class CNN(Model): def __init__(self): super(CNN, self).__init__() ## self.x1 = Input(shape =(1024,1,1)) self.inputshape = tf.keras.layers.InputLayer(input_shape=(1024,1,1)) self.c1 = Conv2D(filters=64, kernel_size=(2, 1), activation='relu',input_shape=(1024,1,1)) self.c2 = Conv2D(filters=64, kernel_size=(2, 1), activation='relu') self.c3 = Conv2D(filters=64, kernel_size=(2, 1), activation='relu') self.flatten = Flatten() self.f1 = Dense(360, activation='relu') self.f2 = Dense(184, activation='relu') self.f3 = Dense(4, activation='softmax') def zx(self, input): x = self.inputshape(input.astype(np.float32)) return self.c1(x) def zx1(self, input): x = self.inputshape(input.astype(np.float32)) x = self.c1(x) x = self.c2(x) x = self.c3(x) x = self.flatten(x) print(x.shape) x = self.f1(x) return self.f2(x) def call(self, input): x = self.inputshape(input) x = self.c1(x) x = self.c2(x) x = self.c3(x) x = self.flatten(x) print(x.shape) x = self.f1(x) self.x2 = self.f2(x) y = self.f3(self.x2) return y model = CNN() model.build(input_shape=(None,1024,1,1)) model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False), metrics=['sparse_categorical_accuracy']) 该CNN模型每层网络的参数如何

这个CNN模型共有5个卷积层和3个全连接层，每个卷积层都使用ReLU激活函数。具体参数如下：

- 输入层：输入形状为(1024,1,1)的张量
- 卷积层c1：使用64个大小为(2,1)的卷积核，步长为(1,1)，padding方式为"valid"，输出张量形状为(1023,1,64)
- 卷积层c2：使用64个大小为(2,1)的卷积核，步长为(1,1)，padding方式为"valid"，输出张量形状为(1022,1,64)
- 卷积层c3：使用64个大小为(2,1)的卷积核，步长为(1,1)，padding方式为"valid"，输出张量形状为(1021,1,64)
- 将输出张量展平为形状为(65504,)的向量
- 全连接层f1：输出向量形状为(360,)，使用ReLU激活函数
- 全连接层f2：输出向量形状为(184,)，使用ReLU激活函数
- 输出层：输出向量形状为(4,)，使用softmax激活函数

相关问题

class DoubleFastRCNNOutputLayers(nn.Module): def __init__( self, cfg, input_size, num_classes, cls_agnostic_bbox_reg, box_dim=4 ): super(DoubleFastRCNNOutputLayers, self).__init__() if not isinstance(input_size, int): input_size = np.prod(input_size) self.cls_score = nn.Linear(input_size, num_classes + 1) num_bbox_reg_classes = 1 if cls_agnostic_bbox_reg else num_classes self.bbox_pred = nn.Linear(input_size, num_bbox_reg_classes * box_dim) nn.init.normal_(self.cls_score.weight, std=0.01) nn.init.normal_(self.bbox_pred.weight, std=0.001) for l in [self.cls_score, self.bbox_pred]: nn.init.constant_(l.bias, 0) self._do_cls_dropout = cfg.MODEL.ROI_HEADS.CLS_DROPOUT self._dropout_ratio = cfg.MODEL.ROI_HEADS.DROPOUT_RATIO def forward(self, x_s, x_l): if x_s.dim() > 2: x_s = torch.flatten(x_s, start_dim=1) if x_l.dim() > 2: x_l = torch.flatten(x_l, start_dim=1) proposal_deltas = self.bbox_pred(x_l) if self._do_cls_dropout: x_s = F.dropout(x_s, self._dropout_ratio, training=self.training) scores = self.cls_score(x_s) return scores, proposal_deltas

这段代码是一个双输入的Fast R-CNN输出层的实现，其中包括一个分类得分层和一个边界框回归层。它接受两个输入x_s和x_l，分别代表短边和长边的特征。在前向传播时，它首先对输入进行扁平化处理，然后通过bbox_pred层获得边界框预测值，通过cls_score层获得分类得分。在进行分类得分的计算时，可以进行dropout操作来防止过拟合。最终，返回分类得分和边界框预测值。

class CNNmodel(nn.Module): def __init__(self): super(CNNmodel, self).__init__() self.conv1d = nn.Conv1d(1, 64, kernel_size=2) self.relu = nn.ReLU(inplace=True) self.Linear1 = nn.Linear(64*75, 50) self.Linear2 = nn.Linear(50, 1) def forward(self, x): x = self.conv1d(x) x = self.relu(x) x = x.view(-1) x = self.Linear1(x) x = self.relu(x) x = self.Linear2(x) return x报错

这段代码存在一些问题，导致运行时报错。以下是可能出现的问题及解决方法：

1. `x = x.view(-1)`这一行应该改为`x = x.view(-1, 64*75)`，因为在前面`self.conv1d`的输出通道数为64，而输出的feature map长度为75，需要将其展平后输入全连接层。

2. `self.Linear2 = nn.Linear(50, 1)`中的1代表输出的类别数，如果是二分类问题，应该改为`self.Linear2 = nn.Linear(50, 2)`，并在模型的输出处加上softmax激活函数。

修改后的代码如下：

class CNNmodel(nn.Module):
    def __init__(self):
        super(CNNmodel, self).__init__()
        self.conv1d = nn.Conv1d(1, 64, kernel_size=2)
        self.relu = nn.ReLU(inplace=True)
        self.Linear1 = nn.Linear(64*75, 50)
        self.Linear2 = nn.Linear(50, 2)

    def forward(self, x):
        x = self.conv1d(x)
        x = self.relu(x)
        x = x.view(-1, 64*75)
        x = self.Linear1(x)
        x = self.relu(x)
        x = self.Linear2(x)
        x = F.softmax(x, dim=1)
        return x