基于Keras的人工神经网络（ANN）实现Fashion-MNIST分类模型

最新推荐文章于 2022-03-10 13:34:42 发布

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最新推荐文章于 2022-03-10 13:34:42 发布

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分类专栏： DL/RL自学笔记文章标签： ANN

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仿照TensorFlow官方教程中的Basic classification: Classify images of clothing
，自己写一遍最朴素的人工神经网络来实现Fashion_MNIST分类模型。

Coursera上学了快两个月理论终于有了个稍微像样的实操......

import tensorflow as tf
from tensorflow import keras

import numpy as np
import matplotlib.pyplot as plt

fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

train_images = train_images / 255.0
test_images = test_images / 255.0

model = keras.Sequential([
  keras.layers.Flatten(input_shape=(28,28)),
  keras.layers.Dense(128, activation='relu'),
  keras.layers.Dropout(0.2),
  keras.layers.Dense(10)                            
])
print(model.summary())

model.compile(optimizer='Adam',
       loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
       metrics=['accuracy'])

model.fit(train_images, train_labels, epochs=20)

test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)

probability_model = tf.keras.Sequential([
  model,
  tf.keras.layers.Softmax()
])

predictions = probability_model.predict(test_images)

训练准确率为90.97%，测试准确率为89.06%