def data_deal(y, batch_size): # y = np.array(y) # 计算stride = 1不补零可生成的batch数量 num_batches = tf.shape(y)[0] # 在数据前面补上一些0 zeros_tensor = tf.zeros([batch_size-1, tf.shape(y)[1]]) y_ = tf.concat([zeros_tensor, y], axis=0) # y_ = tf.concat([tf.zeros((batch_size-1,), dtype=y.dtype), y], axis=0) # 生成每个batch batch_data = y_[:batch_size, :] b = tf.expand_dims(batch_data, axis=0) for i in range(1, num_batches): L = y_[i:i+batch_size, :] M = tf.expand_dims(L, axis=0) b = tf.concat([b, M], axis=0) return b 这段代码如果不用0填补而是用y的第一行填补,怎么修改
时间: 2024-03-28 10:42:24 浏览: 21
可以使用以下代码将0填充改为用y的第一行填充:
```
def data_deal(y, batch_size):
# 计算stride = 1不补零可生成的batch数量
num_batches = tf.shape(y)[0]
# 在数据前面补上一些y的第一行
first_row = tf.expand_dims(y[0], axis=0)
repeat_times = batch_size - 1
zeros_tensor = tf.tile(first_row, [repeat_times, 1])
y_ = tf.concat([zeros_tensor, y], axis=0)
# 生成每个batch
batch_data = y_[:batch_size, :]
b = tf.expand_dims(batch_data, axis=0)
for i in range(1, num_batches):
L = y_[i:i+batch_size, :]
M = tf.expand_dims(L, axis=0)
b = tf.concat([b, M], axis=0)
return b
```
这样就可以用y的第一行填充了。
相关问题
class TimeWindowSequence(Sequence): def __init__(self, x, y, batch_size, window_size): self.x = x self.y = y self.batch_size = batch_size self.window_size = window_size self.window_count = int(np.ceil(x.shape[0] / window_size)) def __len__(self): return int(np.ceil(self.x.shape[0] / self.batch_size)) def __getitem__(self, idx): batch_x = np.zeros((self.batch_size, self.window_size, self.x.shape[1])) batch_y = np.zeros((self.batch_size, self.y.shape[1])) for i in range(self.batch_size): j = idx * self.batch_size + i if j >= self.window_count: break window_x = self.x[j*self.window_size:(j+1)*self.window_size, :] window_y = self.y[j*self.window_size:(j+1)*self.window_size, :] batch_x[i, :window_x.shape[0], :] = window_x batch_y[i, :] = window_y[-1, :] return batch_x, batch_y出现
这段代码中似乎缺少了一个缩进,导致 `return` 语句在 `for` 循环内部。可以尝试将 `return` 语句缩进到 `for` 循环外部,如下所示:
```
class TimeWindowSequence(Sequence):
def __init__(self, x, y, batch_size, window_size):
self.x = x
self.y = y
self.batch_size = batch_size
self.window_size = window_size
self.window_count = int(np.ceil(x.shape[0] / window_size))
def __len__(self):
return int(np.ceil(self.x.shape[0] / self.batch_size))
def __getitem__(self, idx):
batch_x = np.zeros((self.batch_size, self.window_size, self.x.shape[1]))
batch_y = np.zeros((self.batch_size, self.y.shape[1]))
for i in range(self.batch_size):
j = idx * self.batch_size + i
if j >= self.window_count:
break
window_x = self.x[j*self.window_size:(j+1)*self.window_size, :]
window_y = self.y[j*self.window_size:(j+1)*self.window_size, :]
batch_x[i, :window_x.shape[0], :] = window_x
batch_y[i, :] = window_y[-1, :]
return batch_x, batch_y
```
这样应该就可以解决这个问题了。
怎么用神经网络方法达到def data_deal(y, batch_size): # 计算stride = 1不补零可生成的batch数量 num_batches = tf.shape(y)[0] # 在数据前面补上一些0 zeros_tensor = tf.zeros([batch_size-1, tf.shape(y)[1]]) y_ = tf.concat([zeros_tensor, y], axis=0) # y_ = tf.concat([tf.zeros((batch_size-1,), dtype=y.dtype), y], axis=0) # 生成每个batch batch_data = y_[:batch_size, :] b = tf.expand_dims(batch_data, axis=0) for i in range(1, num_batches): L = y_[i:i+batch_size, :] M = tf.expand_dims(L, axis=0) b = tf.concat([b, M], axis=0) return b的效果?
这段代码是用来处理数据的,将一维的输入数据y按照batch_size进行分组,每一组batch中包含batch_size个数据,同时在数据前面补上一些0,以便数据能够被完整地分成多个batch。具体实现如下:
1. 首先计算出stride为1且不补零的情况下,输入数据y能够生成的batch数量;
2. 在数据前面补上batch_size-1个0,这样可以保证数据能够被完整地分成多个batch;
3. 从补零后的数据中取出前batch_size个数据,作为第一个batch_data;
4. 之后,从第二个数据开始,每次取出batch_size个数据,作为一个batch,将这些batch合并成一个张量b;
5. 最后返回合并后的张量b,其中b的shape为[num_batches, batch_size, input_dim],其中input_dim是输入数据的维度。