batch_num = int(mnist.train.num_examples / batch_size) # 跑多个batch; for i in range(batch_num): # 获取当前batch样本; x_batch, y_batch = mnist.train.next_batch(batch_size) # 定义{x: x_batch, y_: y_batch}这个train dict; train_dicts.update({x: x_batch, y_: y_batch}) # ...
x = repeat(tf.range(height), width) y = tf.tile(tf.range(width), tf.pack([height])) cind = tf.range(num_ch) nind = tf.expand_dims(repeat(nind, hwc),1) x = tf.tile(tf.expand_dims(repeat(x, num_ch),1), tf.pack([num_batch,1])) y = tf.tile(tf.expand_dims(repeat(y...
1、选择ModelA1作为训练网络(即resnet38),并使用对应的预训练数据,同时将全连接层转换为卷积层,学习率设置为0.01,batch_size为4,损失函数选用hanming loss,采用SGD优化,在AMD 2600X + GTX 1070Ti搭建的平台,训练了约30个小时。 2、选择Resnet50作为训练网络,同时将全连接层转换为卷积层,学习率设置为0.01,batch...
fork, colinzip(range(n_clusters), colors): my_members = k_means_labels == k cluster_center = k_means_cluster_centers[k] plt.plot(X[my_members,0], X[my_members,1],'w', markerfacecolor=col, marker='.') plt.plot(cluster_center[0...
()assess:sess.run(init)total_batch=int(mnist.train.num_examples/batch_size)# 训练数据 training_epochs为5组实验forepochinrange(training_epochs):# Loop over all batchesforiinrange(total_batch):batch_xs,batch_ys=mnist.train.next_batch(batch_size)#max(x)=1min(x)=0# 运行初始化和误差计算...
输入门再次采用连接的先前隐藏状态h[t-1]和当前序列输入x[t],并将其通过具有学习参数的 Sigmoid 函数,从而输出另一个矩阵i[t],它由 0 到 1 之间的值组成。连接的隐藏状态和序列输入也通过 tanh 函数,该函数将输出压缩在 -1 和 1 之间。 通过i[t]矩阵。 这意味着生成i[t]所需的学习参数可以有效地了解...
for n in range(opt_steps): # optimize pixel values for opt_steps times optimizer.zero_grad() self.model(img_var) loss = -activations.features[0, filter].mean() loss.backward() optimizer.step() img = val_tfms.denorm(img_var.data.cpu().numpy()[0].transpose(1,2,0)) ...
_num_examples += y_pred.shape[0] # target is (batch_size, ...) y_pred = torch.argmax(y_pred, dim=1).flatten() y = y.flatten() target_mask = (y >= 0) & (y < self.num_classes) y = y[target_mask] y_pred = y_pred[target_mask] indices = self.num_classes * y + ...
_batch_size(batch_size: int, local_rank: int, n_gpu: int, gradient_accumulation_steps: int = 1) -> int: eff_batch_size = float(batch_size) eff_batch_size /= gradient_accumulation_steps eff_batch_size /= get_effective_num_gpus(local_rank, n_gpu) return int(eff_batch_size) ...
batch_size = num_processes * num_steps# Make sure we have at least enough for a bunch of batches of size 1.assertbatch_size >= num_mini_batch mini_batch_size = batch_size // num_mini_batch sampler =BatchSampler(SubsetRandomSampler(range(batch_size)), mini_batch_size, drop_last=False...