zero_start, tf.range(num_replica), tf.sparse_to_dense(tf.range(num_replica -2), (num_replica,), tf.range(1, num_replica -1))) exchange_proposed_n = tf.where(zero_start, num_replica //2, num_replica //2-1)else: exchange_proposed = tf.where( zero_start, tf.range(num_replica...
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...
for i in range(epochs): for j in range(batch_num): batch_x,batch_y = mnist.train.next_batch(batch_size) sess.run(optimizer, feed_dict={x:batch_x,y:batch_y,keep_prob:0.75}) loss,acc = sess.run([cost,accuracy],feed_dict={x:batch_x,y:batch_y,keep_prob: 1.}) if epochs %...
# 需要导入模块: from config import config [as 别名]# 或者: from config.config importbatch_size[as 别名]defperturb(self, images, labels):batch_size= images.shape[0]ifbatch_size< FLAGS.batch_size: pad_num = FLAGS.batch_size-batch_sizepad_img = np.zeros([pad_num,299,299,3]) images ...
from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets( 'Minist_data',one_hot=True) #参数初始化 input_num = 784 # 输入的列数 labels = 10 #输出的列数 batchsize = 128 #训练集每一批次的照片
foriinrange(10): list_1 = np.array(np.arange(1,10000)) list_1 = np.sin(list_1) print("使用Numpy用时{}s".format(time.time()-start)) 从如下运行结果,可以看到使用Numpy库的速度快于纯 Python 编写的代码: 使用纯Python用时0.017444372177124...
keras=2.8.0 sklearn=1.0.2 导入数据 path = '' # define column names col_names=["unit_nb","time_cycle"]+["set_1","set_2","set_3"] + [f's_{i}' for i in range(1,22)] # read data df_train = train_data = pd.read_csv(path+"train_FD001.txt", index_col=False, ...
注意:虽然我使用第 40 层(卷积层)来生成我们当前正在查看的图像,但我使用了第 42 层来生成显示每个特征图的平均激活的图。第 41 和 42 层是 batch-norm 和 ReLU。ReLU 激活函数删除所有负值,选择第 42 层而不是 40 的唯一原因是,后者将显示大量负噪声,这使得我们很难看到我们感兴趣的正峰值。
_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) ...
_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 + ...