💡 Problem Formulation: Machine learning practitioners often need to add a classification layer, or “head,” to their neural network models to tackle classification problems. In TensorFlow, this is typically done after pre-processing the data, constructing and training a base model, and then appending a classification layer that outputs the probability of the input belonging to each class. The input is often a set of feature vectors, and the desired output is the class label probabilities for those features.
Method 1: Using ‘Dense’ Layer
This method involves adding a Dense layer as a classification head. A Dense layer is a fully connected neural network layer where each input node is connected to each output node. The Dense layer’s output size should match the number of classes. Also, an activation function, usually ‘softmax’, is used for multi-class classification problems to output probabilities.
Here’s an example:
import tensorflow as tf
model = tf.keras.Sequential([
# ... (previous layers of the model) ...
tf.keras.layers.Dense(10, activation='softmax')
])
# Compiling the model
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
The output is a TensorFlow model with a newly added classification head.
This snippet shows the final layer addition to a previously defined sequential model. Here, a Dense layer with 10 neurons is added for a 10-class classification problem, and ‘softmax’ activation is used to output class probabilities.
Method 2: Custom Keras Model Subclass
Creating a custom model subclass in Keras enables greater flexibility. By subclassing tf.keras.Model, you can define your own forward pass and easily tack on a classification head with your logic for processing inputs and outputs.
Here’s an example:
import tensorflow as tf
class CustomModel(tf.keras.Model):
def __init__(self, num_classes):
super(CustomModel, self).__init__()
self.feature_extractor = # ... (base model layers) ...
self.classifier = tf.keras.layers.Dense(num_classes, activation='softmax')
def call(self, inputs):
x = self.feature_extractor(inputs)
return self.classifier(x)
model = CustomModel(num_classes=10)
The output is a custom TensorFlow model capable of classification.
This code shows how to create a custom model that incorporates a base feature extractor and adds a Dense layer for classification. The class is initialized with the number of classes, and the call() method defines the model’s forward pass.
Method 3: Attaching to a Pretrained Model
In this method, a pretrained model, such as one from tf.keras.applications, is used. The top (classification) layer of the pretrained model is replaced with a new classifier suited for the specific number of classes in the new dataset.
Here’s an example:
import tensorflow as tf
base_model = tf.keras.applications.MobileNetV2(input_shape=(224, 224, 3),
include_top=False,
weights='imagenet')
base_model.trainable = False
global_average_layer = tf.keras.layers.GlobalAveragePooling2D()
prediction_layer = tf.keras.layers.Dense(10, activation='softmax')
model = tf.keras.Sequential([
base_model,
global_average_layer,
prediction_layer
])
The output is a TensorFlow model with a custom classification layer suitable for 10 classes.
This snippet illustrates how to apply transfer learning by taking a MobileNetV2 model pretrained on the ImageNet dataset, freezing its weights for feature extraction, and adding global average pooling and dense layers for classification.
Method 4: Using TensorFlow Feature Columns
TensorFlow feature columns provide a way to handle various types of data transformation. For classification tasks, categorical feature columns can be used to map categorical inputs to one-hot encodings before passing them through a Dense layer.
Here’s an example:
import tensorflow as tf
feature_columns = [
# Define feature columns here
# ... (e.g., tf.feature_column.categorical_column_with_vocabulary_list) ...
]
feature_layer = tf.keras.layers.DenseFeatures(feature_columns)
classifier_layer = tf.keras.layers.Dense(10, activation='softmax')
model = tf.keras.Sequential([
feature_layer,
# ... (additional layers) ...
classifier_layer
])
The output is a TensorFlow model with a classification head that includes feature transformation.
The code here defines a feature layer that handles preprocessing within the model. It’s followed by a Dense layer to classify the preprocessed input into one of the ten possible classes.
Bonus One-Liner Method 5: Lambda Layer
A lambda layer can be used to apply arbitrary functions as a way to attach a simple classification head. In this method, a lambda layer utilizes TensorFlow operations to create a custom classification logic.
Here’s an example:
import tensorflow as tf
model = tf.keras.Sequential([
# ... (pre-existing model layers) ...
tf.keras.layers.Lambda(lambda x: tf.keras.backend.dot(x, tf.Variable(tf.random_normal_initializer()(shape=(x.shape[-1], 10)))) +
tf.Variable(tf.zeros_initializer()(shape=(10,)), trainable=True),
output_shape=(10,))
])
The output is a TensorFlow model that uses a lambda layer for classification.
This code shows a lambda layer with a custom weighted sum operation and bias, mimicking a Dense layer without using the traditional Keras API. It’s less readable and flexible but offers a quick solution for simple models.
Summary/Discussion
- Method 1: Dense Layer. Straightforward and commonly used for adding classification heads. May not offer as much flexibility for custom operations.
- Method 2: Custom Keras Model Subclass. Highly flexible and allows for complex model architectures. Can be more complex to implement.
- Method 3: Pretrained Model. Harnesses pre-trained features for high performance with less data. Must adapt to the input size expected by the base model.
- Method 4: TensorFlow Feature Columns. Integral for handling diverse input data types and preprocessing within the model. May require deeper understanding of feature columns for complex data types.
- Method 5: Lambda Layer. Offers quick and flexible implementation of simple custom functions. Less intuitive and potentially harder to debug.