项目要点
- Fashion-MNIST总共有十个类别的图像。
- 代码运行位置 CPU: cpu=tf.config.set_visible_devices(tf.config.list_physical_devices("CPU"))
- fashion_mnist = keras.datasets.fashion_mnist # fashion_mnist 数据导入
- 训练数据和测试数据拆分: x_valid, x_train = x_train_all[:5000], x_train_all[5000:]
- x_train_scaled = scaler.fit_transform(x_train.astype(np.float32).reshape(55000, -1)).reshape( -1, 28, 28, 1) 标准化处理数据 # scaler = StandardScaler() 标准化处理只能处理一维数据
- 创建模型: model = keras.models.Sequential()
- model.add(keras.layers.Conv2D(filters = 64, kernel_size = 3, padding = 'same', activation = 'relu', input_shape = (28, 28, 1))) 添加输入层
- 池化, 常用最大值池化: model.add(keras.layers.MaxPool2D())
- model.add(keras.layers.Conv2D(filters = 32,kernel_size = 3, padding = 'same',activation = 'relu')) # 添加卷积层
- 维度变化, 卷积完后为四维, 自动变二维: model.add(keras.layers.Flatten())
- model.add(keras.layers.Dense(512, activation = 'relu', input_shape = (784))) # 重新调整形状
- 添加卷积层: model.add(keras.layers.Dense(256, activation = 'relu'))
- 添加输出层: model.add(keras.layers.Dense(10, activation = 'softmax'))
- 查看模型: model.summary()
- 模型配置:
model.compile(loss = 'sparse_categorical_crossentropy',
optimizer = 'adam',
metrics = ['accuracy'])- histroy = model.fit(x_train_scaled, y_train, epochs = 10, validation_data= (x_valid_scaled, y_valid)) 模型训练
- 模型评估: model.evaluate(x_test_scaled, y_test)
- 画图大小设置: pd.DateFrame(history.history).plot(figsize = (8, 5))
- 网格线显示: plt.grid(True)
- y轴设置: plt.gca().set_ylim(0, 1) # plt.gca() 坐标轴设置
- plt.show() 显示图像
一 Fashion-MNIST分类
Fashion-MNIST总共有十个类别的图像。每一个类别由训练数据集6000张图像和测试数据集1000张图像。所以训练集和测试集分别包含60000张和10000张。测试训练集用于评估模型的性能。
每一个输入图像的高度和宽度均为28像素。数据集由灰度图像组成。Fashion-MNIST,中包含十个类别，分别是t-shirt，trouser,pillover,dress,coat,sandal,shirt,sneaker,bag,ankle boot。
1.1 导包
import numpy as np
from tensorflow import keras
import tensorflow as tf
import pandas as pd
import os
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
cpu=tf.config.list_physical_devices("CPU")
tf.config.set_visible_devices(cpu)
print(tf.config.list_logical_devices())1.2 数据导入
fashion_mnist = keras.datasets.fashion_mnist
(x_train_all, y_train_all), (x_test, y_test) = fashion_mnist.load_data()
x_valid, x_train = x_train_all[:5000], x_train_all[5000:]
y_valid, y_train = y_train_all[:5000], y_train_all[5000:]1.3 标准化
scaler = StandardScaler()
x_train_scaled = scaler.fit_transform(x_train.astype(np.float32).reshape(55000, -1)).reshape(-1, 28, 28, 1)
x_valid_scaled = scaler.transform(x_valid.astype(np.float32).reshape(5000, -1)).reshape(-1, 28, 28, 1)
x_test_scaled = scaler.transform(x_test.astype(np.float32).reshape(10000, -1)).reshape(-1, 28, 28, 1)1.4 创建模型
model = keras.models.Sequential()
# filters 过滤器
# 卷积
model.add(keras.layers.Conv2D(filters = 64,
kernel_size = 3,
padding = 'same',
activation = 'relu',
# batch_size, height, width, channels(通道数)
input_shape = (28, 28, 1))) # (28, 28, 32)
# 池化, 常用最大值池化
model.add(keras.layers.MaxPool2D()) # (14, 14, 32)
# 卷积
model.add(keras.layers.Conv2D(filters = 32,
kernel_size = 3,
padding = 'same',
activation = 'relu')) # (14, 14, 64)
# 池化, 常用最大值池化
model.add(keras.layers.MaxPool2D()) # (7, 7, 64)
# 卷积
model.add(keras.layers.Conv2D(filters = 32,
kernel_size = 3,
padding = 'same',
activation = 'relu')) # (7, 7, 128)
# 池化, 常用最大值池化
model.add(keras.layers.MaxPool2D()) # (4, 4, 128)
# 维度变化, 卷积完后为四维, 自动变二维
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(512, activation = 'relu', input_shape = (784, )))
model.add(keras.layers.Dense(256, activation = 'relu'))
model.add(keras.layers.Dense(10, activation = 'softmax'))
model.compile(loss = 'sparse_categorical_crossentropy',
optimizer = 'adam',
metrics = ['accuracy'])
1.5 训练模型
histroy = model.fit(x_train_scaled, y_train, epochs = 10, validation_data= (x_valid_scaled, y_valid))
1.6 模型评估
model.evaluate(x_test_scaled, y_test) # [0.32453039288520813, 0.906000018119812]二 增加卷积
2.1 创建模型
model = keras.models.Sequential()
# filters 过滤器
# 卷积
model.add(keras.layers.Conv2D(filters = 64,
kernel_size = 3,
padding = 'same',
activation = 'relu',
# batch_size, height, width, channels(通道数)
input_shape = (28, 28, 1))) # (28, 28, 32)
model.add(keras.layers.Conv2D(filters = 32,
kernel_size = 3,
padding = 'same',
activation = 'relu')) # (14, 14, 64)
# 池化, 常用最大值池化
model.add(keras.layers.MaxPool2D()) # (14, 14, 32)
# 卷积
model.add(keras.layers.Conv2D(filters = 64,
kernel_size = 3,
padding = 'same',
activation = 'relu')) # (14, 14, 64)
model.add(keras.layers.Conv2D(filters = 64,
kernel_size = 3,
padding = 'same',
activation = 'relu')) # (14, 14, 64)
# 池化, 常用最大值池化
model.add(keras.layers.MaxPool2D()) # (7, 7, 64)
# 卷积
model.add(keras.layers.Conv2D(filters = 128,
kernel_size = 3,
padding = 'same',
activation = 'relu')) # (7, 7, 128)
model.add(keras.layers.Conv2D(filters = 128,
kernel_size = 3,
padding = 'same',
activation = 'relu')) # (14, 14, 64)
# 池化, 常用最大值池化
model.add(keras.layers.MaxPool2D()) # (4, 4, 128)
# 维度变化, 卷积完后为四维, 自动变二维
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(512, activation = 'relu', input_shape = (784, )))
model.add(keras.layers.Dense(256, activation = 'relu'))
model.add(keras.layers.Dense(10, activation = 'softmax'))
model.compile(loss = 'sparse_categorical_crossentropy',
optimizer = 'adam',
metrics = ['accuracy'])
2.2 训练模型
histroy = model.fit(x_train_scaled, y_train, epochs = 10, validation_data= (x_valid_scaled, y_valid))
2.3 评估模型
model.evaluate(x_test_scaled, y_test) # [0.3228122293949127, 0.9052000045776367]
