如何使用 Python 使用 Tensorflow 来组合层?

tensorflowserver side programmingprogrammingpython更新于 2024/7/6 2:20:17

Tensorflow 可用于通过定义从‘ResnetIdentityBlock’继承的类来组合层。这用于定义可用于组合层的块。

TensorFlow Hub 是一个包含预训练 TensorFlow 模型的存储库。TensorFlow 可用于微调学习模型。我们将了解如何将 TensorFlow Hub 中的模型与 tf.keras 结合使用，并使用 TensorFlow Hub 中的图像分类模型。完成此操作后，可以执行迁移学习来微调自定义图像类别的模型。这是通过使用预训练的分类器模型来获取图像并预测其内容来完成的。这可以在不需要任何训练的情况下完成。

我们正在使用 Google Colaboratory 来运行以下代码。Google Colab 或 Colaboratory 有助于在浏览器上运行 Python 代码，并且不需要任何配置，并且可以免费访问 GPU(图形处理单元)。Colaboratory 是在 Jupyter Notebook 之上构建的。

示例

print("Composing layers")
class ResnetIdentityBlock(tf.keras.Model):
   def __init__(self, kernel_size, filters):
      super(ResnetIdentityBlock, self).__init__(name='')
      filters1, filters2, filters3 = filters
      self.conv2a = tf.keras.layers.Conv2D(filters1, (1, 1))
      self.bn2a = tf.keras.layers.BatchNormalization()
      self.conv2b = tf.keras.layers.Conv2D(filters2, kernel_size, padding='same')
      self.bn2b = tf.keras.layers.BatchNormalization()
      self.conv2c = tf.keras.layers.Conv2D(filters3, (1, 1))
      self.bn2c = tf.keras.layers.BatchNormalization()
   def call(self, input_tensor, training=False):
      x = self.conv2a(input_tensor)
      x = self.bn2a(x, training=training)
      x = tf.nn.relu(x)
      x = self.conv2b(x)
      x = self.bn2b(x, training=training)
      x = tf.nn.relu(x)
      x = self.conv2c(x)
      x = self.bn2c(x, training=training)
      x += input_tensor
      return tf.nn.relu(x)
print("The layer is called")
block = ResnetIdentityBlock(1, [1, 2, 3])
_ = block(tf.zeros([1, 2, 3, 3]))
block.layers
len(block.variables)
block.summary()

代码来源 −https://www.tensorflow.org/tutorials/customization/custom_layers

输出

Composing layers
The layer is called
Model: "resnet_identity_block"
_________________________________________________________________
Layer (type)        Output Shape      Param #
=================================================================
conv2d (Conv2D)       multiple          4
_________________________________________________________________
batch_normalization (BatchNo multiple   4
_________________________________________________________________
conv2d_1 (Conv2D)      multiple        4
_________________________________________________________________
batch_normalization_1 (Batch multiple  8
_________________________________________________________________
conv2d_2 (Conv2D)     multiple         9
_________________________________________________________________
batch_normalization_2 (Batch multiple  12
=================================================================
Total params: 41
Trainable params: 29
Non-trainable params: 12