max_ = tf.reduce_max(input, axis=1) # Pytorch语法: max_ = torch.max(input, dim=1) 1. 2. 3. 4. 返回给定张量的矩阵范数或向量范数 AI检测代码解析 # Tensorflow语法: out = tf.norm(input, ord=1, axis=1) # Pytorch语法: out = torch.norm(input, p=1, dim=1) 1. 2. 3. 4. ...
tf.reduce_max( input_tensor, axis=None, name=None, ) 函数是求按axis方向的最值,axis=0按列求最值,axis=1按行求最值。其中有时候会看到参数没有axis,而是reduction_indices=None,其实两者相等,不过后者是axis废弃的名称。 另外该函数等价于np.max:(a, axis=None, out=None, keepdims=False) 代码语言:...
问tf.math.reduce_max允许像torch.max那样的梯度流吗？EN欧洲机器人研究团队开发了一种带有“人工迷你大...
@tf.function def train_step(dist_inputs): def step_fn(inputs): features, labels = inputs with tf.GradientTape() as tape: logits = model(features) cross_entropy = tf.nn.softmax_cross_entropy_with_logits( logits=logits, labels=labels) loss = tf.reduce_sum(cross_entropy) * (1.0 / gl...
tensorflow用tf.keras.datasets.mnist.load_data()加载数据,numpy.ndarray格式;pytorch使用torchvison.datasets.MNIST加载的数据集,数据格式为image(无法直接使用,需要设置transform = transforms.ToTensor()转换成tensor张量数据),transform.Compose()还能通过list传参进行图片转换、正则化等操作。
tf.reduce_sum() reduce_sum( ) 是求和函数,在 tensorflow 里面,计算的都是 tensor,可以通过调整 axis =0,1 的维度来控制求和维度。 reduce_sum( ) 是求和函数,在 tensorflow 里面,计算的都是 tensor,可以通过调整 axis =0,1 的维度来控制求和维度。
Reduce compute time and production costs. Dozens of model architectures with 1M+ pretrained checkpoints across all modalities. Choose the right framework for every part of a models lifetime: Train state-of-the-art models in 3 lines of code. Move a single model between PyTorch/JAX/TF2.0 framew...
max(torch.where(row)[0]) for row in mask] else: raise ValueError(f"Unsupported backend: {keras.backend.backend()}") # Find position of last 'True' in each row. seq_ends: Sequence[int] = [ 1 + tf.reduce_max(tf.where(mask[i])).numpy().tolist() for i in range(mask.shape[0...
returntf.reduce_sum(FL)defmulti_class_focal_loss(label,logits,alpha,gamma):# label:[b,h,w,c] logits:[b,h,w,c] alpha:list[c] gamma:floatepsilon=1.e-7pred=tf.sigmoid(logits)pred=tf.clip_by_value(pred,epsilon,1-epsilon)alpha=tf.constant(alpha,dtype=tf.float32)index=tf.argmax(...
记录了将tensorflow转pytorch时,一些常用的函数转换: 不能直接转换tf.transpose(input,[1, 0, 2]) -> input.permute([1, 0, 2]) 不能直接换成torch.transpose,因为操作不了多维tf.expand_dims(input), axi…