mxnet由resnet换向alexnet时候出现问题

最新推荐文章于 2025-07-30 15:29:18 发布

AllyLi0224

最新推荐文章于 2025-07-30 15:29:18 发布

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分类专栏： mxnet python

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本文介绍了一个基于MXNet的卷积神经网络模型，并详细展示了如何调整模型参数以优化训练效果。通过调整卷积层的过滤器数量等关键设置，可以诊断模型表现不佳的原因。

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调试参数解决：

import mxnet as mx

def get_symbol(num_classes, **kwargs):
    input_data = mx.symbol.Variable(name="data")
    # stage 1
    conv1 = mx.symbol.Convolution(
        data=input_data, kernel=(11, 11), stride=(2, 2), num_filter=96)
    relu1 = mx.symbol.Activation(data=conv1, act_type="relu")
    pool1 = mx.symbol.Pooling(
        data=relu1, pool_type="max", kernel=(3, 3), stride=(2,2))
    lrn1 = mx.symbol.LRN(data=pool1, alpha=0.0001, beta=0.75, knorm=1, nsize=5)
    # stage 2
    conv2 = mx.symbol.Convolution(
        data=lrn1, kernel=(5, 5), pad=(2, 2), num_filter=256)
    relu2 = mx.symbol.Activation(data=conv2, act_type="relu")
    pool2 = mx.symbol.Pooling(data=relu2, kernel=(3, 3), stride=(2, 2), pool_type="max")
    lrn2 = mx.symbol.LRN(data=pool2, alpha=0.0001, beta=0.75, knorm=1, nsize=5)
    # stage 3
    conv3 = mx.symbol.Convolution(
        data=lrn2, kernel=(1, 1), pad=(1, 1), num_filter=384)   #参数调小了，原来为3
    relu3 = mx.symbol.Activation(data=conv3, act_type="relu")
    conv4 = mx.symbol.Convolution(
        data=relu3, kernel=(1, 1), pad=(1, 1), num_filter=384)  #参数调小了，原来为3
    relu4 = mx.symbol.Activation(data=conv4, act_type="relu")
    conv5 = mx.symbol.Convolution(
        data=relu4, kernel=(1, 1), pad=(1, 1), num_filter=256)  #参数调小了，原来为3
    relu5 = mx.symbol.Activation(data=conv5, act_type="relu")
    pool3 = mx.symbol.Pooling(data=relu5, kernel=(3, 3), stride=(2, 2), pool_type="max")
    # stage 4
    flatten = mx.symbol.Flatten(data=pool3)
    fc1 = mx.symbol.FullyConnected(data=flatten, num_hidden=4096)
    relu6 = mx.symbol.Activation(data=fc1, act_type="relu")
    dropout1 = mx.symbol.Dropout(data=relu6, p=0.5)
    # stage 5
    fc2 = mx.symbol.FullyConnected(data=dropout1, num_hidden=4096)
    relu7 = mx.symbol.Activation(data=fc2, act_type="relu")
    dropout2 = mx.symbol.Dropout(data=relu7, p=0.5)
    # stage 6
    fc3 = mx.symbol.FullyConnected(data=dropout2, num_hidden=num_classes)
    softmax = mx.symbol.SoftmaxOutput(data=fc3, name='softmax')
    return softmax

调整以后训练结果不一定好，只是为了找寻换模型不通过的原因才调试