Addressing Vanishing Gradients in TensorFlow
- Utilize Activation Functions Effectively
To mitigate vanishing gradients, consider using activation functions like ReLU (Rectified Linear Unit) or its variants such as Leaky ReLU, Parametric ReLU, or Exponential Linear Unit (ELU), instead of sigmoid or tanh, which tend to suffer more from this problem.
```python
import tensorflow as tf
model = tf.keras.models.Sequential([
tf.keras.layers.Dense(128, activation='relu', input_shape=(784,)),
tf.keras.layers.Dense(10, activation='softmax')
])
```
- Apply Proper Weight Initialization
Use weight initializations like He initialization which are designed for activation functions such as ReLU. This helps in keeping the gradient flow stable as the signal traverses through the network.
```python
initializer = tf.keras.initializers.HeNormal()
model = tf.keras.models.Sequential([
tf.keras.layers.Dense(128, activation='relu', kernel_initializer=initializer, input_shape=(784,)),
tf.keras.layers.Dense(10, activation='softmax')
])
```
- Incorporate Batch Normalization
Batch normalization helps in addressing the covariate shift within the training, thus maintaining the gradient flow. It normalizes the activations for every mini-batch.
```python
model = tf.keras.models.Sequential([
tf.keras.layers.Dense(128, activation='relu', input_shape=(784,)),
tf.keras.layers.BatchNormalization(),
tf.keras.layers.Dense(10, activation='softmax')
])
```
- Use Gradient Clipping
Gradient clipping avoids extreme value gradients by capping them within a predefined threshold. This technique can also help in preventing exploding gradients.
```python
optimizer = tf.keras.optimizers.Adam(clipnorm=1.0)
model.compile(optimizer=optimizer,
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
```
- Experiment with Alternative Architectures
Explore using architectures designed to mitigate vanishing gradients, such as ResNet, LSTM, or GRU. Residual networks (ResNets) utilize skip connections which improve gradient flow.
```python
class ResNetBlock(tf.keras.Model):
def init(self, num_filters, kernel_size=3):
super(ResNetBlock, self).init()
self.conv1 = tf.keras.layers.Conv2D(num_filters, kernel_size, padding='same')
self.bn1 = tf.keras.layers.BatchNormalization()
self.conv2 = tf.keras.layers.Conv2D(num_filters, kernel_size, padding='same')
self.bn2 = tf.keras.layers.BatchNormalization()
def call(self, inputs):
x = self.conv1(inputs)
x = self.bn1(x)
x = tf.keras.activations.relu(x)
x = self.conv2(x)
x = self.bn2(x)
x = x + inputs # Skip connection
return tf.keras.activations.relu(x)
```
- Adjust the Network Architecture
Selecting an appropriate depth for your network is critical. Consider shallower networks if deeper ones exacerbate vanishing gradients, or incorporate residual connections or recurrent units that naturally manage long propagation paths.
