branch_6 = tf.expand_dims(input=branch_6, axis=1) branch_6 = tf.expand_dims(input=branch_6, axis=2) predict_6 = self.predict_6(branch_6) # predict_i shape : (batch_size, h, w, k * (c+4)), where c is self.num_classes. return [predict_1, predict_2, predict_3, predict...
修改num_classes为你想要识别的物体的类别数目。 num_classes: 1 1. 修改num_examples为你在\images\test路径下有的图片数目 eval_config: { num_examples: 173 # Note: The below line limits the evaluation process to 10 evaluations. # Remove the below line to evaluate indefinitely. max_evals: 8 } ...
def bulid_resNet(x, num_class, training=True, usBN=True): conv1 = conv_op(x, "conv1", 64, training, usBN, 3, 3, 1, 1) pool1 = max_pool_op(conv1, "pool1", kh=3, kw=3) block1_1 = res_block_layers(pool1, "block1_1", [64, 256], True, 1) block1_2 = res_b...
class ResNet(nn.Module): #初始化网络结构和参数 def __init__(self,block,layers,num_classes=1000): #self.inplane为当前的fm的通道数 self.inplane=64 super(ResNet,self).__init__() #参数 self.block=block self.layers=layers #stem的网络层 self.conv1=nn.Conv2d(3,self.inplane,kernel_size...
format(len(train_loader), len(eval_loader))) 训练集样本量: 4,验证集样本量: 1 3. 调用paddle.vision.models中的res50模型In [7] paddle.vision.set_image_backend('cv2') model = models.resnet50(pretrained=True,num_classes=config_parameters['class_dim']) paddle.summary(model, (16, 3, ...
分布式训练需要额外指定num_shard和shard_id两个参数 子网开发:训练子网和 loss 子网 将网络中不同模块或子模块作为一个个子网抽离出来单独开发,这样可以保证各个子网并行开发,互相不受干扰。 分析ResNet50 网络代码,主要可以分成以下几个子网: conv1x1、conv3x3:定义了不同 kernel_size 的卷积。
In [1] !unzip -q -o data/data195256/RAF-DB.zip #解压数据集 In [2] #各参数定义,包含优化器、学习率、标签、模型保存路径等 text = '''__all__ = ['CONFIG', 'get'] CONFIG = { 'model_save_dir': "./output/ResNet50", 'num_classes': 7, 'total_images': 15339, 'epochs': 50...
predictions = Dense(train_generator.num_classes, activation=’softmax’)(x) 构建模型 model = Model(inputs=base_model.input, outputs=predictions) 冻结预训练模型的权重 for layer in base_model.layers: layer.trainable = False 编译模型 model.compile(optimizer=Adam(lr=0.0001), loss=’categorical_...
class ResNet50(nn.Module):def __init__(self, num_classes=1000):# ... 前几层代码 ...# 4个残差块的block1self.layer1 = self._make_layer(ResidualBlock, 64, 3, stride=1)# 4个残差块的block2self.layer2 = self._make_layer(ResidualBlock, 128, 4, stride=2)# 4个残差块的block3self...
def __init__(self, transforms, num_classes=4, mode='train'): # 数据集存放位置 self.dataset_dir = "./work/peach-classification" #dataset_dir为数据集实际路径,需要填写全路径 self.transforms = transforms self.num_classes = num_classes ...