更新时间：2020-10-6
# import lib import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers from tensorflow.keras.layers import Conv2D, BatchNormalization, MaxPool2D, Flatten, Dense,Dropout import numpy as np import matplotlib.pyplot as plt print(tf.__version__) # 准备数据 fashion_mnist = keras.datasets.fashion_mnist (train_imgs, train_labels), (test_imgs, test_labels) = fashion_mnist.load_data() print(train_imgs.shape) # 简单归一化 train_imgs, test_imgs = train_imgs / 255.0, test_imgs / 255.0 # 增加一个维度： 通道维度 train_imgs = train_imgs[..., tf.newaxis] test_imgs = test_imgs[..., tf.newaxis] # 构建模型 # 基于keras的序列式模型 model = keras.Sequential([ Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)), BatchNormalization(), Conv2D(64, (3, 3), activation='relu', input_shape=(28, 28, 1)), BatchNormalization(), MaxPool2D((2,2)), Conv2D(128, (3, 3), activation='relu', input_shape=(28, 28, 1)), BatchNormalization(), Flatten(), Dense(1000, activation='relu'), Dropout(0.2), Dense(100, activation='relu'), layers.Dense(10, activation='softmax') ]) # 模型编译 # 优化器选择：adam # loss选择：交叉熵损失 # 验证方式: 精度 model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) # 模型选择 # batch_szie: 32 # epochs: 10 model.fit(train_imgs, train_labels, epochs=1, batch_size=32) # 模型验证 test_loss, test_acc = model.evaluate(test_imgs, test_labels,verbose=0) print(test_acc)该示例是基于keras的序列式模型构建的方式。包含了常用的卷积层，BN层，最大池化，全连接层。
