batch_size = tf.shape(pc)[0]
时间: 2024-06-02 16:14:19 浏览: 13
This line of code is creating a TensorFlow operation that finds the number of rows in the tensor "pc" and assigns it to the variable "batch_size".
The "tf.shape()" function returns the shape of a tensor as a 1-D integer tensor. We are passing "pc" as an argument to this function, which means we want to find the shape of "pc". The first element of the resulting tensor represents the number of rows in "pc", which is the batch size of the input data.
We then assign this value to the variable "batch_size" using the TensorFlow assignment operation. This variable can be later used in the code to perform operations that depend on the batch size of the input data.
相关问题
batch_size = inputs.size(0)
batch_size = inputs.size(0)
This line of code determines the batch size of the input data. The input tensor is expected to have a shape of (batch_size, input_size), where batch_size is the number of samples in the batch and input_size is the number of input features. By calling the `size()` method on the input tensor and passing `0` as the argument, we obtain the size of the first dimension, which corresponds to the batch size. This value is then assigned to the `batch_size` variable.
这段代码tf,conat时出现了问题for i in range(0, num_samples, batch_size): batch = x_data[i:i + batch_size] batch = tf.expand_dims(batch, axis=0) if i + batch_size > num_samples: batch_num = num_samples - i if batch_num == 1: feature_batch = model.predict(batch.reshape(1, *input_shape), batch_size=batch_num) else: feature_batch = model.predict(batch, batch_size=batch_num) tf.concat(features, axis=1) else: feature_batch = model.predict(batch, batch_size=batch_size) tf.concat(features, axis=1)
这段代码中有两个问题:
1. 在使用`tf.concat()`函数进行拼接时,没有将拼接后的结果重新赋值给`features`。因为`tf.concat()`函数返回的是拼接后的结果,需要将其重新赋值给`features`。修改后的代码如下:
```python
features = tf.concat([features, feature_batch], axis=1)
```
2. 在拼接之前,需要对`features`进行初始化。因为`features`是一个TensorFlow变量,需要使用`tf.Variable()`函数对其进行初始化。修改后的代码如下:
```python
features = tf.Variable(initial_value=tf.zeros((feature_shape, 0)), dtype=tf.float32)
```
综合修改后的代码如下:
```python
features = tf.Variable(initial_value=tf.zeros((feature_shape, 0)), dtype=tf.float32)
for i in range(0, num_samples, batch_size):
batch = x_data[i:i + batch_size]
batch = tf.expand_dims(batch, axis=0)
if i + batch_size > num_samples:
batch_num = num_samples - i
if batch_num == 1:
feature_batch = model.predict(batch.reshape(1, *input_shape), batch_size=batch_num)
else:
feature_batch = model.predict(batch, batch_size=batch_num)
features = tf.concat([features, feature_batch], axis=1)
else:
feature_batch = model.predict(batch, batch_size=batch_size)
features = tf.concat([features, feature_batch], axis=1)
```
希望能够帮到你!
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