Tensorflow---使用Tensorflow实现Fashion_MNIST数据集多分类模型

一、代码中的数据集可以通过以下代码继续加载

import tensorflow as tf
(train_image, train_label), (test_image, test_label) = tf.keras.datasets.fashion_mnist.load_data()

二、代码运行环境

Tensorflow-gpu==2.4.0

Python==3.7

三、训练代码如下：

import tensorflow as tf
import os
import numpy as np
import matplotlib.pyplot as plt

# 环境变量配置
os.environ['TF_XLA_FLAGS'] = '--tf_xla_enable_xla_devices'
os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'

# 数据的加载
(train_image, train_label), (test_image, test_label) = tf.keras.datasets.fashion_mnist.load_data()
train_image = train_image / 255
test_image = test_image / 255

# 模型的建立
model = tf.keras.Sequential()
model.add(tf.keras.layers.Flatten(input_shape=(28, 28)))
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dense(10, activation='softmax'))

# 模型的编译
model.compile(
    optimizer='adam',
    loss='sparse_categorical_crossentropy',
    metrics=['acc']
)

# 模型的训练
model.fit(train_image, train_label, epochs=50)

# 模型的保存
model.save(r'model_data/model.h5')

四、验证代码如下：

import tensorflow as tf
import os
import numpy as np
import matplotlib.pyplot as plt

# 环境变量配置
os.environ['TF_XLA_FLAGS'] = '--tf_xla_enable_xla_devices'
os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'

# 数据的加载
(train_image, train_label), (test_image, test_label) = tf.keras.datasets.fashion_mnist.load_data()
train_image = train_image / 255
test_image = test_image / 255

# 模型的加载
model = tf.keras.models.load_model(r'model_data/model.h5')

# 模型的验证
model.evaluate(test_image, test_label)