技術標籤：TensorFlow神經網路和深度學習tensorflow神經網路深度學習

以MNIST資料集為例來訓練模型

# -*- coding: UTF-8 -*-
"""
Author: LGD
FileName: fashion_mnist_tfdataset
DateTime: 2020/11/26 09:04 
SoftWare: PyCharm
"""
import tensorflow as tf

print('Tensorflow version: {}'.format(tf.__version__))

(train_images,
 
 train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()

# 資料歸一化
train_images = train_images / 255
test_images = test_images / 255

# 建立train_images的Dataset
ds_train_img = tf.data.Dataset.from_tensor_slices(train_images)
print(ds_train_img)
ds_train_label = tf.data.Dataset.from_tensor_slices(
 
train_labels)
print(ds_train_label)

# 使用zip將資料合併到一起
ds_train = tf.data.Dataset.zip((ds_train_img, ds_train_label))
print(ds_train)

# 對資料做變換，取出10000組資料亂序，迴圈，分批次，每批次資料量為64
ds_train = ds_train.shuffle(10000).repeat().batch(64)

# 建立模型
model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=(28,
 
 28)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

# 編譯模型
model.compile(
    optimizer='adam',
    loss='sparse_categorical_crossentropy',
    metrics=['accuracy']
)

# # 訓練
# steps_per_epochs = train_images.shape[0] // 64  # 每次迭代64張圖片，每個epoch迭代的步數
# model.fit(
#     ds_train,
#     epochs=5,
#     steps_per_epoch=steps_per_epochs
# )

# 建立test_images的Dataset
ds_test = tf.data.Dataset.from_tensor_slices((test_images, test_labels))
ds_test = ds_test.batch(64)

# 訓練
steps_per_epochs = train_images.shape[0] // 64  # 每次迭代64張圖片，每個epoch迭代的步數
model.fit(
    ds_train,
    epochs=5,
    steps_per_epoch=steps_per_epochs,
    validation_data=ds_test,
    validation_steps=10000//64  # 由於有迴圈，必須要有step它才知道什麼時候列印一下驗證準確率。
)

獲取MNIST資料集，可以直接是在程式碼載入裡下載，也可以關注下列公眾號加讀者微信，分享百度網盤連結。