MXNet之CNN：自定义CNN-OCR算法训练车牌数据集(umpy.ndarray格式数据)的模型实现一张新车牌照片字符预测

输出结果

设计思路

核心代码

def getnet():        

   data = mx.symbol.Variable('data')

   label = mx.symbol.Variable('softmax_label')

   conv1 = mx.symbol.Convolution(data=data, kernel=(5,5), num_filter=32)

   pool1 = mx.symbol.Pooling(data=conv1, pool_type="max", kernel=(2,2), stride=(1, 1))

   relu1 = mx.symbol.Activation(data=pool1, act_type="relu")

   conv2 = mx.symbol.Convolution(data=relu1, kernel=(5,5), num_filter=32)

   pool2 = mx.symbol.Pooling(data=conv2, pool_type="avg", kernel=(2,2), stride=(1, 1))

   relu2 = mx.symbol.Activation(data=pool2, act_type="relu")

   flatten = mx.symbol.Flatten(data = relu2)

   fc1 = mx.symbol.FullyConnected(data = flatten, num_hidden = 120)  

   fc21 = mx.symbol.FullyConnected(data = fc1, num_hidden = 65)

   fc22 = mx.symbol.FullyConnected(data = fc1, num_hidden = 65)

   fc23 = mx.symbol.FullyConnected(data = fc1, num_hidden = 65)

   fc24 = mx.symbol.FullyConnected(data = fc1, num_hidden = 65)

   fc25 = mx.symbol.FullyConnected(data = fc1, num_hidden = 65)

   fc26 = mx.symbol.FullyConnected(data = fc1, num_hidden = 65)

   fc27 = mx.symbol.FullyConnected(data = fc1, num_hidden = 65)

   fc2 = mx.symbol.Concat(*[fc21, fc22, fc23, fc24,fc25,fc26,fc27], dim = 0)  

   return mx.symbol.SoftmaxOutput(data = fc2, name = "softmax")

sym = getnet()

executor = sym.simple_bind(ctx = mx.cpu(), **input_shapes)