We welcome the ascension of our artificial overlords!

One of the applications I most anticipate from wetware-hardware cooperation is the production of neuron-preserving cerebral hardware that can keep my boring memories (e.g., uninterested learning topics) from leaving my mindscape for future recollection. And speaking of neurons…

Keras offers a streamlined approach to constructing neural networks by sequentially stacking layers. This high-level abstraction simplifies the process of building complex models, allowing developers to focus on architecture and hyperparameters rather than low-level tensor manipulations.

Sequential model

A Keras Sequential model is a linear stack of layers, where the output of one layer becomes the input to the next. It is a straightforward way to construct neural networks with a sequential flow of data. This model is suitable for most feedforward neural networks, such as those used for image classification, natural language processing, and time series forecasting. On the opposite side of the spectrum, the flexibility of a Sequential model is limited, making it unsuitable for complex architectures with shared layers or multiple inputs/outputs.

Input shape specification in Keras

Understanding the expected input format for different layer types is crucial for building accurate models. Be mindful of what you input into each part of Keras:

• Initial Layer Requirement: the first layer in a Sequential model must specify the input shape to establish the data format for subsequent layers.

• Input Shape Format: input shape is defined as a tuple of integers, is essential for establishing data consistency throughout the model, and represents the dimensions of the input data. For example, (32, 32, 3) for a 32 * 32 RGB image.

• Batch Size Exclusion: input shape does not include the batch size – a flexible dimension that can be adjusted independently of the input shape – as it can vary during training and inference.

• Dimensionality: different layer types have specific input shape requirements. Dense layers expect 2D inputs, while convolutional layers typically handle 3D or 4D data.

Common parameters in Keras

Here are several basic parameters for building Keras neural networks:

• Dense: connects all neurons in one layer to all neurons in the next layer. Parameters include number of neurons (units), activation function, kernel initializer, bias initializer, etc.

• Activation: applies a mathematical function to the output of a layer. Includes ReLU, sigmoid, tanh, softmax, etc.

• Dropout: introduces noise during training to improve generalization. Prevents overfitting by randomly setting input units to zero at each update.

• Flatten: reshapes input data for subsequent dense layers. Converts multi-dimensional input (e.g., image) into a one-dimensional vector.

• Reshape: allows for manipulation of tensor dimensions. Reshapes the tensor to a new shape without changing its data. Parameter includes desired shape of the output tensor (target_shape).

Sequential modelling on Python

For running a Keras Sequential model on Python, we will be using CIFAR-10. 

Below is the Sequential model script. Here is what occurs in each descending sentence:

1. Creates an empty sequential model and refers it to a shorter, pure string variable.

2. Adds a convolutional layer with 64 filters, 3 * 3 kernel size, 'same' padding to preserve input shape, and input shape derived from the training data.

3. Applies the ReLU (rectified linear unit) activation function to the output of the convolutional layer. The function is f(x) = max(0, x), which outputs the input if it is positive, otherwise it outputs zero.

4. Converts the 2D feature maps from the convolutional layer into a 1D vector. In the context of neural networks, a 1D vector often represents a feature vector, a numerical representation of a data point.

5. Adds a fully connected layer with 512 neurons.

6. Applies the ReLU activation function to the output of the dense layer, again.

7. Introduces dropout with a rate of 0.5, where 50% of the neurons in a layer are randomly dropped out (set to 0) during each training iteration.

8. Adds the final dense layer with the number of neurons equal to the number of classes.

9. Applies the softmax activation function for classification. Softmax softmax(x_i) = exp(x_i) / sum(exp(x_j)) for all j converts a vector of numbers into a probability distribution over K possible outcomes.

10. Displays the model architecture and parameter count.

The graph below contains data on the Sequential model. A notable observation in the model is the use of dropout, which suggests an awareness of overfitting, but the effectiveness of a single dropout layer might be limited for complex datasets. Also, the Conv2D layer lacks an explicit input_shape argument, which might lead to errors if the input data does not match the expected dimensions.