Neural Network Mind Map - Agentic Agent
The provided code is of an extensive and advanced TensorFlow neural network script. It constructs a complex model using custom residual blocks and implements a custom training loop with distributed strategies and detailed logging via TensorBoard. The script demonstrates intricate TensorFlow mechanics and is designed to push the envelope of complexity.
The script starts by importing the necessary TensorFlow and NumPy libraries and setting the TensorFlow logging level to suppress unnecessary logs.
It then defines custom layers using the subclassing approach. The CustomDense layer implements a dense layer with customizable options such as activation, bias, and regularizers. The ResidualBlock layer implements a residual block consisting of two custom dense layers and an activation layer. These custom layers provide flexibility in defining complex neural network architectures.
The script also defines a complex model, ComplexModel, which utilizes the custom layers and residual blocks to construct the overall model architecture. The model includes an input layer, hidden layers, residual blocks, batch normalization, dropout, and an output layer. The model is capable of handling classification tasks with softmax activation.
A custom learning rate scheduler callback, CustomLearningRateScheduler, is implemented to adjust the learning rate during training based on a predefined decay factor and steps.
The script includes a custom training loop function, custom_train_loop, which uses TensorFlow's tf.GradientTape for manual gradient computation. This function performs the training steps for a given number of epochs and logs the loss and accuracy metrics.
The main function provides the main execution flow of the script. It generates a synthetic dataset, creates a tf.data.Dataset from the data, sets up a distributed strategy, instantiates the ComplexModel, sets up the optimizer and loss function, compiles the model, and initializes the TensorBoard logging.
There are two options for training the model: using the model.fit function with complex callbacks or using the fully custom training loop defined earlier. Finally, the trained model is saved with the architecture and weights included.
Overall, this script showcases advanced TensorFlow features, such as custom layers, residual blocks, custom training loop, distributed strategy, and detailed logging with TensorBoard. It can serve as a reference for building complex neural networks and training models from scratch.
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