Layers Python Implementation

Affine (Fully connected layer):

from builtins import range
import numpy as np


def affine_forward(x, w, b):
    """
    Computes the forward pass for an affine (fully-connected) layer.

    The input x has shape (N, d_1, ..., d_k) and contains a minibatch of N
    examples, where each example x[i] has shape (d_1, ..., d_k). We will
    reshape each input into a vector of dimension D = d_1 * ... * d_k, and
    then transform it to an output vector of dimension M.

    Inputs:
    - x: A numpy array containing input data, of shape (N, d_1, ..., d_k)
    - w: A numpy array of weights, of shape (D, M)
    - b: A numpy array of biases, of shape (M,)

    Returns a tuple of:
    - out: output, of shape (N, M)
    - cache: (x, w, b)
    """
    # Number of images in the batch.
    NN = x.shape[0]

    # Reshape each input in our batch to a vector.
    reshaped_input = np.reshape(x,[NN, -1])

    # FC layer forward pass.
    out = np.dot(reshaped_input, w) + b
    
    cache = (x, w, b)
    return out, cache

def affine_backward(dout, cache):
    """
    Computes the backward pass for an affine layer.

    Inputs:
    - dout: Upstream derivative, of shape (N, M)
    - cache: Tuple of:
      - x: Input data, of shape (N, d_1, ... d_k)
      - w: Weights, of shape (D, M)

    Returns a tuple of:
    - dx: Gradient with respect to x, of shape (N, d1, ..., d_k)
    - dw: Gradient with respect to w, of shape (D, M)
    - db: Gradient with respect to b, of shape (M,)
    """
    x, w, b = cache

    # Number of images in the batch.
    NN = x.shape[0]

    # Reshape each input in our batch to a vector.
    reshaped_x = np.reshape(x,[NN, -1]) # input is an image, need to resize first.

    # Calculate dx = w*dout - remember to reshape back to shape of x.
    dx = np.dot(dout, w.T)
    dx = np.reshape(dx, x.shape)

    # Calculate dw = x*dout
    dw = np.dot(reshaped_x.T,dout)

    # Calculate db = dout
    db = np.sum(dout, axis=0)

    return dx, dw, db

Relu

def relu_forward(x):
    """
    Computes the forward pass for a layer of rectified linear units (ReLUs).

    Input:
    - x: Inputs, of any shape

    Returns a tuple of:
    - out: Output, of the same shape as x
    - cache: x
    """
    # Forward Relu.
    out = x.copy()  # Must use copy in numpy to avoid pass by reference.
    out[out < 0] = 0

    cache = x

    return out, cache

def relu_backward(dout, cache):
    """
    Computes the backward pass for a layer of rectified linear units (ReLUs).

    Input:
    - dout: Upstream derivatives, of any shape
    - cache: Input x, of same shape as dout

    Returns:
    - dx: Gradient with respect to x
    """
    x = cache

    # For Relu we only backprop to non-negative elements of x
    relu_mask = (x >= 0)
    dx = dout * relu_mask
    
    return dx

Batch Norm

def batchnorm_forward(x, gamma, beta, bn_param):
    """
    Forward pass for batch normalization.

    During training the sample mean and (uncorrected) sample variance are
    computed from minibatch statistics and used to normalize the incoming data.
    During training we also keep an exponentially decaying running mean of the
    mean and variance of each feature, and these averages are used to normalize
    data at test-time.

    At each timestep we update the running averages for mean and variance using
    an exponential decay based on the momentum parameter:

    running_mean = momentum * running_mean + (1 - momentum) * sample_mean
    running_var = momentum * running_var + (1 - momentum) * sample_var

    Note that the batch normalization paper suggests a different test-time
    behavior: they compute sample mean and variance for each feature using a
    large number of training images rather than using a running average. For
    this implementation we have chosen to use running averages instead since
    they do not require an additional estimation step; the torch7
    implementation of batch normalization also uses running averages.

    Input:
    - x: Data of shape (N, D)
    - gamma: Scale parameter of shape (D,)
    - beta: Shift paremeter of shape (D,)
    - bn_param: Dictionary with the following keys:
      - mode: 'train' or 'test'; required
      - eps: Constant for numeric stability
      - momentum: Constant for running mean / variance.
      - running_mean: Array of shape (D,) giving running mean of features
      - running_var Array of shape (D,) giving running variance of features

    Returns a tuple of:
    - out: of shape (N, D)
    - cache: A tuple of values needed in the backward pass
    """
    mode = bn_param['mode']
    eps = bn_param.get('eps', 1e-5)
    momentum = bn_param.get('momentum', 0.9)

    N, D = x.shape
    running_mean = bn_param.get('running_mean', np.zeros(D, dtype=x.dtype))
    running_var = bn_param.get('running_var', np.zeros(D, dtype=x.dtype))

    out, cache = None, None
    if mode == 'train':
        #######################################################################
        # TODO: Implement the training-time forward pass for batch norm.      #
        # Use minibatch statistics to compute the mean and variance, use      #
        # these statistics to normalize the incoming data, and scale and      #
        # shift the normalized data using gamma and beta.                     #
        #                                                                     #
        # You should store the output in the variable out. Any intermediates  #
        # that you need for the backward pass should be stored in the cache   #
        # variable.                                                           #
        #                                                                     #
        # You should also use your computed sample mean and variance together #
        # with the momentum variable to update the running mean and running   #
        # variance, storing your result in the running_mean and running_var   #
        # variables.                                                          #
        #######################################################################

        # Take sample mean & var of our minibatch across each dimension.
        sample_mean = np.mean(x, axis=0)
        sample_var = np.var(x, axis=0)

        # Normalize our batch then shift and scale with gamma/beta.
        normalized_data = (x - sample_mean) / np.sqrt(sample_var + eps)
        out = gamma * normalized_data + beta

        # Update our running mean and variance then store.
        running_mean = momentum * running_mean + (1 - momentum) * sample_mean
        running_var = momentum * running_var + (1 - momentum) * sample_var
        bn_param['running_mean'] = running_mean
        bn_param['running_var'] = running_var

        # Store intermediate results needed for backward pass.
        cache = {
            'x_minus_mean': (x - sample_mean),
            'normalized_data': normalized_data,
            'gamma': gamma,
            'ivar': 1./np.sqrt(sample_var + eps),
            'sqrtvar': np.sqrt(sample_var + eps),
        }
    elif mode == 'test':
        # Test time batch norm using learned gamma/beta and calculated running mean/var.
        out = (gamma / (np.sqrt(running_var + eps)) * x) + (beta - (gamma*running_mean)/np.sqrt(running_var + eps))
    else:
        raise ValueError('Invalid forward batchnorm mode "%s"' % mode)

    # Store the updated running means back into bn_param
    bn_param['running_mean'] = running_mean
    bn_param['running_var'] = running_var

    return out, cache

def batchnorm_backward(dout, cache):
    """
    Backward pass for batch normalization.

    For this implementation, you should write out a computation graph for
    batch normalization on paper and propagate gradients backward through
    intermediate nodes.

    Inputs:
    - dout: Upstream derivatives, of shape (N, D)
    - cache: Variable of intermediates from batchnorm_forward.

    Returns a tuple of:
    - dx: Gradient with respect to inputs x, of shape (N, D)
    - dgamma: Gradient with respect to scale parameter gamma, of shape (D,)
    - dbeta: Gradient with respect to shift parameter beta, of shape (D,)
    """
    # Get cached results from the forward pass.
    N, D = dout.shape
    normalized_data = cache.get('normalized_data')
    gamma = cache.get('gamma')
    ivar = cache.get('ivar')
    x_minus_mean = cache.get('x_minus_mean')
    sqrtvar = cache.get('sqrtvar')

    # Backprop dout to calculate dbeta and dgamma.
    dbeta = np.sum(dout, axis=0)
    dgamma = np.sum(dout * normalized_data, axis=0)

    # Carry on the backprop in steps to calculate dx.
    # Step1
    dxhat = dout*gamma
    # Step2
    dxmu1 = dxhat*ivar
    # Step3
    divar = np.sum(dxhat*x_minus_mean, axis=0)
    # Step4
    dsqrtvar = divar * (-1/sqrtvar**2)
    # Step5
    dvar = dsqrtvar * 0.5 * (1/sqrtvar)
    # Step6
    dsq = (1/N)*dvar*np.ones_like(dout)
    # Step7
    dxmu2 = dsq * 2 * x_minus_mean
    # Step8
    dx1 = dxmu1 + dxmu2
    dmu = -1*np.sum(dxmu1 + dxmu2, axis=0)
    # Step9
    dx2 = (1/N)*dmu*np.ones_like(dout)
    # Step10
    dx = dx2 + dx1
    
    return dx, dgamma, dbeta

The variance of a random variable is the expected value of the squared deviation from the mean of , $\mu =\operatorname {E} [X]$ : $\operatorname {Var} (X)=\operatorname {E} \left[(X-\mu )^{2}\right].$

This definition encompasses random variables that are generated by processes that are discrete, continuous, neither, or mixed. The variance can also be thought of as the covariance of a random variable with itself: $\operatorname {Var} (X)=\operatorname {Cov} (X,X).$

The variance is also equivalent to the second cumulant of a probability distribution that generates . The variance is typically designated as $\operatorname {Var} (X)$ , $\sigma _{X}^{2}$ , or simply (pronounced "sigma squared"). The expression for the variance can be expanded:

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