将cifar10改成单一通道后，套用前面的softmax分类，分类率40%左右，想哭。。。

 

 

In [1]:

%matplotlib inlinefrom mxnet.gluon import data as gdatafrom mxnet import autograd,ndimport gluonbook as gbimport sys

In [2]:

cifar_train = gdata.vision.CIFAR10(train=True)cifar_test = gdata.vision.CIFAR10(train=False)

In [3]:

(len(cifar_train),len(cifar_test))

Out[3]:

(50000, 10000)

In [4]:

feature,label = cifar_train[0]

In [5]:

feature.shape,feature.dtype

Out[5]:

((32, 32, 3), numpy.uint8)

In [6]:

label,type(label),label.dtype

Out[6]:

(6, numpy.int32, dtype('int32'))

In [7]:

batch_size = 256transformer = gdata.vision.transforms.ToTensor()

In [8]:

if sys.platform.startswith('win'):    num_workers = 0  # 0 表示不用额外的进程来加速读取数据。else:    num_workers = 4train_iter = gdata.DataLoader(cifar_train.transform_first(transformer),                              batch_size, shuffle=True,                              num_workers=num_workers)test_iter = gdata.DataLoader(cifar_test.transform_first(transformer),                             batch_size, shuffle=False,                             num_workers=num_workers)

In [9]:

len(train_iter)

Out[9]:

196

In [10]:

for X,y in train_iter:    print(X)    break

 

[[[[0.3137255  0.3019608  0.34509805 ... 0.2901961  0.3019608    0.34901962]   [0.36078432 0.35686275 0.32941177 ... 0.23137255 0.2509804    0.3764706 ]   [0.34509805 0.42352942 0.47058824 ... 0.1882353  0.19607843    0.3254902 ]   ...   [0.7529412  0.654902   0.5882353  ... 0.67058825 0.6627451    0.78039217]   [0.72156864 0.60784316 0.5764706  ... 0.63529414 0.63529414    0.7372549 ]   [0.65882355 0.6117647  0.6039216  ... 0.67058825 0.6627451    0.6901961 ]]  [[0.3137255  0.28627452 0.3137255  ... 0.28627452 0.29803923    0.34509805]   [0.36078432 0.34117648 0.3019608  ... 0.22745098 0.24705882    0.37254903]   [0.34509805 0.40392157 0.44313726 ... 0.18431373 0.19215687    0.32156864]   ...   [0.8039216  0.7058824  0.6431373  ... 0.7019608  0.69803923    0.8156863 ]   [0.7764706  0.6627451  0.6313726  ... 0.6666667  0.6666667    0.7764706 ]   [0.7176471  0.6666667  0.65882355 ... 0.7019608  0.69803923    0.7254902 ]]  [[0.21960784 0.2        0.23137255 ... 0.21176471 0.21960784    0.26666668]   [0.26666668 0.2509804  0.21960784 ... 0.14901961 0.16862746    0.29411766]   [0.2509804  0.31764707 0.36078432 ... 0.10588235 0.11372549    0.24313726]   ...   [0.6039216  0.5058824  0.4392157  ... 0.49803922 0.48235294    0.5882353 ]   [0.5764706  0.4627451  0.43137255 ... 0.46666667 0.4627451    0.5529412 ]   [0.5137255  0.46666667 0.45882353 ... 0.5137255  0.49803922    0.5137255 ]]] [[[0.14901961 0.14901961 0.15294118 ... 0.14509805 0.09411765    0.23137255]   [0.15686275 0.15686275 0.16078432 ... 0.15686275 0.11372549    0.2509804 ]   [0.16078432 0.16470589 0.16862746 ... 0.16862746 0.12941177    0.2627451 ]   ...   [0.16862746 0.12156863 0.14901961 ... 0.30588236 0.42352942    0.24313726]   [0.16862746 0.1254902  0.13333334 ... 0.28235295 0.39607844    0.22352941]   [0.16470589 0.1254902  0.09411765 ... 0.19607843 0.29411766    0.16862746]]  [[0.15294118 0.15294118 0.15686275 ... 0.15294118 0.09803922    0.23529412]   [0.16078432 0.16078432 0.16470589 ... 0.16470589 0.11764706    0.25490198]   [0.16470589 0.16862746 0.17254902 ... 0.1764706  0.13725491    0.27058825]   ...   [0.17254902 0.1254902  0.14901961 ... 0.23137255 0.3019608    0.19607843]   [0.16862746 0.1254902  0.13333334 ... 0.22745098 0.28627452    0.18039216]   [0.16862746 0.12941177 0.09411765 ... 0.1764706  0.24705882    0.14901961]]  [[0.13333334 0.13333334 0.13725491 ... 0.15686275 0.09019608    0.21568628]   [0.14117648 0.14117648 0.14509805 ... 0.16862746 0.10980392    0.23529412]   [0.14509805 0.14901961 0.15294118 ... 0.18039216 0.1254902    0.24705882]   ...   [0.14901961 0.10980392 0.13333334 ... 0.17254902 0.21960784    0.15686275]   [0.14901961 0.11372549 0.12156863 ... 0.18431373 0.20392157    0.13333334]   [0.14901961 0.11372549 0.08627451 ... 0.16078432 0.21176471    0.1254902 ]]] [[[0.07843138 0.08627451 0.10196079 ... 0.0627451  0.05490196    0.04705882]   [0.10980392 0.08627451 0.11764706 ... 0.06666667 0.05490196    0.04705882]   [0.09019608 0.07058824 0.09411765 ... 0.05882353 0.05882353    0.04705882]   ...   [0.18039216 0.16862746 0.1882353  ... 0.13725491 0.13725491    0.13333334]   [0.14901961 0.15294118 0.16470589 ... 0.14901961 0.12941177    0.12156863]   [0.13725491 0.14117648 0.15686275 ... 0.13725491 0.12156863    0.11764706]]  [[0.08627451 0.09411765 0.10980392 ... 0.07058824 0.0627451    0.05490196]   [0.12156863 0.09411765 0.1254902  ... 0.07450981 0.0627451    0.05490196]   [0.10588235 0.08235294 0.10196079 ... 0.06666667 0.06666667    0.05490196]   ...   [0.19607843 0.1882353  0.2        ... 0.15294118 0.15294118    0.14509805]   [0.16470589 0.17254902 0.1764706  ... 0.16078432 0.14117648    0.13333334]   [0.15294118 0.16078432 0.16862746 ... 0.14901961 0.13333334    0.12941177]]  [[0.07058824 0.07843138 0.09019608 ... 0.05882353 0.05098039    0.05098039]   [0.10980392 0.07450981 0.10588235 ... 0.0627451  0.05490196    0.05098039]   [0.08627451 0.05882353 0.08627451 ... 0.05490196 0.05490196    0.04705882]   ...   [0.16078432 0.14901961 0.16862746 ... 0.1254902  0.1254902    0.12156863]   [0.12941177 0.13333334 0.14117648 ... 0.13333334 0.11372549    0.10588235]   [0.11764706 0.1254902  0.13333334 ... 0.12156863 0.10588235    0.10196079]]] ... [[[0.20784314 0.36078432 0.85490197 ... 0.972549   0.9647059    0.96862745]   [0.22745098 0.35686275 0.827451   ... 0.9764706  0.96862745    0.9647059 ]   [0.3372549  0.5019608  0.90588236 ... 0.9764706  0.9764706    0.9647059 ]   ...   [0.08627451 0.08627451 0.05098039 ... 0.15294118 0.10980392    0.09803922]   [0.14901961 0.09411765 0.05098039 ... 0.10980392 0.18431373    0.2784314 ]   [0.3882353  0.27058825 0.14117648 ... 0.07058824 0.11764706    0.16470589]]  [[0.09803922 0.24705882 0.8156863  ... 0.9411765  0.9254902    0.91764706]   [0.14509805 0.25882354 0.7882353  ... 0.9372549  0.9254902    0.8980392 ]   [0.2784314  0.43137255 0.88235295 ... 0.9372549  0.9411765    0.92941177]   ...   [0.06666667 0.07450981 0.05098039 ... 0.13725491 0.09411765    0.08235294]   [0.14117648 0.09019608 0.05098039 ... 0.09803922 0.17254902    0.26666668]   [0.3882353  0.27450982 0.14117648 ... 0.0627451  0.10980392    0.15686275]]  [[0.10588235 0.26666668 0.827451   ... 0.9607843  0.9411765    0.92156863]   [0.14117648 0.28627452 0.8156863  ... 0.94509804 0.9411765    0.9254902 ]   [0.27450982 0.4392157  0.88235295 ... 0.9254902  0.9490196    0.96862745]   ...   [0.0627451  0.07058824 0.04313726 ... 0.13725491 0.09803922    0.09019608]   [0.13333334 0.08235294 0.04313726 ... 0.09803922 0.1764706    0.27058825]   [0.38039216 0.2627451  0.13333334 ... 0.06666667 0.11372549    0.16078432]]] [[[0.35686275 0.33333334 0.34901962 ... 0.19607843 0.1882353    0.1882353 ]   [0.38431373 0.37254903 0.39215687 ... 0.25882354 0.27450982    0.2627451 ]   [0.38431373 0.38039216 0.3882353  ... 0.2509804  0.25490198    0.24705882]   ...   [0.7764706  0.76862746 0.72156864 ... 0.76862746 0.77254903    0.77254903]   [0.77254903 0.7647059  0.77254903 ... 0.76862746 0.76862746    0.77254903]   [0.7647059  0.75686276 0.7529412  ... 0.75686276 0.7529412    0.75686276]]  [[0.35686275 0.3372549  0.34509805 ... 0.20784314 0.20392157    0.19607843]   [0.3882353  0.38039216 0.39607844 ... 0.26666668 0.2901961    0.2627451 ]   [0.3882353  0.38039216 0.3882353  ... 0.2509804  0.26666668    0.25490198]   ...   [0.78039217 0.77254903 0.73333335 ... 0.76862746 0.77254903    0.77254903]   [0.77254903 0.7647059  0.77254903 ... 0.76862746 0.76862746    0.77254903]   [0.7647059  0.75686276 0.75686276 ... 0.7490196  0.7529412    0.75686276]]  [[0.2901961  0.2627451  0.28235295 ... 0.13725491 0.13725491    0.13725491]   [0.34901962 0.3372549  0.36078432 ... 0.20392157 0.21960784    0.2       ]   [0.36078432 0.3529412  0.37254903 ... 0.20784314 0.21568628    0.21176471]   ...   [0.77254903 0.7607843  0.72156864 ... 0.7607843  0.7647059    0.7647059 ]   [0.7647059  0.75686276 0.7607843  ... 0.7607843  0.7607843    0.7647059 ]   [0.7607843  0.7529412  0.7490196  ... 0.74509805 0.74509805    0.7490196 ]]] [[[0.8745098  0.8784314  0.8784314  ... 0.8235294  0.8    0.7490196 ]   [0.83137256 0.8235294  0.827451   ... 0.7647059  0.74509805    0.73333335]   [0.8039216  0.79607844 0.8039216  ... 0.67058825 0.6313726    0.70980394]   ...   [0.40784314 0.3647059  0.34901962 ... 0.29803923 0.27450982    0.28235295]   [0.41568628 0.36078432 0.35686275 ... 0.26666668 0.25882354    0.28627452]   [0.3882353  0.3529412  0.34117648 ... 0.2784314  0.26666668    0.28235295]]  [[0.8901961  0.89411765 0.89411765 ... 0.8117647  0.8039216    0.76862746]   [0.84705883 0.8392157  0.84313726 ... 0.75686276 0.74509805    0.7529412 ]   [0.81960785 0.8117647  0.81960785 ... 0.6627451  0.6313726    0.7294118 ]   ...   [0.3372549  0.31764707 0.30588236 ... 0.2784314  0.25490198    0.2627451 ]   [0.32156864 0.29803923 0.29411766 ... 0.23921569 0.23529412    0.25882354]   [0.29411766 0.28235295 0.27450982 ... 0.2509804  0.24705882    0.25882354]]  [[0.9372549  0.9411765  0.9411765  ... 0.85490197 0.8627451    0.8352941 ]   [0.89411765 0.8862745  0.8901961  ... 0.79607844 0.8039216    0.81960785]   [0.8666667  0.85882354 0.8666667  ... 0.7019608  0.6901961    0.79607844]   ...   [0.23921569 0.20784314 0.19607843 ... 0.30588236 0.2627451    0.2627451 ]   [0.23529412 0.2        0.19607843 ... 0.26666668 0.23137255    0.2509804 ]   [0.21960784 0.2        0.1882353  ... 0.27058825 0.23921569    0.2509804 ]]]]
           

 

In [11]:

def wrapped_iter(data_iter):    for X, y in data_iter:        X = X[:, :1, :, :]        yield X, yfor X, y in wrapped_iter(train_iter):    print(X)    print(y)    breakfor X, y in wrapped_iter(test_iter):    print(X)    print(y)    break

 

[[[[0.40784314 0.3882353  0.40392157 ... 0.2509804  0.23921569    0.22745098]   [0.4        0.3882353  0.4        ... 0.2627451  0.2627451    0.23529412]   [0.39607844 0.38039216 0.4        ... 0.2901961  0.2901961    0.26666668]   ...   [0.79607844 0.7882353  0.7882353  ... 0.59607846 0.58431375    0.5764706 ]   [0.74509805 0.7607843  0.74509805 ... 0.6431373  0.62352943    0.6117647 ]   [0.73333335 0.7254902  0.7372549  ... 0.6392157  0.6431373    0.6313726 ]]] [[[1.         0.99215686 0.96862745 ... 0.62352943 0.6862745    0.8627451 ]   [1.         0.96862745 0.92156863 ... 0.5764706  0.6901961    0.7607843 ]   [1.         0.95686275 0.8745098  ... 0.63529414 0.7529412    0.7607843 ]   ...   [0.49411765 0.5058824  0.58431375 ... 0.7019608  0.7294118    0.7490196 ]   [0.6431373  0.69803923 0.7254902  ... 0.7019608  0.7137255    0.7176471 ]   [0.8666667  0.9137255  0.8039216  ... 0.7058824  0.75686276    0.77254903]]] [[[0.5411765  0.5411765  0.5647059  ... 0.29411766 0.21960784    0.25882354]   [0.58431375 0.56078434 0.5803922  ... 0.25490198 0.20392157    0.26666668]   [0.61960787 0.5686275  0.57254905 ... 0.23137255 0.21960784    0.25882354]   ...   [0.59607846 0.6745098  0.70980394 ... 0.8352941  0.81960785    0.8       ]   [0.60784316 0.6901961  0.70980394 ... 0.8980392  0.91764706    0.8156863 ]   [0.6745098  0.75686276 0.7372549  ... 0.89411765 0.92156863    0.9098039 ]]] ... [[[0.20392157 0.21176471 0.2        ... 0.14509805 0.16862746    0.13725491]   [0.19215687 0.20392157 0.21568628 ... 0.15294118 0.12156863    0.09019608]   [0.22352941 0.20784314 0.19607843 ... 0.21176471 0.17254902    0.09803922]   ...   [0.49019608 0.47058824 0.5058824  ... 0.17254902 0.09411765    0.14509805]   [0.5019608  0.5882353  0.7019608  ... 0.1882353  0.18039216    0.18039216]   [0.42352942 0.5529412  0.68235296 ... 0.2        0.20784314    0.23137255]]] [[[0.6431373  0.5803922  0.5921569  ... 0.24313726 0.3647059    0.27450982]   [0.69803923 0.6901961  0.5372549  ... 0.40392157 0.36078432    0.2901961 ]   [0.44705883 0.65882355 0.6        ... 0.49803922 0.3529412    0.29411766]   ...   [0.827451   0.8039216  0.72156864 ... 0.25490198 0.25490198    0.29411766]   [0.89411765 0.8156863  0.7490196  ... 0.23529412 0.25882354    0.2901961 ]   [0.91764706 0.8392157  0.65882355 ... 0.22352941 0.22745098    0.27058825]]] [[[0.04313726 0.07843138 0.14117648 ... 0.31764707 0.3254902    0.25882354]   [0.03529412 0.0627451  0.10980392 ... 0.3254902  0.28235295    0.2627451 ]   [0.01960784 0.05098039 0.07843138 ... 0.27450982 0.23529412    0.2901961 ]   ...   [0.2627451  0.2901961  0.2509804  ... 0.32941177 0.34901962    0.3254902 ]   [0.24313726 0.21176471 0.1882353  ... 0.32941177 0.3137255    0.28627452]   [0.28235295 0.24705882 0.21960784 ... 0.3254902  0.29411766    0.26666668]]]]
           
            [2 9 4 7 3 1 3 5 9 6 2 9 4 4 9 5 3 7 2 9 3 2 1 4 3 1 0 6 7 4 4 0 5 6 3 3 8 2 6 1 8 1 4 0 7 1 4 8 4 5 1 0 6 8 1 0 8 4 4 7 0 9 9 2 6 4 4 2 7 3 4 3 0 0 9 2 4 0 7 6 5 9 6 5 0 0 0 6 7 8 8 7 7 8 7 9 3 4 4 6 1 0 5 6 0 6 6 7 1 8 9 2 2 5 2 9 9 8 6 2 4 3 1 7 0 2 4 8 3 6 3 7 2 4 4 9 2 3 7 0 6 9 4 9 6 6 7 6 8 2 5 4 7 6 0 2 9 5 9 3 1 5 9 2 1 7 7 0 5 0 5 2 3 9 7 1 3 5 5 7 0 6 2 3 1 5 3 6 2 2 5 7 0 7 5 8 5 9 7 0 7 2 8 1 7 4 2 3 8 6 1 6 1 6 0 8 8 8 7 9 4 2 6 6 9 1 5 2 5 1 4 6 1 8 9 2 4 7 0 4 3 3 6 5 9 4 1 0 2 5 9 3 1 6 6 6]
            
             [[[[0.61960787 0.62352943 0.64705884 ... 0.5372549  0.49411765    0.45490196]   [0.59607846 0.5921569  0.62352943 ... 0.53333336 0.49019608    0.46666667]   [0.5921569  0.5921569  0.61960787 ... 0.54509807 0.50980395    0.47058824]   ...   [0.26666668 0.16470589 0.12156863 ... 0.14901961 0.05098039    0.15686275]   [0.23921569 0.19215687 0.13725491 ... 0.10196079 0.11372549    0.07843138]   [0.21176471 0.21960784 0.1764706  ... 0.09411765 0.13333334    0.08235294]]] [[[0.92156863 0.90588236 0.9098039  ... 0.9137255  0.9137255    0.9098039 ]   [0.93333334 0.92156863 0.92156863 ... 0.9254902  0.9254902    0.92156863]   [0.92941177 0.91764706 0.91764706 ... 0.92156863 0.92156863    0.91764706]   ...   [0.34117648 0.16862746 0.07450981 ... 0.6627451  0.7137255    0.7372549 ]   [0.32156864 0.18039216 0.14117648 ... 0.68235296 0.7254902    0.73333335]   [0.33333334 0.24313726 0.22745098 ... 0.65882355 0.7058824    0.7294118 ]]] [[[0.61960787 0.61960787 0.54509807 ... 0.89411765 0.92941177    0.93333334]   [0.6666667  0.6745098  0.5921569  ... 0.9098039  0.9647059    0.9647059 ]   [0.68235296 0.6901961  0.6156863  ... 0.9019608  0.98039216    0.9607843 ]   ...   [0.12156863 0.11764706 0.10196079 ... 0.14509805 0.03529412    0.01568628]   [0.09019608 0.10588235 0.09803922 ... 0.07450981 0.01568628    0.01960784]   [0.10980392 0.11764706 0.1254902  ... 0.01960784 0.01568628    0.02745098]]] ... [[[0.2627451  0.26666668 0.27450982 ... 0.28235295 0.2784314    0.27450982]   [0.27058825 0.2784314  0.28627452 ... 0.2901961  0.2901961    0.28627452]   [0.2784314  0.28235295 0.28627452 ... 0.29411766 0.2901961    0.28627452]   ...   [0.35686275 0.3882353  0.37254903 ... 0.30980393 0.34901962    0.3647059 ]   [0.33333334 0.35686275 0.34901962 ... 0.27058825 0.26666668    0.28235295]   [0.3254902  0.3372549  0.33333334 ... 0.2627451  0.26666668    0.25882354]]] [[[0.7254902  0.7058824  0.6745098  ... 0.6156863  0.59607846    0.54901963]   [0.7921569  0.69411767 0.63529414 ... 0.6039216  0.5764706    0.5529412 ]   [0.7176471  0.6392157  0.627451   ... 0.5764706  0.5764706    0.5803922 ]   ...   [0.6901961  0.62352943 0.6156863  ... 0.37254903 0.31764707    0.29803923]   [0.6784314  0.6392157  0.67058825 ... 0.39215687 0.38431373    0.36078432]   [0.64705884 0.59607846 0.62352943 ... 0.47843137 0.5176471    0.46666667]]] [[[0.8        0.8039216  0.8156863  ... 0.8352941  0.84705883    0.84705883]   [0.80784315 0.8156863  0.827451   ... 0.8352941  0.8235294    0.827451  ]   [0.7882353  0.7921569  0.80784315 ... 0.78431374 0.76862746    0.76862746]   ...   [0.5058824  0.50980395 0.52156866 ... 0.45882353 0.5137255    0.5294118 ]   [0.49411765 0.49803922 0.5058824  ... 0.4627451  0.5176471    0.5254902 ]   [0.4862745  0.49019608 0.49803922 ... 0.4509804  0.49803922    0.5058824 ]]]]
             
              [3 8 8 0 6 6 1 6 3 1 0 9 5 7 9 8 5 7 8 6 7 0 4 9 5 2 4 0 9 6 6 5 4 5 9 2 4 1 9 5 4 6 5 6 0 9 3 9 7 6 9 8 0 3 8 8 7 7 4 6 7 3 6 3 6 2 1 2 3 7 2 6 8 8 0 2 9 3 3 8 8 1 1 7 2 5 2 7 8 9 0 3 8 6 4 6 6 0 0 7 4 5 6 3 1 1 3 6 8 7 4 0 6 2 1 3 0 4 2 7 8 3 1 2 8 0 8 3 5 2 4 1 8 9 1 2 9 7 2 9 6 5 6 3 8 7 6 2 5 2 8 9 6 0 0 5 2 9 5 4 2 1 6 6 8 4 8 4 5 0 9 9 9 8 9 9 3 7 5 0 0 5 2 2 3 8 6 3 4 0 5 8 0 1 7 2 8 8 7 8 5 1 8 7 1 3 0 5 7 9 7 4 5 9 8 0 7 9 8 2 7 6 9 4 3 9 6 4 7 6 5 1 5 8 8 0 4 0 5 5 1 1 8 9 0 3 1 9 2 2 5 3 9 9 4 0]
              
             
            
           

In [12]:

from mxnet import gluon, initfrom mxnet.gluon import loss as gloss, nn

 

In [13]:

net = nn.Sequential()net.add(nn.Dense(10))net.initialize(init.Normal(sigma=0.01))

In [14]:

loss = gloss.SoftmaxCrossEntropyLoss()

In [25]:

trainer = gluon.Trainer(net.collect_params(), 'sgd', {
          'learning_rate': 0.0001})

In [26]:

num_epochs = 100gb.train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, None,             None, trainer)

 

epoch 1, loss 1.6195, train acc 0.457, test acc 0.410epoch 2, loss 1.6196, train acc 0.457, test acc 0.411epoch 3, loss 1.6181, train acc 0.457, test acc 0.411epoch 4, loss 1.6183, train acc 0.457, test acc 0.411epoch 5, loss 1.6191, train acc 0.457, test acc 0.410epoch 6, loss 1.6196, train acc 0.457, test acc 0.411epoch 7, loss 1.6189, train acc 0.457, test acc 0.410epoch 8, loss 1.6189, train acc 0.457, test acc 0.411epoch 9, loss 1.6183, train acc 0.457, test acc 0.410epoch 10, loss 1.6186, train acc 0.457, test acc 0.411epoch 11, loss 1.6182, train acc 0.457, test acc 0.410epoch 12, loss 1.6175, train acc 0.457, test acc 0.410epoch 13, loss 1.6181, train acc 0.457, test acc 0.410epoch 14, loss 1.6182, train acc 0.457, test acc 0.411epoch 15, loss 1.6192, train acc 0.457, test acc 0.410epoch 16, loss 1.6191, train acc 0.457, test acc 0.411epoch 17, loss 1.6182, train acc 0.457, test acc 0.410epoch 18, loss 1.6176, train acc 0.457, test acc 0.410epoch 19, loss 1.6175, train acc 0.458, test acc 0.410epoch 20, loss 1.6182, train acc 0.457, test acc 0.410epoch 21, loss 1.6178, train acc 0.457, test acc 0.410epoch 22, loss 1.6180, train acc 0.457, test acc 0.410epoch 23, loss 1.6178, train acc 0.457, test acc 0.411epoch 24, loss 1.6179, train acc 0.457, test acc 0.411epoch 25, loss 1.6178, train acc 0.457, test acc 0.411epoch 26, loss 1.6180, train acc 0.457, test acc 0.411epoch 27, loss 1.6181, train acc 0.457, test acc 0.410epoch 28, loss 1.6172, train acc 0.457, test acc 0.410epoch 29, loss 1.6177, train acc 0.457, test acc 0.411epoch 30, loss 1.6170, train acc 0.458, test acc 0.410epoch 31, loss 1.6162, train acc 0.458, test acc 0.410epoch 32, loss 1.6184, train acc 0.457, test acc 0.410epoch 33, loss 1.6175, train acc 0.457, test acc 0.410epoch 34, loss 1.6174, train acc 0.457, test acc 0.411epoch 35, loss 1.6173, train acc 0.457, test acc 0.411epoch 36, loss 1.6177, train acc 0.457, test acc 0.411epoch 37, loss 1.6174, train acc 0.457, test acc 0.410epoch 38, loss 1.6174, train acc 0.457, test acc 0.410epoch 39, loss 1.6171, train acc 0.457, test acc 0.411epoch 40, loss 1.6178, train acc 0.457, test acc 0.410epoch 41, loss 1.6173, train acc 0.457, test acc 0.410epoch 42, loss 1.6169, train acc 0.457, test acc 0.411epoch 43, loss 1.6166, train acc 0.457, test acc 0.410epoch 44, loss 1.6172, train acc 0.457, test acc 0.410epoch 45, loss 1.6166, train acc 0.457, test acc 0.410epoch 46, loss 1.6174, train acc 0.457, test acc 0.410epoch 47, loss 1.6170, train acc 0.457, test acc 0.410epoch 48, loss 1.6166, train acc 0.457, test acc 0.410epoch 49, loss 1.6165, train acc 0.457, test acc 0.410epoch 50, loss 1.6163, train acc 0.457, test acc 0.410epoch 51, loss 1.6167, train acc 0.457, test acc 0.410epoch 52, loss 1.6172, train acc 0.457, test acc 0.410epoch 53, loss 1.6163, train acc 0.458, test acc 0.410epoch 54, loss 1.6166, train acc 0.457, test acc 0.410epoch 55, loss 1.6163, train acc 0.457, test acc 0.410epoch 56, loss 1.6171, train acc 0.457, test acc 0.410epoch 57, loss 1.6170, train acc 0.457, test acc 0.410epoch 58, loss 1.6163, train acc 0.457, test acc 0.410epoch 59, loss 1.6160, train acc 0.458, test acc 0.410epoch 60, loss 1.6163, train acc 0.457, test acc 0.410epoch 61, loss 1.6165, train acc 0.457, test acc 0.410epoch 62, loss 1.6157, train acc 0.457, test acc 0.410epoch 63, loss 1.6169, train acc 0.457, test acc 0.410epoch 64, loss 1.6158, train acc 0.457, test acc 0.410epoch 65, loss 1.6167, train acc 0.457, test acc 0.410epoch 66, loss 1.6162, train acc 0.458, test acc 0.410epoch 67, loss 1.6167, train acc 0.457, test acc 0.410epoch 68, loss 1.6163, train acc 0.457, test acc 0.409epoch 69, loss 1.6170, train acc 0.457, test acc 0.410epoch 70, loss 1.6164, train acc 0.457, test acc 0.410epoch 71, loss 1.6166, train acc 0.457, test acc 0.410epoch 72, loss 1.6157, train acc 0.457, test acc 0.410epoch 73, loss 1.6159, train acc 0.457, test acc 0.410epoch 74, loss 1.6163, train acc 0.457, test acc 0.410epoch 75, loss 1.6162, train acc 0.457, test acc 0.410epoch 76, loss 1.6154, train acc 0.457, test acc 0.409epoch 77, loss 1.6161, train acc 0.457, test acc 0.410epoch 78, loss 1.6169, train acc 0.457, test acc 0.409epoch 79, loss 1.6154, train acc 0.457, test acc 0.409epoch 80, loss 1.6162, train acc 0.457, test acc 0.409epoch 81, loss 1.6163, train acc 0.457, test acc 0.410epoch 82, loss 1.6161, train acc 0.457, test acc 0.409epoch 83, loss 1.6156, train acc 0.457, test acc 0.410epoch 84, loss 1.6153, train acc 0.458, test acc 0.409epoch 85, loss 1.6159, train acc 0.457, test acc 0.409epoch 86, loss 1.6164, train acc 0.457, test acc 0.410epoch 87, loss 1.6154, train acc 0.457, test acc 0.410epoch 88, loss 1.6152, train acc 0.457, test acc 0.410epoch 89, loss 1.6154, train acc 0.457, test acc 0.410epoch 90, loss 1.6155, train acc 0.457, test acc 0.409epoch 91, loss 1.6160, train acc 0.458, test acc 0.409epoch 92, loss 1.6148, train acc 0.458, test acc 0.409epoch 93, loss 1.6156, train acc 0.457, test acc 0.409epoch 94, loss 1.6152, train acc 0.457, test acc 0.409epoch 95, loss 1.6157, train acc 0.458, test acc 0.410epoch 96, loss 1.6152, train acc 0.458, test acc 0.410epoch 97, loss 1.6152, train acc 0.457, test acc 0.410epoch 98, loss 1.6151, train acc 0.457, test acc 0.410epoch 99, loss 1.6150, train acc 0.457, test acc 0.409epoch 100, loss 1.6158, train acc 0.457, test acc 0.410

In [17]:

gb.train_ch3??

In [ ]: