global max pooling

时间: 2023-04-25 07:06:20 浏览: 100
全局最大池化（global max pooling）是一种池化操作，它将输入张量的每个通道的最大值提取出来，形成一个新的张量。这个新的张量的形状是（batch_size, channels），其中batch_size是输入张量的批次大小，channels是输入张量的通道数。全局最大池化通常用于卷积神经网络的最后一层，将卷积层的输出转换为固定大小的向量，用于分类或回归任务。
相关问题

global average pooling是什么

Global average pooling是一种池化方法，用于将卷积神经网络的特征图降维成一个特征向量。它的操作是对每个特征图做平均，将每个通道的输出值取平均后得到一个标量，这样就可以将特征图降为一个向量。与传统的max pooling不同，global average pooling更注重特征的平均值，而不是最大值，因此可以更好地保留特征的整体信息。它在图像分类、目标检测和图像分割等任务中都有广泛应用。

Traceback (most recent call last): File "training.py", line 96, in <module> train(model, device, train_loader, optimizer, epoch+1) File "training.py", line 28, in train output = model(data) File "/home/qcui/anaconda3/envs/geometric/lib/python3.8/site-packages/torch/nn/modules/module.py", line 532, in __call__ result = self.forward(*input, **kwargs) File "/data/qcui/DTA/GraphDTA/ecfp_maccs_graph/CPI_prediction/models/gcn.py", line 64, in forward x = gmp(x, batch) # global max pooling File "/home/qcui/anaconda3/envs/geometric/lib/python3.8/site-packages/torch_geometric/nn/glob/glob.py", line 76, in global_max_pool return scatter(x, batch, dim=0, dim_size=size, reduce='max') File "/home/qcui/anaconda3/envs/geometric/lib/python3.8/site-packages/torch_scatter/scatter.py", line 155, in scatter return scatter_max(src, index, dim, out, dim_size)[0] RuntimeError: expected scalar type Long but found Float The above operation failed in interpreter. Traceback (most recent call last): File "/home/qcui/anaconda3/envs/geometric/lib/python3.8/site-packages/torch_scatter/scatter.py", line 69 dim_size: Optional[int] = None ) -> Tuple[torch.Tensor, torch.Tensor]: return torch.ops.torch_scatter.scatter_max(src, index, dim, out, dim_size) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE

这是一个Python程序的错误信息，其中包含了一些文件名和行号。具体来说，这个错误信息显示了在训练模型时出现了错误，错误发生在第96行，train函数中的第28行调用了模型，但是模型的输入出现了问题，导致程序无法继续执行。具体的错误信息是在gcn.py文件的第64行，模型在进行计算时调用了gmp函数，但是输入的数据和batch信息有问题。

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max pooling with dropout

在matlab上实现了具备max pooling和dropout功能的convolutional neural network，并附带MNIST数据集，可以直接运行测试。 时间有限，仅有1层convolution和1层pooling。
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pool_config_multi.zip_SUM_max pooling_pooling

sum pooling, max pooling, average pooling,三种 Feature Coding pooling方法
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MaxPooling 最大池化简单实现

def max_pooling(feature_map, size=2, stride=2): """max_pooling""" channel = feature_map.shape[0] height = feature_map.shape[1] width = feature_map.shape[2] # 确定最后的输出形状 out_height = np....

AVGPooling

平均池化（Average Pooling）是一种计算图像区域平均值作为该区域池化后的值的操作。它保留了整体数据的特征，并能突出背景的信息。在平均池化中，每个区域的激活贡献相等，这可以显著降低整体区域特征的强度。全局平均池化（Global Average Pooling，GAP）是一种特殊的平均池化，它对整个特征图进行平均池化操作。\[1\] 在GPU上计算平均池化时，由于有大量的计算单元，使用队列反而会更低效。因此，常见的做法是对于每一个n,c维度上的池化单元，都使用单独的一个线程去负责实现。这样可以充分利用GPU上的计算资源。\[2\] 在平均池化中，权重与相应的激活值一起用作非线性变换。较高的激活比较低的激活占更多的主导地位。这是因为大多数池化操作都是在高维的特征空间中执行的，突出显示具有更大效果的激活比简单地选择最大值是一种更平衡的方法。具体的步骤是计算权重Wi，其中Wi是所有邻域内激活值加权求和的结果。然后，通过将权重与输入进行元素相乘，并进行平均池化操作，得到最终的池化结果。\[3\] 下面是一个示例代码，展示了如何实现平均池化操作： python def soft_pool2d(x, kernel_size=2, stride=None, force_inplace=False): if x.is_cuda and not force_inplace: return CUDA_SOFTPOOL2d.apply(x, kernel_size, stride) kernel_size = _pair(kernel_size) if stride is None: stride = kernel_size else: stride = _pair(stride) # 获取输入的大小 _, c, h, w = x.size() # 创建每个元素的指数值和：Tensor \[b x 1 x h x w\] e_x = torch.sum(torch.exp(x), dim=1, keepdim=True) # 对输入应用掩码并进行池化，并计算指数和 # Tensor: \[b x c x h x w\] -> \[b x c x h' x w'\] return F.avg_pool2d(x.mul(e_x), kernel_size, stride=stride).mul_(sum(kernel_size)).div_(F.avg_pool2d(e_x, kernel_size, stride=stride).mul_(sum(kernel_size))) 这段代码展示了如何使用PyTorch实现平均池化操作。它首先计算每个元素的指数和，然后将输入与指数和进行元素相乘，并进行平均池化操作，最后得到最终的池化结果。 #### 引用[.reference_title] - *1* *3* [池化操作average pooling、max pooling、SoftPool、Spatial Pyramid Pooling（SPP）](https://blog.csdn.net/weixin_42764932/article/details/112515715)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insertT0,239^v3^insert_chatgpt"}} ] [.reference_item] - *2* [关于maxpooling和avgpooling](https://blog.csdn.net/digitalbiscuitz/article/details/98481405)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insertT0,239^v3^insert_chatgpt"}} ] [.reference_item] [ .reference_list ]

输入为（none，20），none为时间步/样本数，20为特征数，下列代码参数怎么修改batch_size = 64 input_2 = keras.Input(shape=(batch_size, 20)) x = Conv1D(filters=16, kernel_size=12, strides=4, padding='causal')(input_2) x = MaxPooling1D(4)(x) x = tf.keras.layers.Dropout(0.4)(x) x = tcnBlock(x, 16, 3, 1) x = tcnBlock(x, 8, 3, 2) x = tcnBlock(x, 4, 3, 4) x = GlobalAveragePooling1D()(x) x = LayerNormalization()(x) output_2 = keras.layers.Dense(1, activation='sigmoid')(x) model2 = keras.Model(inputs=input_2, outputs=output_2) model2.summary()

如果想要修改batch_size为128，需要将input_2的shape修改为(shape=(128, 20))。修改后的代码如下： python batch_size = 128 input_2 = keras.Input(shape=(batch_size, 20)) x = Conv1D(filters=16, kernel_size=12, strides=4, padding='causal')(input_2) x = MaxPooling1D(4)(x) x = tf.keras.layers.Dropout(0.4)(x) x = tcnBlock(x, 16, 3, 1) x = tcnBlock(x, 8, 3, 2) x = tcnBlock(x, 4, 3, 4) x = GlobalAveragePooling1D()(x) x = LayerNormalization()(x) output_2 = keras.layers.Dense(1, activation='sigmoid')(x) model2 = keras.Model(inputs=input_2, outputs=output_2) model2.summary()

S_inputs = Input(shape=(11,), dtype='int32') #(None,600) O_seq = Embedding(5000, 128)(S_inputs) #(None,600,128) cnn1 = Conv1D(256, 3, padding='same', strides=1, activation='relu')(O_seq) cnn1 = MaxPooling1D(pool_size=3)(cnn1) cnn = cnn1 O_seq = GlobalAveragePooling1D()(cnn) #(None,128) print(O_seq.shape) O_seq = Dropout(0.9)(O_seq) outputs = Dense(1, activation='tanh',kernel_regularizer = tf.keras.regularizers.L2())(O_seq) model = Model(inputs=S_inputs, outputs=outputs) opt = SGD(learning_rate=0.1, decay=0.00001) loss = 'categorical_crossentropy' model.compile(loss=loss, optimizer=opt, metrics=['categorical_accuracy']) print('Train...') h = model.fit(Xtrain, ytrain,batch_size=batch_size,validation_split = 0.2,epochs=5) plt.plot(h.history["loss"], label="train_loss") plt.plot(h.history["val_loss"], label="test_loss") plt.legend() plt.show()给这段代码加注释

# 导入模块 from tensorflow.keras.layers import Input, Embedding, Conv1D, MaxPooling1D, GlobalAveragePooling1D, Dropout, Dense from tensorflow.keras.models import Model from tensorflow.keras.optimizers import SGD import tensorflow as tf import matplotlib.pyplot as plt # 定义输入层 S_inputs = Input(shape=(11,), dtype='int32') #(None,600) # 创建嵌入层 O_seq = Embedding(5000, 128)(S_inputs) #(None,600,128) # 创建卷积层并进行池化操作 cnn1 = Conv1D(256, 3, padding='same', strides=1, activation='relu')(O_seq) cnn1 = MaxPooling1D(pool_size=3)(cnn1) cnn = cnn1 # 全局平均池化 O_seq = GlobalAveragePooling1D()(cnn) #(None,128) # 添加 dropout 层 O_seq = Dropout(0.9)(O_seq) # 创建输出层 outputs = Dense(1, activation='tanh',kernel_regularizer = tf.keras.regularizers.L2())(O_seq) # 定义模型并进行编译 model = Model(inputs=S_inputs, outputs=outputs) opt = SGD(learning_rate=0.1, decay=0.00001) loss = 'categorical_crossentropy' model.compile(loss=loss, optimizer=opt, metrics=['categorical_accuracy']) # 输出模型结构 model.summary() # 训练模型 print('Train...') h = model.fit(Xtrain, ytrain,batch_size=batch_size,validation_split = 0.2,epochs=5) # 绘制损失函数曲线 plt.plot(h.history["loss"], label="train_loss") plt.plot(h.history["val_loss"], label="test_loss") plt.legend() plt.show()

请根据一下内容完善一下代码：使用 6 个卷积层，卷积层的 kernel size=3、没经过两个卷积层，特征的 空间分辨率降低两倍，特征的 channel 数量提升两倍；每个卷积层后跟一个 Batch Normalization、一个 ReLU 层；在最后的输出层，使用 Global Average Pooling 操作，把特征层转换为 一个特征向量；使用全连接神经网络完成最终的预测；

以下是使用 PyTorch 实现该卷积神经网络的代码： python import torch import torch.nn as nn class ConvNet(nn.Module): def __init__(self, num_classes=10): super(ConvNet, self).__init__() self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1) self.bn1 = nn.BatchNorm2d(64) self.relu1 = nn.ReLU(inplace=True) self.conv2 = nn.Conv2d(64, 64, kernel_size=3, padding=1) self.bn2 = nn.BatchNorm2d(64) self.relu2 = nn.ReLU(inplace=True) self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2) self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1) self.bn3 = nn.BatchNorm2d(128) self.relu3 = nn.ReLU(inplace=True) self.conv4 = nn.Conv2d(128, 128, kernel_size=3, padding=1) self.bn4 = nn.BatchNorm2d(128) self.relu4 = nn.ReLU(inplace=True) self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2) self.conv5 = nn.Conv2d(128, 256, kernel_size=3, padding=1) self.bn5 = nn.BatchNorm2d(256) self.relu5 = nn.ReLU(inplace=True) self.conv6 = nn.Conv2d(256, 256, kernel_size=3, padding=1) self.bn6 = nn.BatchNorm2d(256) self.relu6 = nn.ReLU(inplace=True) self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(256, num_classes) def forward(self, x): x = self.conv1(x) x = self.bn1(x) x = self.relu1(x) x = self.conv2(x) x = self.bn2(x) x = self.relu2(x) x = self.pool1(x) x = self.conv3(x) x = self.bn3(x) x = self.relu3(x) x = self.conv4(x) x = self.bn4(x) x = self.relu4(x) x = self.pool2(x) x = self.conv5(x) x = self.bn5(x) x = self.relu5(x) x = self.conv6(x) x = self.bn6(x) x = self.relu6(x) x = self.pool3(x) x = self.avgpool(x) x = x.view(x.size(0), -1) x = self.fc(x) return x 该网络实现了6个卷积层，每个卷积层后跟一个 Batch Normalization 和一个 ReLU 层，经过两个卷积层后，特征的空间分辨率降低两倍，特征的 channel 数量提升两倍。在最后的输出层，使用 Global Average Pooling 操作，把特征层转换为一个特征向量，然后使用全连接神经网络完成最终的预测。

def MEAN_Spot(opt): # channel 1 inputs1 = layers.Input(shape=(42,42,1)) conv1 = layers.Conv2D(3, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs1) bn1 = layers.BatchNormalization()(conv1) pool1 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3,3))(bn1) do1 = layers.Dropout(0.3)(pool1) # channel 2 inputs2 = layers.Input(shape=(42,42,1)) conv2 = layers.Conv2D(3, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs2) bn2 = layers.BatchNormalization()(conv2) pool2 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3,3))(bn2) do2 = layers.Dropout(0.3)(pool2) # channel 3 inputs3 = layers.Input(shape=(42,42,1)) conv3 = layers.Conv2D(8, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs3) bn3 = layers.BatchNormalization()(conv3) pool3 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3,3))(bn3) do3 = layers.Dropout(0.3)(pool3) # merge 1 merged = layers.Concatenate()([do1, do2, do3]) # interpretation 1 merged_conv = layers.Conv2D(8, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.1))(merged) merged_pool = layers.MaxPooling2D(pool_size=(2, 2), padding='same', strides=(2,2))(merged_conv) flat = layers.Flatten()(merged_pool) flat_do = layers.Dropout(0.2)(flat) # outputs outputs = layers.Dense(1, activation='linear', name='spot')(flat_do) #Takes input u, v, os model = keras.models.Model(inputs=[inputs1, inputs2, inputs3], outputs=[outputs]) model.compile( loss={'spot':'mse'}, optimizer=opt, metrics={'spot':tf.keras.metrics.MeanAbsoluteError()}, ) return model 这段代码哪里可以加注意力机制

可以在merged_conv之后添加注意力机制，如下所示： python import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers def attention(inputs, filters): x = layers.GlobalAveragePooling2D()(inputs) x = layers.Dense(filters // 8, activation='relu')(x) x = layers.Dense(filters, activation='sigmoid')(x) x = layers.Reshape((1, 1, filters))(x) x = layers.Multiply()([inputs, x]) return x def MEAN_Spot(opt): # channel 1 inputs1 = layers.Input(shape=(42,42,1)) conv1 = layers.Conv2D(3, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs1) bn1 = layers.BatchNormalization()(conv1) pool1 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3,3))(bn1) do1 = layers.Dropout(0.3)(pool1) # channel 2 inputs2 = layers.Input(shape=(42,42,1)) conv2 = layers.Conv2D(3, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs2) bn2 = layers.BatchNormalization()(conv2) pool2 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3,3))(bn2) do2 = layers.Dropout(0.3)(pool2) # channel 3 inputs3 = layers.Input(shape=(42,42,1)) conv3 = layers.Conv2D(8, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs3) bn3 = layers.BatchNormalization()(conv3) pool3 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3,3))(bn3) do3 = layers.Dropout(0.3)(pool3) # merge 1 merged = layers.Concatenate()([do1, do2, do3]) # attention attn = attention(merged, filters=8) # interpretation 1 merged_conv = layers.Conv2D(8, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.1))(attn) merged_pool = layers.MaxPooling2D(pool_size=(2, 2), padding='same', strides=(2,2))(merged_conv) flat = layers.Flatten()(merged_pool) flat_do = layers.Dropout(0.2)(flat) # outputs outputs = layers.Dense(1, activation='linear', name='spot')(flat_do) # Takes input u, v, os model = keras.models.Model(inputs=[inputs1, inputs2, inputs3], outputs=[outputs]) model.compile( loss={'spot':'mse'}, optimizer=opt, metrics={'spot':tf.keras.metrics.MeanAbsoluteError()}, ) return model 以上代码将注意力机制添加在了merged_conv之后，通过attention函数实现。注意力机制可以增强模型对于重要特征的关注，提高模型性能。

解析这段代码from keras.models import Sequential from keras.layers import Dense, Conv2D, Flatten, MaxPooling2D, Dropout, Activation, BatchNormalization from keras import backend as K from keras import optimizers, regularizers, Model from keras.applications import vgg19, densenet def generate_trashnet_model(input_shape, num_classes): # create model model = Sequential() # add model layers model.add(Conv2D(96, kernel_size=11, strides=4, activation='relu', input_shape=input_shape)) model.add(MaxPooling2D(pool_size=3, strides=2)) model.add(Conv2D(256, kernel_size=5, strides=1, activation='relu')) model.add(MaxPooling2D(pool_size=3, strides=2)) model.add(Conv2D(384, kernel_size=3, strides=1, activation='relu')) model.add(Conv2D(384, kernel_size=3, strides=1, activation='relu')) model.add(Conv2D(256, kernel_size=3, strides=1, activation='relu')) model.add(MaxPooling2D(pool_size=3, strides=2)) model.add(Flatten()) model.add(Dropout(0.5)) model.add(Dense(4096)) model.add(Activation(lambda x: K.relu(x, alpha=1e-3))) model.add(Dropout(0.5)) model.add(Dense(4096)) model.add(Activation(lambda x: K.relu(x, alpha=1e-3))) model.add(Dense(num_classes, activation="softmax")) # compile model using accuracy to measure model performance model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) return model # Generate model using a pretrained architecture substituting the fully connected layer def generate_transfer_model(input_shape, num_classes): # imports the pretrained model and discards the fc layer base_model = densenet.DenseNet121( include_top=False, weights='imagenet', input_tensor=None, input_shape=input_shape, pooling='max') #using max global pooling, no flatten required x = base_model.output #x = Dense(256, activation="relu")(x) x = Dense(256, activation="relu", kernel_regularizer=regularizers.l2(0.01))(x) x = Dropout(0.6)(x) x = BatchNormalization()(x) predictions = Dense(num_classes, activation="softmax")(x) # this is the model we will train model = Model(inputs=base_model.input, outputs=predictions) # compile model using accuracy to measure model performance and adam optimizer optimizer = optimizers.Adam(lr=0.001) #optimizer = optimizers.SGD(lr=0.0001, momentum=0.9, nesterov=True) model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy']) return model

这段代码使用Keras框架定义了两个函数：generate_trashnet_model和generate_transfer_model，用于生成垃圾分类模型。其中： - generate_trashnet_model函数定义了一个序列模型，该模型包含多个卷积层和池化层，以及两个全连接层。最后使用softmax激活函数输出预测结果。该函数接收输入数据的形状和分类数目，返回生成的模型。 - generate_transfer_model函数定义了一个迁移学习模型，该模型使用预训练的DenseNet121模型作为基础模型，去掉最后的全连接层，然后添加一个全连接层和一个分类层。该函数接收输入数据的形状和分类数目，返回生成的模型。 这两个函数都使用了Adam优化器、交叉熵损失函数和准确率作为模型评估指标。generate_transfer_model还使用了正则化技术和批量归一化技术来提高模型的泛化能力。

def conv_block(inputs, filters): x = layers.BatchNormalization()(inputs) x = layers.Activation('relu')(x) x = layers.Conv2D(filters, 1, padding='same')(x) x = layers.BatchNormalization()(x) x = layers.Activation('relu')(x) x = layers.Conv2D(filters, 3, padding='same')(x) x = layers.Conv2D(filters, 1, padding='same')(x) return x def dense_block(inputs, filters, n_layers): x = inputs for i in range(n_layers): conv = conv_block(x, filters) x = layers.Concatenate()([x, conv]) return x def transition_block(inputs, compression): filters = int(inputs.shape[-1] * compression) x = layers.BatchNormalization()(inputs) x = layers.Activation('relu')(x) x = layers.Conv2D(filters, 1, padding='same')(x) x = layers.AveragePooling2D(2)(x) return x def Inception_block(inputs, filters): x1 = layers.Conv2D(filters, 1, padding='same', activation='relu')(inputs) x2 = layers.Conv2D(filters, 1, padding='same', activation='relu')(inputs) x2 = layers.Conv2D(filters, 3, padding='same', activation='relu')(x2) x3 = layers.Conv2D(filters, 1, padding='same', activation='relu')(inputs) x3 = layers.Conv2D(filters, 5, padding='same', activation='relu')(x3) x4 = layers.MaxPooling2D(3, strides=1, padding='same')(inputs) x4 = layers.Conv2D(filters, 1, padding='same', activation='relu')(x4) x = layers.Concatenate()([x1, x2, x3, x4]) return x inputs = keras.Input(shape=(224, 224, 3)) x = layers.Conv2D(64, 7, strides=2, padding='same')(inputs) x = layers.BatchNormalization()(x) x = layers.Activation('relu')(x) x = layers.MaxPooling2D(3, strides=2, padding='same')(x) x = dense_block(x, 32, 6) x = transition_block(x, 0.5) x = Inception_block(x, 64) x = dense_block(x, 32, 12) x = transition_block(x, 0.5) x = Inception_block(x, 128) x = dense_block(x, 32, 48) x = transition_block(x, 0.5) x = Inception_block(x, 256) x = layers.GlobalAveragePooling2D()(x) outputs = layers.Dense(10, activation='softmax')(x) model = keras.Model(inputs, outputs)这串代码有问题

这段代码并没有明显的语法错误，但是其中使用的函数和模块并未导入。需要在代码文件开头导入所需的模块和函数，例如： from tensorflow import keras from tensorflow.keras import layers def conv_block(inputs, filters): x = layers.BatchNormalization()(inputs) x = layers.Activation('relu')(x) x = layers.Conv2D(filters, 1, padding='same')(x) x = layers.BatchNormalization()(x) x = layers.Activation('relu')(x) x = layers.Conv2D(filters, 3, padding='same')(x) x = layers.Conv2D(filters, 1, padding='same')(x) return x def dense_block(inputs, filters, n_layers): x = inputs for i in range(n_layers): conv = conv_block(x, filters) x = layers.Concatenate()([x, conv]) return x def transition_block(inputs, compression): filters = int(inputs.shape[-1] * compression) x = layers.BatchNormalization()(inputs) x = layers.Activation('relu')(x) x = layers.Conv2D(filters, 1, padding='same')(x) x = layers.AveragePooling2D(2)(x) return x def Inception_block(inputs, filters): x1 = layers.Conv2D(filters, 1, padding='same', activation='relu')(inputs) x2 = layers.Conv2D(filters, 1, padding='same', activation='relu')(inputs) x2 = layers.Conv2D(filters, 3, padding='same', activation='relu')(x2) x3 = layers.Conv2D(filters, 1, padding='same', activation='relu')(inputs) x3 = layers.Conv2D(filters, 5, padding='same', activation='relu')(x3) x4 = layers.MaxPooling2D(3, strides=1, padding='same')(inputs) x4 = layers.Conv2D(filters, 1, padding='same', activation='relu')(x4) x = layers.Concatenate()([x1, x2, x3, x4]) return x inputs = keras.Input(shape=(224, 224, 3)) x = layers.Conv2D(64, 7, strides=2, padding='same')(inputs) x = layers.BatchNormalization()(x) x = layers.Activation('relu')(x) x = layers.MaxPooling2D(3, strides=2, padding='same')(x) x = dense_block(x, 32, 6) x = transition_block(x, 0.5) x = Inception_block(x, 64) x = dense_block(x, 32, 12) x = transition_block(x, 0.5) x = Inception_block(x, 128) x = dense_block(x, 32, 48) x = transition_block(x, 0.5) x = Inception_block(x, 256) x = layers.GlobalAveragePooling2D()(x) outputs = layers.Dense(10, activation='softmax')(x) model = keras.Model(inputs, outputs)
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