plotly分割显示mnist的方法

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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)

1

xTrain:torch.Tensor;yTrain:torch.Tensor; xTest:torch.Tensor; yTest:torch.Tensor(xTrain,yTrain,xTest,yTest)=loadMnist()

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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

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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

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#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()

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"""

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()

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# 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()