plotly分割显示mnist的方法详解

Vanna ·

更新时间:2024-09-20

· 969 次阅读

加载mnist

plotly显示多个mnist样本

plotly显示单个图片

mnist单样本分拆显示

mnist单样本错误的分拆显示

总结

加载mnist


import numpy
def loadMnist() -> (numpy.ndarray,numpy.ndarray,numpy.ndarray,numpy.ndarray):
    """
    :return:  (xTrain,yTrain,xTest,yTest)
    """
    global _TRAIN_SAMPLE_CNT
    global PIC_H
    global PIC_W
    global _TEST_SAMPLE_CNT
    global PIC_HW
    from tensorflow import keras #修改点: tensorflow:2.6.2,keras:2.6.0 此版本下,  import keras 换成 from tensorflow import keras
    import tensorflow
    print(f"keras.__version__:{keras.__version__}")#2.6.0
    print(f"tensorflow.__version__:{tensorflow.__version__}")#2.6.2
    # avatar_img_path = "/kaggle/working/data"
    import os
    import cv2
    xTrain:numpy.ndarray; label_train:numpy.ndarray; xTest:numpy.ndarray; label_test:numpy.ndarray
    yTrain:numpy.ndarray; yTest:numpy.ndarray
    #%userprofile%\.keras\datasets\mnist.npz
    (xTrain, label_train), (xTest, label_test) = keras.datasets.mnist.load_data()
    # x_train.shape,y_train.shape, x_test.shape, label_test.shape
    # (60000, 28, 28), (60000,), (10000, 28, 28), (10000,)
    _TRAIN_SAMPLE_CNT,PIC_H,PIC_W=xTrain.shape
    PIC_HW=PIC_H*PIC_W
    xTrain=xTrain.reshape((-1, PIC_H * PIC_W))
    xTest=xTest.reshape((-1, PIC_H * PIC_W))
    _TEST_SAMPLE_CNT=label_test.shape[0]
    from sklearn import preprocessing
    #pytorch 的 y 不需要 oneHot
    #_label_train是1列多行的样子.  _label_train.shape : (60000, 1)
    yTrain=label_train
    # y_train.shape:(60000) ; y_train.dtype: dtype('int')
    CLASS_CNT=yTrain.shape[0]
    yTest=label_test
    # y_test.shape:(10000) ; y_test.dtype: dtype('int')
    xTrainMinMaxScaler:preprocessing.MinMaxScaler; xTestMinMaxScaler:preprocessing.MinMaxScaler
    xTrainMinMaxScaler=preprocessing.MinMaxScaler()
    xTestMinMaxScaler=preprocessing.MinMaxScaler()
    # x_train.dtype: dtype('uint8') -> dtype('float64')
    xTrain=xTrainMinMaxScaler.fit_transform(xTrain)
    # x_test.dtype: dtype('uint8') -> dtype('float64')
    xTest = xTestMinMaxScaler.fit_transform(xTest)
    return (xTrain,yTrain,xTest,yTest)

xTrain:torch.Tensor;yTrain:torch.Tensor; xTest:torch.Tensor; yTest:torch.Tensor(xTrain,yTrain,xTest,yTest)=loadMnist() plotly 显示多个mnist样本

import plotly.express
import plotly.graph_objects
import plotly.subplots
import numpy
xTrain:numpy.ndarray=numpy.random.random((2,28,28))
#xTrain[0].shape:(28,28)
#fig:plotly.graph_objects.Figure=None
fig=plotly.subplots.make_subplots(rows=1,cols=2,shared_xaxes=True,shared_yaxes=True) #共1行2列
fig.add_trace(trace=plotly.express.imshow(img=xTrain[0]).data[0],row=1,col=1) #第1行第1列
fig.add_trace(trace=plotly.express.imshow(img=xTrain[1]).data[0],row=1,col=2) #第1行第2列
fig.show()
#参数row、col从1开始，  不是从0开始的

plotly 显示单个图片


import numpy
xTrain:numpy.ndarray=numpy.random.random((2,28,28))
#xTrain[0].shape:(28,28)
import plotly.express
import plotly.graph_objects
plotly.express.imshow(img=xTrain[0]).show()
#其中plotly.express.imshow(img=xTrain[0]) 的类型是 plotly.graph_objects.Figure

xTrain[0]显示如下:

mnist单样本分拆显示

#mnist单样本分割 分割成4*4小格子显示出来, 以确认分割的对不对。 以下代码是正确的分割。 主要逻辑是：   (7,4,7,4)   [h, :, w, :] 
fig:plotly.graph_objects.Figure=plotly.subplots.make_subplots(rows=7,cols=7,shared_xaxes=True,shared_yaxes=True,vertical_spacing=0,horizontal_spacing=0)
xTrain0Img:torch.Tensor=xTrain[0].reshape((PIC_H,PIC_W))
plotly.express.imshow(img=xTrain0Img).show()
xTrain0ImgCells:torch.Tensor=xTrain0Img.reshape((7,4,7,4))
for h in range(7):
    for w in range(7):
        print(f"h,w:{h},{w}")
        fig.add_trace(trace=plotly.express.imshow(xTrain0ImgCells[h,:,w,:]).data[0],col=h+1,row=w+1)
fig.show()

mnist单样本分拆显示结果: 由此图可知 (7,4,7,4) [h, :, w, :] 是正常的取相邻的像素点出而形成的4*4的小方格，这正是所需要的

上图显示的横坐标拉伸比例大于纵坐标所以看起来像一个被拉横了的手写数字5 ，如果能让plotly把横纵拉伸比例设为相等上图会更像手写数字5

可以用torch.swapdim进一步改成以下代码

    """
    mnist单样本分割 分割成4*4小格子显示出来, 重点逻辑是： (7, 4, 7, 4)  [h, :, w, :]
    :param xTrain:
    :return:
    """
    fig: plotly.graph_objects.Figure = plotly.subplots.make_subplots(rows=7, cols=7, shared_xaxes=True,  shared_yaxes=True, vertical_spacing=0,  horizontal_spacing=0)
    xTrain0Img: torch.Tensor = xTrain[0].reshape((PIC_H, PIC_W))
    plotly.express.imshow(img=xTrain0Img).show()
    xTrain0ImgCells: torch.Tensor = xTrain0Img.reshape((7, 4, 7, 4))
    xTrain0ImgCells=torch.swapdims(input=xTrain0ImgCells,dim0=1,dim1=2)#交换 (7, 4, 7, 4) 维度1、维度2 即 (0:7, 1:4, 2:7, 3:4)
    for h in range(7):
        for w in range(7):
            print(f"h,w:{h},{w}")
            fig.add_trace(trace=plotly.express.imshow(xTrain0ImgCells[h, w]).data[0], col=h + 1, row=w + 1) # [h, w, :, :] 或 [h, w]
    fig.show()

mnist单样本错误的分拆显示

以下 mnist单样本错误的分拆显示:

# mnist单样本错误的分拆显示:
    fig: plotly.graph_objects.Figure = plotly.subplots.make_subplots(rows=7, cols=7, shared_xaxes=True,  shared_yaxes=True, vertical_spacing=0,  horizontal_spacing=0)
    xTrain0Img: torch.Tensor = xTrain[0].reshape((PIC_H, PIC_W))
    plotly.express.imshow(img=xTrain0Img).show()
    xTrain0ImgCells: torch.Tensor = xTrain0Img.reshape((4,7, 4, 7))  #原本是: (7,4,7,4)
    for h in range(7):
        for w in range(7):
            print(f"h,w:{h},{w}")
            fig.add_trace(trace=plotly.express.imshow(xTrain0ImgCells[:, h,  :, w]).data[0], col=h + 1, row=w + 1)  #原本是: [h,:,w,:]
    fig.show()

其结果为: 由此图可知 (4,7, 4, 7) [:, h, :, w] 是间隔的取出而形成的4*4的小方格