• 设为首页
  • 点击收藏
  • 手机版
    手机扫一扫访问
    迪恩网络手机版
  • 关注官方公众号
    微信扫一扫关注
    迪恩网络公众号
登陆 注册
描述文字

Python functions.max_pooling函数代码示例

原作者: [db:作者] 来自: [db:来源] 收藏 邀请

本文整理汇总了Python中nnabla.functions.max_pooling函数的典型用法代码示例。如果您正苦于以下问题：Python max_pooling函数的具体用法？Python max_pooling怎么用？Python max_pooling使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。



在下文中一共展示了max_pooling函数的8个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Python代码示例。

示例1: cnn_model_003

def cnn_model_003(ctx, x, act=F.relu, test=False):
    with nn.context_scope(ctx):
        # Convblock0
        h = conv_unit(x, "conv00", 128, k=3, s=1, p=1, act=act, test=test)
        h = conv_unit(h, "conv01", 128, k=3, s=1, p=1, act=act, test=test)
        h = conv_unit(h, "conv02", 128, k=3, s=1, p=1, act=act, test=test)
        h = F.max_pooling(h, (2, 2))  # 32 -> 16
        with nn.parameter_scope("bn0"):
            h = PF.batch_normalization(h, batch_stat=not test)
        if not test:
            h = F.dropout(h)

        # Convblock 1
        h = conv_unit(h, "conv10", 256, k=3, s=1, p=1, act=act, test=test)
        h = conv_unit(h, "conv11", 256, k=3, s=1, p=1, act=act, test=test)
        h = conv_unit(h, "conv12", 256, k=3, s=1, p=1, act=act, test=test)
        h = F.max_pooling(h, (2, 2))  # 16 -> 8
        with nn.parameter_scope("bn1"):
            h = PF.batch_normalization(h, batch_stat=not test)
        if not test:
            h = F.dropout(h)

        # Convblock 2
        h = conv_unit(h, "conv20", 512, k=3, s=1, p=0, act=act, test=test)  # 8 -> 6
        h = conv_unit(h, "conv21", 256, k=1, s=1, p=0, act=act, test=test)
        h = conv_unit(h, "conv22", 128, k=1, s=1, p=0, act=act, test=test)
        h = conv_unit(h, "conv23", 10, k=1, s=1, p=0, act=act, test=test)

        # Convblock 3
        h = F.average_pooling(h, (6, 6))
        with nn.parameter_scope("bn2"):
            h = PF.batch_normalization(h, batch_stat=not test)
        h = F.reshape(h, (h.shape[0], np.prod(h.shape[1:])))
        return h
开发者ID:kzky，项目名称:works，代码行数:34，代码来源:cnn_model_005_001.py


示例2: mnist_lenet_prediction

def mnist_lenet_prediction(image, test=False):
    """
    Construct LeNet for MNIST.
    """
    image /= 255.0
    c1 = PF.convolution(image, 16, (5, 5), name='conv1')
    c1 = F.relu(F.max_pooling(c1, (2, 2)), inplace=True)
    c2 = PF.convolution(c1, 16, (5, 5), name='conv2')
    c2 = F.relu(F.max_pooling(c2, (2, 2)), inplace=True)
    c3 = F.relu(PF.affine(c2, 50, name='fc3'), inplace=True)
    c4 = PF.affine(c3, 10, name='fc4')
    return c4
开发者ID:zwsong，项目名称:nnabla，代码行数:12，代码来源:classification.py


示例3: cnn_model_003

def cnn_model_003(ctx, x, act=F.elu, do=True, test=False):
    with nn.context_scope(ctx):
        # Convblock0
        h = conv_unit(x, "conv00", 128, k=3, s=1, p=1, act=act, test=test)
        h = conv_unit(h, "conv01", 128, k=3, s=1, p=1, act=act, test=test)
        h = conv_unit(h, "conv02", 128, k=3, s=1, p=1, act=act, test=test)
        h = F.max_pooling(h, (2, 2))  # 32 -> 16
        with nn.parameter_scope("bn0"):
            h = PF.batch_normalization(h, batch_stat=not test)
        if not test and do:
            h = F.dropout(h)

        # Convblock 1
        h = conv_unit(h, "conv10", 256, k=3, s=1, p=1, act=act, test=test)
        h = conv_unit(h, "conv11", 256, k=3, s=1, p=1, act=act, test=test)
        h = conv_unit(h, "conv12", 256, k=3, s=1, p=1, act=act, test=test)
        h = F.max_pooling(h, (2, 2))  # 16 -> 8
        with nn.parameter_scope("bn1"):
            h = PF.batch_normalization(h, batch_stat=not test)
        if not test and do:
            h = F.dropout(h)

        # Convblock 2
        h = conv_unit(h, "conv20", 512, k=3, s=1, p=0, act=act, test=test)  # 8 -> 6
        h = conv_unit(h, "conv21", 256, k=1, s=1, p=0, act=act, test=test)
        h = conv_unit(h, "conv22", 128, k=1, s=1, p=0, act=act, test=test)
        h_branch = h

        # Convblock 3
        h = conv_unit(h_branch, "conv23", 10, k=1, s=1, p=0, act=act, test=test)
        h = F.average_pooling(h, (6, 6))
        with nn.parameter_scope("bn2"):
            h = PF.batch_normalization(h, batch_stat=not test)
        pred = F.reshape(h, (h.shape[0], np.prod(h.shape[1:])))

        # Uncertainty
        u0 = conv_unit(h_branch, "u0", 10, k=1, s=1, p=0, act=act, test=test)
        u0 = F.average_pooling(u0, (6, 6))
        with nn.parameter_scope("u0bn"):
            u0 = PF.batch_normalization(u0, batch_stat=not test)
            log_var = F.reshape(u0, (u0.shape[0], np.prod(u0.shape[1:])))

        # Uncertainty for uncertainty
        u1 = conv_unit(h_branch, "u1", 10, k=1, s=1, p=0, act=act, test=test)
        u1 = F.average_pooling(u1, (6, 6))
        with nn.parameter_scope("u1bn"):
            u1 = PF.batch_normalization(u1, batch_stat=not test)
            log_s = F.reshape(u1, (u1.shape[0], np.prod(u1.shape[1:])))

        return pred, log_var, log_s
开发者ID:kzky，项目名称:works，代码行数:50，代码来源:cnn_model_050.py


示例4: cnn_model_003

def cnn_model_003(ctx, h, act=F.elu, do=True, test=False):
    with nn.context_scope(ctx):
        if not test:
            b, c, s, s = h.shape
            h = F.image_augmentation(h, (c, s, s),
                                     min_scale=1.0, max_scale=1.5,
                                     angle=0.5, aspect_ratio=1.3, distortion=0.2,
                                     flip_lr=True)
        # Convblock0
        h = conv_unit(h, "conv00", 128, k=3, s=1, p=1, act=act, test=test)
        h = conv_unit(h, "conv01", 128, k=3, s=1, p=1, act=act, test=test)
        h = conv_unit(h, "conv02", 128, k=3, s=1, p=1, act=act, test=test)
        h = F.max_pooling(h, (2, 2))  # 32 -> 16
        with nn.parameter_scope("bn0"):
            h = PF.batch_normalization(h, batch_stat=not test)
        if not test and do:
            h = F.dropout(h)

        # Convblock 1
        h = conv_unit(h, "conv10", 256, k=3, s=1, p=1, act=act, test=test)
        h = conv_unit(h, "conv11", 256, k=3, s=1, p=1, act=act, test=test)
        h = conv_unit(h, "conv12", 256, k=3, s=1, p=1, act=act, test=test)
        h = F.max_pooling(h, (2, 2))  # 16 -> 8
        with nn.parameter_scope("bn1"):
            h = PF.batch_normalization(h, batch_stat=not test)
        if not test and do:
            h = F.dropout(h)

        # Convblock 2
        h = conv_unit(h, "conv20", 512, k=3, s=1, p=0, act=act, test=test)  # 8 -> 6
        h = conv_unit(h, "conv21", 256, k=1, s=1, p=0, act=act, test=test)
        h = conv_unit(h, "conv22", 128, k=1, s=1, p=0, act=act, test=test)
        u = h

        # Convblock 3
        h = conv_unit(h, "conv23", 10, k=1, s=1, p=0, act=act, test=test)
        h = F.average_pooling(h, (6, 6))
        with nn.parameter_scope("bn2"):
            h = PF.batch_normalization(h, batch_stat=not test)
        pred = F.reshape(h, (h.shape[0], np.prod(h.shape[1:])))

        # Uncertainty
        u = conv_unit(u, "u0", 10, k=1, s=1, p=0, act=act, test=test)
        u = F.average_pooling(u, (6, 6))
        with nn.parameter_scope("u0bn"):
            u = PF.batch_normalization(u, batch_stat=not test)
            log_var = F.reshape(u, (u.shape[0], np.prod(u.shape[1:])))

        return pred, log_var
开发者ID:kzky，项目名称:works，代码行数:49，代码来源:cnn_model_051.py


示例5: res_unit

    def res_unit(x, scope_name, dn=False, test=False):
        C = x.shape[1]
        with nn.parameter_scope(scope_name):

            # Conv -> BN -> Relu
            with nn.parameter_scope("conv1"):
                h = PF.convolution(x, C / 2, kernel=(1, 1), pad=(0, 0),
                                   with_bias=False)
                h = PF.batch_normalization(h, batch_stat=not test)
                h = F.relu(h)
            # Conv -> BN -> Relu
            with nn.parameter_scope("conv2"):
                h = PF.convolution(h, C / 2, kernel=(3, 3), pad=(1, 1),
                                   with_bias=False)
                h = PF.batch_normalization(h, batch_stat=not test)
                h = F.relu(h)
            # Conv -> BN
            with nn.parameter_scope("conv3"):
                h = PF.convolution(h, C, kernel=(1, 1), pad=(0, 0),
                                   with_bias=False)
                h = PF.batch_normalization(h, batch_stat=not test)
            # Residual -> Relu
            h = F.relu(h + x)

            # Maxpooling
            if dn:
                h = F.max_pooling(h, kernel=(2, 2), stride=(2, 2))
            return h
开发者ID:kzky，项目名称:works，代码行数:28，代码来源:cnn_model_060.py


示例6: mnist_resnet_prediction

def mnist_resnet_prediction(image, test=False):
    """
    Construct ResNet for MNIST.
    """
    image /= 255.0

    def bn(x):
        return PF.batch_normalization(x, batch_stat=not test)

    def res_unit(x, scope):
        C = x.shape[1]
        with nn.parameter_scope(scope):
            with nn.parameter_scope('conv1'):
                h = F.elu(bn(PF.convolution(x, C / 2, (1, 1), with_bias=False)))
            with nn.parameter_scope('conv2'):
                h = F.elu(
                    bn(PF.convolution(h, C / 2, (3, 3), pad=(1, 1), with_bias=False)))
            with nn.parameter_scope('conv3'):
                h = bn(PF.convolution(h, C, (1, 1), with_bias=False))
        return F.elu(F.add2(h, x, inplace=True))
    # Conv1 --> 64 x 32 x 32
    with nn.parameter_scope("conv1"):
        c1 = F.elu(
            bn(PF.convolution(image, 64, (3, 3), pad=(3, 3), with_bias=False)))
    # Conv2 --> 64 x 16 x 16
    c2 = F.max_pooling(res_unit(c1, "conv2"), (2, 2))
    # Conv3 --> 64 x 8 x 8
    c3 = F.max_pooling(res_unit(c2, "conv3"), (2, 2))
    # Conv4 --> 64 x 8 x 8
    c4 = res_unit(c3, "conv4")
    # Conv5 --> 64 x 4 x 4
    c5 = F.max_pooling(res_unit(c4, "conv5"), (2, 2))
    # Conv5 --> 64 x 4 x 4
    c6 = res_unit(c5, "conv6")
    pl = F.average_pooling(c6, (4, 4))
    with nn.parameter_scope("classifier"):
        y = PF.affine(pl, 10)
    return y
开发者ID:zwsong，项目名称:nnabla，代码行数:38，代码来源:classification.py


示例7: stochastic_res_unit

def stochastic_res_unit(x, scope_name, act=F.relu, dn=False, test=False):
    if not test:
        flag = np.random.randint(2)
        if flag:
            h = res_block(x, scope_name, act=act, dn=dn, test=test)
            h = F.add2(h, x)
        else:
            h = x
    else:
        h = res_block(x, scope_name, act=act, dn=dn, test=test)
        h = F.add2(h, x)
    h = act(h)
    # Maxpooling
    if dn:
        h = F.max_pooling(h, kernel=(2, 2), stride=(2, 2))
    return h
开发者ID:kzky，项目名称:works，代码行数:16，代码来源:cnn_model_024.py


示例8: cnn_model_003_with_cross_attention

def cnn_model_003_with_cross_attention(ctx, x_list, act=F.relu, test=False):
    """With attention before pooling
    """
    with nn.context_scope(ctx):
        # Convblock0
        h0_list = []
        for x in x_list:
            h = conv_unit(x, "conv00", 128, k=3, s=1, p=1, act=act, test=test)
            h = conv_unit(h, "conv01", 128, k=3, s=1, p=1, act=act, test=test)
            h = conv_unit(h, "conv02", 128, k=3, s=1, p=1, act=act, test=test)
            h0_list.append(h)

        # Corss attention
        ca0 = attention(h0_list[0], h0_list[1], h0_list[1], 
                        div_dim=True, softmax=True)
        ca1 = attention(h0_list[1], h0_list[0], h0_list[0], 
                        div_dim=True, softmax=True)

        # Maxpooing, Batchnorm, Dropout
        h0_list = []
        for h in [ca0, ca1]:
            h = F.max_pooling(h, (2, 2))  # 32 -> 16
            with nn.parameter_scope("bn0"):
                h = PF.batch_normalization(h, batch_stat=not test)
            if not test:
                h = F.dropout(h)
            h0_list.append(h)

        # Convblock 1
        h1_list = []
        for h in h0_list:
            h = conv_unit(h, "conv10", 256, k=3, s=1, p=1, act=act, test=test)
            h = conv_unit(h, "conv11", 256, k=3, s=1, p=1, act=act, test=test)
            h = conv_unit(h, "conv12", 256, k=3, s=1, p=1, act=act, test=test)
            h1_list.append(h)

        # Corss attention
        ca0 = attention(h1_list[0], h1_list[1], h1_list[1], 
                        div_dim=True, softmax=True)
        ca1 = attention(h1_list[1], h1_list[0], h1_list[0], 
                        div_dim=True, softmax=True)
            
        # Maxpooing, Batchnorm, Dropout
        h1_list = []
        for h in [ca0, ca1]:
            h = F.max_pooling(h, (2, 2))  # 16 -> 8
            with nn.parameter_scope("bn1"):
                h = PF.batch_normalization(h, batch_stat=not test)
            if not test:
                h = F.dropout(h)
                h1_list.append(h)

        # Convblock 2
        h2_list = []
        for h in h1_list:
            h = conv_unit(h, "conv20", 512, k=3, s=1, p=0, act=act, test=test)  # 8 -> 6
            h = conv_unit(h, "conv21", 256, k=1, s=1, p=0, act=act, test=test)
            h = conv_unit(h, "conv22", 128, k=1, s=1, p=0, act=act, test=test)
            h = conv_unit(h, "conv23", 10, k=1, s=1, p=0, act=act, test=test)
            h2_list.append(h)

        # Corss attention
        ca0 = attention(h2_list[0], h2_list[1], h2_list[1], 
                        div_dim=True, softmax=True)
        ca1 = attention(h2_list[1], h2_list[0], h2_list[0], 
                        div_dim=True, softmax=True)

        # Convblock 3
        h3_list = []
        for h in [ca0, ca1]:
            h = F.average_pooling(h, (6, 6))
            with nn.parameter_scope("bn2"):
                h = PF.batch_normalization(h, batch_stat=not test)
            h = F.reshape(h, (h.shape[0], np.prod(h.shape[1:])))
            h3_list.append(h)
        return h3_list
开发者ID:kzky，项目名称:works，代码行数:76，代码来源:cnn_model_025.py



注：本文中的nnabla.functions.max_pooling函数示例由纯净天空整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。

鲜花
握手
雷人
路过
鸡蛋
该文章已有0人参与评论

请发表评论

全部评论

专题导读
More+
上一篇：
Python functions.mean函数代码示例发布时间：2022-05-27
下一篇：
Python functions.exp函数代码示例发布时间：2022-05-27
热门推荐
More+
热门话题
More+
阅读排行榜

扫描微信二维码

查看手机版网站

随时了解更新最新资讯

139-2527-9053

在线客服（服务时间 9:00～18:00）

在线QQ客服
地址：深圳市南山区西丽大学城创智工业园
电邮：jeky_zhao#qq.com
移动电话：139-2527-9053

Powered by 互联科技 X3.4© 2001-2213 极客世界.|Sitemap