TruncatedNormalclass - a wrapper with extralocandscaleparameters of the parent Normal distribution; Differentiability wrt parameters of the distribution; Batching support. Why I just needed differentiation with respect to parameters of the distribution and found out that truncated normal distribution is not...
mnist = input_data.read_data_sets("MNIST_data/",one_hot=True) #每个批次的大小 batch_size = 100 #计算一共 有多少个批次 n_batch = mnist.train.num_examples//batch_size #初始化权值 def weight_variable(shape): initial = tf.truncated_normal(shape,stddev = 0.1) #生成一个截断的正态分布 r...
[num_labels, hidden_size], initializer=tf.truncated_normal_initializer(stddev=0.02)) output_bias = tf.get_variable( "output_bias", [num_labels], initializer=tf.zeros_initializer()) logits = tf.matmul(output_layer, output_weights, transpose_b=True) logits = tf.nn.bias_add(logits, mtt_...
1), padding='valid', kernel_initializer=tf.truncated_normal_initializer) # => (142, 142, 32) batch_norm_1 = tf.nn.relu(tf.layers.batch_normalization(conv_1, training=is_training)) # (142, 142, 32) conv_2 = tf.layers.conv2d(batch_norm_1, 64, (3, 3), strides=(1, 1), pad...
# 需要导入模块: import torch [as 别名]# 或者: from torch importload[as 别名]definit_truncated_normal(model, aux_str=''):ifmodelisNone:returnNoneinit_path ='{path}/{in_dim:d}_{out_dim:d}{aux_str}.pth'\ .format(path=path, in_dim=model.in_features, out_dim=model.out_features,...
ModuleList): [truncated_normal(sub) for sub in model] else: truncated_normal(model) print('generate init weight: {init_path}'.format(init_path=init_path)) torch.save(model.state_dict(), init_path) print('save init weight: {init_path}'.format(init_path=init_path)) return model ...
ModuleList): [truncated_normal(sub) for sub in model] else: truncated_normal(model) print('generate init weight: {init_path}'.format(init_path=init_path)) torch.save(model.state_dict(), init_path) print('save init weight: {init_path}'.format(init_path=init_path)) return model ...
trunc_normalw = torch.empty(3, 5) torch_init.trunc_normal_(w) 复制tensor([[ 0.0626, 0.5258, 0.7458, -0.1970, 1.2821], [ 0.2344, 0.5350, 0.4402, 0.2152, 1.0044], [ 0.3880, -0.1332, 0.6127, 0.9253, 1.5593]]) 复制w = ms_init.initializer(ms_init.TruncatedNormal(1), (3,5)) print...
Expand All @@ -25,12 +26,14 @@ def _no_grad_trunc_normal_(tensor, mean, std, a, b, generator=None): # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf def norm_cdf(x): # Computes standard normal cumulative distribution function return (1. +...
step(action) episode_reward += reward if terminated or truncated: episode_rewards.append(episode_reward) break # 更新收集的数据到重放缓冲区 buffer.extend(observations) # 从重放缓冲区中采样数据用于训练 for _ in range(100): batch = buffer.sample(batch_size) loss = ppo_loss(batch) optimizer....