三、Tensorflow图像处理预算
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前文
Tensorflow图像处理预算
#%%
# 一、线性回归模型的tensorflow实现
# 二、掌握teonsorflow的框架下建立X到Y的线性回归模型
# 掌握模型构建的流程
# 掌握关于数据的处理方法
# 三、实验内容
# P50 3.2.2
导入数据
#%%
import pandas as pd
MasterFile = pd.read_csv("D:FoodScore.csv")
MasterFile[:5]
直方图
MasterFile.hist()
分离因变量
#%%
import numpy as np
fileName = MasterFile["ID"]
#fileName
N = len(fileName)
Y = np.array(MasterFile["score"]).reshape([N, 1])
处理图像数据
#%%
from PIL import Image
Imsize = 128
X = np.zeros([N, Imsize, Imsize, 3])
for i in range(N):
myfile = fileName[i]
Im = Image.open("D:data_foodscore\\"
+ myfile + ".jpg")
Im = Im.resize([Imsize, Imsize])
Im = np.array(Im) / 255
X[i,] = Im
展示图片
#%%
from matplotlib import pyplot as plt
plt.figure()
fig, ax = plt.subplots(2, 5)
fig.set_figheight(7.5)
fig.set_figwidth(15)
ax = ax.flatten()
for i in range(10):
ax[i].imshow(X[i,])
ax[i].set_title(np.round(Y[i], 2))
划分训练集和测试集
#%%
from sklearn.model_selection import train_test_split
# X0, X1, Y0, Y1 = test_split(X, Y, test_size=0.5, random_state=0)
X0, X1, Y0, Y1 = train_test_split(X, Y, test_size=0.5, random_state=0)
构建模型
#%%
from keras.layers import Dense, Flatten, Input
from keras import Model
input_Layer = Input([Imsize, Imsize, 3])
layer2 = Flatten()(input_Layer)
layer3 = Dense(1)(layer2)
output_layer = layer3
model = Model(input_Layer, output_layer)
model.summary()
模型编译
#%%
from keras.optimizers import Adam
model.compile(loss="mse", optimizer=Adam(lr=0.001), metrics=["mse"])
#model.compile(loss="mse",optimizer="adam",metrics=["mse"])
# from keras.optimizers import Adam
# #?Adam
模型拟合
#%%
model.fit(X0, Y0, validation_data=[X1, Y1], batch_size=100, epochs=100)
模型预测
#%%
mypic = Image.open("E:\ChromeDownload\wlop\\532754530.jpg")
mypic = mypic.resize([128, 128])
mypic = np.array(mypic) / 255
mypic = mypic.reshape([1, 128, 128, 3])
y_pre = model.predict(mypic)
y_pre
GitHub下载地址
本文来自博客园,作者:李好秀,转载请注明原文链接:https://www.cnblogs.com/lehoso/p/15550053.html