Correct normalization values for CIFAR-10: (0.4914, 0.4822, 0.4465), (0.247, 0.243, 0.261)
# 训练数据集中样本最少的类别中的样本数 n = collections.Counter(labels.values()).most_common()[-1][1] # 验证集中每个类别的样本数 n_valid_per_label = max(1, math.floor(n * valid_ratio)) label_count = {} for train_file in os.listdir(os.path.join(data_dir, 'train')): label =...
4、优化器 #Define the optimizeroptimizer = tf.train.MomentumOptimizer(learning_rate, momentum=0.9)#Relate to the batch normalizationupdate_ops =tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(update_ops): opt_op= optimizer.minimize(loss, global_step)...
pytorch的矩阵被称为(更准确说是被封装为)张量(tensor), 其实就是换了个名字, 你可以理解区别就是它可以被放在GPU运算. 首先是flatten函数. importtorch# 核心moduledefflatten(x): N = x.shape[0]# read in N, C, H, Wreturnx.view(N, -1)# "flatten" the C * H * W values into a single v...
一、批量归一化(BatchNormalization) 1. 对输入的标准化(浅层模型):处理后的任意一个特征在数据集中所有样本上的均值为0、标准差为1。 标准化处理输入数据使各个特征的分布相近...函数之间。 2.2 对卷积层做批量归一化位置:卷积计算之后、应⽤激活函数之前。 如果卷积计算输出多个通道,我们需要对这些通道的输出...
TODO(gpapan): The batch-normalization related default values above are appropriate for use in conjunction with the reference ResNet models released at https://github.com/KaimingHe/deep-residual-networks. When training ResNets from scratch, they might need to be tuned. Args: weight_decay: The ...
设置电脑上保存数据集的路径。 # cifar10.data_path = "data/CIFAR-10/" CIFAR-10数据集大概有163MB,如果给定路径没有找到文件的话,将会自动下载。 cifar10.maybe_download_and_extract() Data has apparently already been downloaded and unpacked.
–BatchNormalization(): Add a batch normalization layer. –Conv2D(32, (3, 3), activation=’relu’, padding=’same’): Add another 2D convolution layer with the same specifications as above. –BatchNormalization(): Add another batch normalization layer. ...
# Local Response Normalization (parameters from paper) norm2 = tf.nn.lrn(pool2, depth_radius=5, bias=2.0, alpha=1e-3, beta=0.75, name='norm2') # Reshape output into a single matrix for multiplication for the fully connected layers ...
def lrn(x, radius, alpha, beta, name, bias=1.0): """Create a local response normalization layer.""" return tf.nn.local_response_normalization(x, depth_radius=radius, alpha=alpha, beta=beta, bias=bias, name=name) def dropout(x, rate): """Create a dropout layer.""" return tf.nn....