y_train = tf.gather(x, idx[:50000]), tf.gather(y, idx[:50000])  #[0, 50000]
x_val, y_val = tf.gather(x, idx[-10000:]) , tf.gather(y, idx[-10000:])   #[50000,60000]
print(x_train.shape, y_train.shape, x_val.shape, y_val.shape)

用這兩部分資料集進行邊訓練，邊測評，能夠檢視當前訓練的指標效果。可以檢視每次訓練後評價指標，找出最好的引數。

#一共training6次，每2次評測一次
# db_train：training資料集，  epochs：資料集的training的次數，  validation_data：用於評測的資料集，  validation_freq：每n個epoch做一次評測
network.fit(db_train, epochs=6, validation_data=db_val, validation_freq=2)

用(x_test, y_test)資料集對模型進行進行測試。

network.evaluate(db_test)

完整程式碼：

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())


#把[x, y]拆分成[x_train, y_train]和[x_val, y_val]
idx = tf.range(60000)
idx = tf.random.shuffle(idx)
x_train, y_train = tf.gather(x, idx[:50000]), tf.gather(y, idx[:50000])  #[0, 50000]
x_val, y_val = tf.gather(x, idx[-10000:]) , tf.gather(y, idx[-10000:])   #[50000,60000]
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']
	)

#一共training6次，每2次評測一次
# db_train：training資料集，  epochs：資料集的training的次數，  validation_data：用於評測的資料集，  validation_freq：每n個epoch做一次評測
network.fit(db_train, epochs=6, validation_data=db_val, validation_freq=2)

#測試的資料集
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)