ActivityDetection.py

# Libraries
# Standard library
import random
import time

# Third-party libraries
import numpy as np


class Network(object):

    def __init__(self, sizes):
        """The list ``sizes`` contains the number of neurons in the
        respective layers of the network.  For example, if the list
        was [2, 3, 1] then it would be a three-layer network, with the
        first layer containing 2 neurons, the second layer 3 neurons,
        and the third layer 1 neuron.  The biases and weights for the
        network are initialized randomly, using a Gaussian
        distribution with mean 0, and variance 1.  Note that the first
        layer is assumed to be an input layer, and by convention we
        won't set any biases for those neurons, since biases are only
        ever used in computing the outputs from later layers."""
        self.num_layers = len(sizes)
        self.sizes = sizes
        self.biases = [np.random.randn(y, 1) for y in sizes[1:]]
        self.weights = [np.random.randn(y, x)
                        for x, y in zip(sizes[:-1], sizes[1:])]

    def feedforward(self, a):
        """Return the output of the network if ``a`` is input."""
        for b, w in zip(self.biases, self.weights):
            a = sigmoid(np.dot(w, a)+b)
        return a

    def SGD(self, training_data, epochs, mini_batch_size, eta,
            test_data=None):
        """Train the neural network using mini-batch stochastic
        gradient descent.  The ``training_data`` is a list of tuples
        ``(x, y)`` representing the training inputs and the desired
        outputs.  The other non-optional parameters are
        self-explanatory.  If ``test_data`` is provided then the
        network will be evaluated against the test data after each
        epoch, and partial progress printed out.  This is useful for
        tracking progress, but slows things down substantially."""

        training_data = list(training_data)
        n = len(training_data)

        if test_data:
            test_data = list(test_data)
            n_test = len(test_data)

        for j in range(epochs):
            random.shuffle(training_data)

            mini_batches = [training_data[k:k+mini_batch_size]
                            for k in range(0, n, mini_batch_size)]

            for mini_batch in mini_batches:
                self.update_mini_batch(mini_batch, eta)
            if test_data:
                print(f"Epoch {j} : {self.evaluate(test_data)} / {n_test}")
            else:
                print(f"Epoch {j} complete")

    def update_mini_batch(self, mini_batch, eta):
        """Update the network's weights and biases by applying
        gradient descent using backpropagation to a single mini batch.
        The ``mini_batch`` is a list of tuples ``(x, y)``, and ``eta``
        is the learning rate."""
        nabla_b = [np.zeros(b.shape) for b in self.biases]
        nabla_w = [np.zeros(w.shape) for w in self.weights]
        for x, y in mini_batch:
            delta_nabla_b, delta_nabla_w = self.backprop(x, y)
            nabla_b = [nb+dnb for nb, dnb in zip(nabla_b, delta_nabla_b)]
            nabla_w = [nw+dnw for nw, dnw in zip(nabla_w, delta_nabla_w)]
        self.weights = [w-(eta/len(mini_batch))*nw for w,
                        nw in zip(self.weights, nabla_w)]
        self.biases = [b-(eta/len(mini_batch))*nb for b,
                       nb in zip(self.biases, nabla_b)]

    def backprop(self, x, y):
        """Return a tuple ``(nabla_b, nabla_w)`` representing the
        gradient for the cost function C_x.  ``nabla_b`` and
        ``nabla_w`` are layer-by-layer lists of numpy arrays, similar
        to ``self.biases`` and ``self.weights``."""
        nabla_b = [np.zeros(b.shape) for b in self.biases]
        nabla_w = [np.zeros(w.shape) for w in self.weights]
        # feedforward
        activation = x
        activations = [x]  # list to store all the activations, layer by layer
        zs = []  # list to store all the z vectors, layer by layer
        for b, w in zip(self.biases, self.weights):
            z = np.dot(w, activation)+b
            zs.append(z)
            activation = sigmoid(z)
            activations.append(activation)
        # backward pass
        delta = self.cost_derivative(activations[-1], y) * \
            sigmoid_prime(zs[-1])
        nabla_b[-1] = delta
        nabla_w[-1] = np.dot(delta, activations[-2].transpose())
        for l in range(2, self.num_layers):
            z = zs[-l]
            sp = sigmoid_prime(z)
            delta = np.dot(self.weights[-l+1].transpose(), delta) * sp
            nabla_b[-l] = delta
            nabla_w[-l] = np.dot(delta, activations[-l-1].transpose())
        return (nabla_b, nabla_w)

    def evaluate(self, test_data):
        """Return the number of test inputs for which the neural
        network outputs the correct result. Note that the neural
        network's output is assumed to be the index of whichever
        neuron in the final layer has the highest activation."""
        test_results = [(np.argmax(self.feedforward(x)), y)
                        for (x, y) in test_data]
        return sum(int(x == y) for (x, y) in test_results)

    def cost_derivative(self, output_activations, y):
        """Return the vector of partial derivatives \partial C_x /
        \partial a for the output activations."""
        return (output_activations-y)

# Miscellaneous functions


def sigmoid(z):
    """The sigmoid function."""
    return 1.0/(1.0+np.exp(-z))


def sigmoid_prime(z):
    """Derivative of the sigmoid function."""
    return sigmoid(z)*(1-sigmoid(z))


def data_loader(x_train, y_train, x_test, y_test):
    training_inputs = [np.reshape(x, (2250, 1)) for x in x_train]
    training_results = [vectorized_result(y) for y in y_train]
    training_data = zip(training_inputs, training_results)
    test_inputs = [np.reshape(x, (2250, 1)) for x in x_test]
    test_data = zip(test_inputs, y_test)
    return (training_data, test_data)

# Modify based on the number of activities you're trying to classify


def vectorized_result(j):
    e = np.zeros((2, 1))
    e[j] = 1.0
    return e

# ! Must Implement load_data


def load_data():
    # Should iterate over each json file corresponding to each frame of a video 30 times (for 30 frames) and should extract the 25 keypoints
    # and store them in an array who's dimensions are of the form (number of training examples, 2250). More detail below
    # * Note: If input data is in the correct format, this function should only fetch the data, not process it
    pass


# Import data
(x_train, y_train), (x_test, y_test) = load_data()
# x_train should be of dimension (number of training examples, 2250) where 2250 is because each frame has 25 keypoints which translates
# to 75 pieces of information, coupled with 30 frames. It's assumed the coordinates of x_train are normalized between [0,1]. y_train
# is expected to have dimension (number of training examples, 1) where the y value of each training example is the number that corresponds to the activity.
# x_test and y_test are defined in the same way.

# Process data
train, test = data_loader(x_train, y_train, x_test, y_test)


# Initialize Neural Network
net = Network([2250, 30, 15, 2])

t1 = time.perf_counter()
net.SGD(train, 5, 10, 3, test)
print(f'Time taken: {time.perf_counter() - t1}s')