优惠做网站生产网线需要什么设备
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 StandardScalercpu=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]