plotly 分割显示 mnist

加载mnist

import numpydef 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.expressimport plotly.graph_objectsimport plotly.subplotsimport numpyxTrain:numpy.ndarray=numpy.random.random((2,28,28))#xTrain[0].shape:(28,28)#fig:plotly.graph_objects.Figure=Nonefig=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 numpyxTrain:numpy.ndarray=numpy.random.random((2,28,28))#xTrain[0].shape:(28,28)import plotly.expressimport plotly.graph_objectsplotly.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