import numpy as np

##Finished
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 samples,
    where each sample 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.


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

    :return: A tuple of
            - out: output of shape (N, M)
            - cache: (x, w, b)
    """

    out = None
    out = x.reshape(x.shape[0], -1).dot(w) + b
    cache = (x, w, b)
    return out, cache


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


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

    :return: A tuple of
            - dx: Gradient with respect to x, of shape (N, d_1, ..., 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
    dx, dw, db = None, None, None
    dx = dout.dot(w.T)
    dx = np.reshape(dx, x.shape).copy()

    dw = (x.reshape((x.shape[0], -1)).T).dot(dout)
    db = np.sum(dout, axis=0)

    return dx, dw, db


##Finished
def relu_forward(x):
    """
    Compute the forward pass for a layer of rectified linear units(RELU)

    :param x: Inputs, of any shape

    :return: a tuple of
            - out: Output of the same shape as x
            - cache: x
    """
    out = None
    out = np.maximum(0, x)
    cache = x
    return out, cache


##Finished
def relu_backward(dout, cache):
    """
    Compute the backward pass for a layer of rectified linear units(ReLU)

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

    :return: dx Gradient w.r.t. x
    """
    dx, x = None, cache

    dx = dout
    dx[x < 0] = 0
    return dx


##Finished
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':
        sample_mean = np.mean(x, axis=0)
        sample_var = np.var(x, axis=0)
        x_estimate = (x-sample_mean)/np.sqrt(sample_var + eps)
        out = gamma*x_estimate + beta

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

        temp = 1 / np.sqrt(sample_var + eps)
        cache = (x, x_estimate, temp, gamma, sample_mean)

    elif mode == 'test':
        x_estimate = (x-running_mean)/np.sqrt(running_var + eps)
        out = gamma*x_estimate + beta
    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


##Finished
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,)
    """
    dx, dgamma, dbeta = None, None, None
    x, x_estimate, temp, gamma, sample_mean, = cache
    N, D = x.shape


    dgamma = np.sum(dout*x_estimate, axis=0)
    dbeta = np.sum(dout, axis=0)

    dl_xhat = dout*gamma
    dl_x1 = dl_xhat*temp

    dl_vari = dl_xhat*(x-sample_mean)*(-1/2)*(temp**3)
    dl_var = np.sum(dl_vari, axis=0)
    dl_x2 = dl_var*2*(x-sample_mean)*(1/N)

    dl_mean = np.sum(-1*dl_x1, axis=0) + dl_var*(1/N)*np.sum(-2*(x-sample_mean), axis=0)
    dl_x3 = dl_mean*(1/N)

    dx = dl_x1 + dl_x2 + dl_x3

    return dx, dgamma, dbeta


##Finished
def batchnorm_backward_alt(dout, cache):
    """
    Alternative backward pass for batch normalization.

    For this implementation you should work out the derivatives for the batch
    normalizaton backward pass on paper and simplify as much as possible. You
    should be able to derive a simple expression for the backward pass.
    See the jupyter notebook for more hints.

    Note: This implementation should expect to receive the same cache variable
    as batchnorm_backward, but might not use all of the values in the cache.

    Inputs / outputs: Same as batchnorm_backward
    """
    dx, dgamma, dbeta = None, None, None
    x, x_estimate, intermidiate, gamma, sample_mean = cache
    N, _ = x.shape

    dgamma = np.sum(dout*x_estimate, axis=0)
    dbeta = np.sum(dout, axis=0)

    dx = gamma*(1/N)*intermidiate*(N*dout - (x-sample_mean)*np.square(intermidiate)*np.sum(dout*(x-sample_mean), axis=0) - np.sum(dout, axis=0))


    return dx, dgamma, dbeta


##Finished
def layernorm_forward(x, gamma, beta, ln_param):
    """
    Forward pass for layer normalization.

    During both training and test-time, the incoming data is normalized per data-point,
    before being scaled by gamma and beta parameters identical to that of batch normalization.

    Note that in contrast to batch normalization, the behavior during train and test-time for
    layer normalization are identical, and we do not need to keep track of running averages
    of any sort.

    Input:
    - x: Data of shape (N, D)
    - gamma: Scale parameter of shape (D,)
    - beta: Shift paremeter of shape (D,)
    - ln_param: Dictionary with the following keys:
        - eps: Constant for numeric stability

    Returns a tuple of:
    - out: of shape (N, D)
    - cache: A tuple of values needed in the backward pass
    """
    out, cache = None, None
    eps = ln_param.get('eps', 1e-5)

    N, D = x.shape
    mean = np.mean(x, axis=1) ##Shape(N,)
    var = np.var(x, axis=1) ##Shape(N,)

    x_estimate = (x-mean[:,np.newaxis])/np.sqrt(var[:,np.newaxis]+eps)
    out = gamma*x_estimate + beta

    temp = 1/np.sqrt(var[:,np.newaxis]+eps)
    cache = (x, x_estimate, temp, gamma, mean)
    return out, cache


##Finished
def layernorm_backward(dout, cache):
    """
    Backward pass for layer normalization.

    For this implementation, you can heavily rely on the work you've done already
    for batch normalization.

    Inputs:
    - dout: Upstream derivatives, of shape (N, D)
    - cache: Variable of intermediates from layernorm_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,)
    """
    dx, dgamma, dbeta = None, None, None

    x, x_estimate, temp, gamma, mean = cache
    N, D = x.shape

    dgamma = np.sum(x_estimate*dout, axis=0)
    dbeta = np.sum(dout, axis=0)

    dl_xhat = dout*gamma
    dl_x1 = dl_xhat*temp

    dl_vari = dl_xhat*(x-mean[:, np.newaxis])*(-1/2)*(temp**3)
    dl_var = np.sum(dl_vari, axis=1) ##Shape(N,)
    dl_x2 = 2*(x-mean[:, np.newaxis])*(1/D)*dl_var[:, np.newaxis]

    dl_mean = np.sum(-1*dl_x1, axis=1) + dl_var*(1/D)*np.sum(-2*(x-mean[:,np.newaxis]), axis=1)
    dl_x3 = dl_mean*(1/D)

    print(dl_x1.shape, dl_x2.shape, dl_x3.shape)
    dx = dl_x1 + dl_x2 + dl_x3[:, np.newaxis]

    return dx, dgamma, dbeta


##Finished
def dropout_forward(x, dropout_param):
    """
    Performs the forward pass for (inverted) dropout.

    Inputs:
    - x: Input data, of any shape
    - dropout_param: A dictionary with the following keys:
      - p: Dropout parameter. We keep each neuron output with probability p.
      - mode: 'test' or 'train'. If the mode is train, then perform dropout;
        if the mode is test, then just return the input.
      - seed: Seed for the random number generator. Passing seed makes this
        function deterministic, which is needed for gradient checking but not
        in real networks.

    Outputs:
    - out: Array of the same shape as x.
    - cache: tuple (dropout_param, mask). In training mode, mask is the dropout
      mask that was used to multiply the input; in test mode, mask is None.

    NOTE: Please implement **inverted** dropout, not the vanilla version of dropout.
    See http://cs231n.github.io/neural-networks-2/#reg for more details.

    NOTE 2: Keep in mind that p is the probability of **keep** a neuron
    output; this might be contrary to some sources, where it is referred to
    as the probability of dropping a neuron output.
    """
    p, mode = dropout_param['p'], dropout_param['mode']
    if 'seed' in dropout_param:
        np.random.seed(dropout_param['seed'])

    mask = None
    out = None

    if mode == 'train':
        mask = (np.random.rand(*x.shape) < p)/p
        out = mask*x

    elif mode == 'test':
        out = x

    cache = (dropout_param, mask)
    out = out.astype(x.dtype, copy=False)

    return out, cache


##Finished
def dropout_backward(dout, cache):
    """
    Perform the backward pass for (inverted) dropout.

    Inputs:
    - dout: Upstream derivatives, of any shape
    - cache: (dropout_param, mask) from dropout_forward.
    """
    dropout_param, mask = cache
    mode = dropout_param['mode']

    dx = None
    if mode == 'train':
        dx = dout*mask

    elif mode == 'test':
        dx = dout
    return dx


##Finished
def conv_forward_naive(x, w, b, conv_param):
    """
    A naive implementation of the forward pass for a convolutional layer.

    The input consists of N data points, each with C channels, height H and
    width W. We convolve each input with F different filters, where each filter
    spans all C channels and has height HH and width WW.

    Input:
    - x: Input data of shape (N, C, H, W)
    - w: Filter weights of shape (F, C, HH, WW)
    - b: Biases, of shape (F,)
    - conv_param: A dictionary with the following keys:
      - 'stride': The number of pixels between adjacent receptive fields in the
        horizontal and vertical directions.
      - 'pad': The number of pixels that will be used to zero-pad the input.


    During padding, 'pad' zeros should be placed symmetrically (i.e equally on both sides)
    along the height and width axes of the input. Be careful not to modfiy the original
    input x directly.

    Returns a tuple of:
    - out: Output data, of shape (N, F, H', W') where H' and W' are given by
      H' = 1 + (H + 2 * pad - HH) / stride
      W' = 1 + (W + 2 * pad - WW) / stride
    - cache: (x, w, b, conv_param)
    """
    out = None
    stride = conv_param['stride']
    pad = conv_param['pad']
    pad_width = ((0,0),(0,0),(pad, pad),(pad, pad))
    N, C, H, W = x.shape
    F, C, HH, WW = w.shape
    H_prime = int(1 + (H + 2*pad - HH)/stride)
    W_prime = int(1 + (W + 2*pad - WW)/stride)
    out = np.zeros((N, F, H_prime, W_prime))

    x_padded = np.pad(x, pad_width=pad_width, mode='constant', constant_values=0)
    for n in range(N):
        #every single image
        for f in range(F):
            #every single filter
            i = 0
            while i < H_prime:
                ##every single dim1
                j = 0
                while j < W_prime:
                    i_index = i*stride
                    j_index = j*stride
                    out[n, f, i, j] = np.sum(w[f, :]*x_padded[n, :, i_index:i_index+HH, j_index:j_index+WW]) + b[f]
                    j += 1
                i += 1

    cache = (x, w, b, conv_param)
    return out, cache


##Finished
def conv_backward_naive(dout, cache):
    """
    A naive implementation of the backward pass for a convolutional layer.

    Inputs:
    - dout: Upstream derivatives of shape(N, F, H', W')
    - cache: A tuple of (x, w, b, conv_param) as in conv_forward_naive

    Returns a tuple of:
    - dx: Gradient with respect to x, of shape(N, C, H, W)
    - dw: Gradient with respect to w, of shape(F, C, HH, WW)
    - db: Gradient with respect to b, of shape(F,)
    """
    dx, dw, db = None, None, None
    x, w, b, conv_param = cache
    N, C, H, W = x.shape
    F, C, HH, WW = w.shape
    N, F, H_prime, W_prime = dout.shape
    stride = conv_param['stride']
    pad = conv_param['pad']
    pad_width = ((0, 0), (0, 0), (pad, pad), (pad, pad))
    x_padded = np.pad(x, pad_width=pad_width, mode='constant', constant_values=0)

    dx = np.zeros_like(x_padded, dtype=np.float64)
    dw = np.zeros_like(w, dtype=np.float64)


    for n in range(N):
        for f in range(F):
            for i in range(H_prime):
                for j in range(W_prime):
                    h_index = i*stride
                    w_index = j*stride
                    dw[f, :] += dout[n, f, i, j]*x_padded[n, :, h_index:h_index+HH, w_index:w_index+WW]
                    dx[n, :, h_index:h_index+HH, w_index:w_index+WW] += dout[n, f, i, j]*w[f, :]

    dx = dx[:,:, pad:-pad, pad:-pad]
    db = np.sum(dout, axis=(3,2,0))


    return dx, dw, db


##Finished
def max_pool_forward_naive(x, pool_param):
    """
    A naive implementation of the forward pass for a max-pooling layer.

    Inputs:
    - x: Input data, of shape (N, C, H, W)
    - pool_param: dictionary with the following keys:
      - 'pool_height': The height of each pooling region
      - 'pool_width': The width of each pooling region
      - 'stride': The distance between adjacent pooling regions

    No padding is necessary here. Output size is given by

    Returns a tuple of:
    - out: Output data, of shape (N, C, H', W') where H' and W' are given by
      H' = 1 + (H - pool_height) / stride
      W' = 1 + (W - pool_width) / stride
    - cache: (x, pool_param)
    """
    out = None
    pool_height = pool_param['pool_height']
    pool_width = pool_param['pool_width']
    stride = pool_param['stride']
    N, C, H, W = x.shape
    H_prime = int(1 + (H-pool_height)/stride)
    W_prime = int(1 + (W-pool_width)/stride)

    out = np.zeros((N, C, H_prime, W_prime), dtype=x.dtype)
    for n in range(N):
        for c in range(C):
            for i in range(H_prime):
                for j in range(W_prime):
                    out[n, c, i, j] = np.max(x[n, c, i*stride:i*stride+pool_height, j*stride:j*stride+pool_width])


    cache = (x, pool_param)
    return out, cache


##Finished
def max_pool_backward_naive(dout, cache):
    """
    A naive implementation of the backward pass for a max-pooling layer.

    Inputs:
    - dout: Upstream derivatives
    - cache: A tuple of (x, pool_param) as in the forward pass.

    Returns:
    - dx: Gradient with respect to x
    """
    dx = None
    x, pool_param = cache
    N, C, H, W = x.shape
    pool_height = pool_param['pool_height']
    pool_width = pool_param['pool_width']
    stride = pool_param['stride']
    _, _, H_prime, W_prime = dout.shape

    dx = np.zeros_like(x, dtype=x.dtype)


    for n in range(N):
        for c in range(C):
            for i in range(H_prime):
                for j in range(W_prime):
                    mask = x[n,c,i*stride:i*stride+pool_height,j*stride:j*stride+pool_width]
                    dx[n,c,i*stride:i*stride+pool_height,j*stride:j*stride+pool_width] += dout[n,c,i,j]*(mask >= mask.max())

    return dx


##Finished
def spatial_batchnorm_forward(x, gamma, beta, bn_param):
    """
    Computes the forward pass for spatial batch normalization.

    Inputs:
    - x: Input data of shape (N, C, H, W)
    - gamma: Scale parameter, of shape (C,)
    - beta: Shift parameter, of shape (C,)
    - bn_param: Dictionary with the following keys:
      - mode: 'train' or 'test'; required
      - eps: Constant for numeric stability
      - momentum: Constant for running mean / variance. momentum=0 means that
        old information is discarded completely at every time step, while
        momentum=1 means that new information is never incorporated. The
        default of momentum=0.9 should work well in most situations.
      - 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: Output data, of shape (N, C, H, W)
    - cache: Values needed for the backward pass
    """
    out, cache = None, None

    x_transpose = x.transpose((0,2,3,1))
    x_flat = np.reshape(x_transpose, (-1, x_transpose.shape[3]))
    out, cache = batchnorm_forward(x_flat, gamma, beta, bn_param)
    out = np.reshape(out, x_transpose.shape)
    out = out.transpose((0,3,1,2))

    return out, cache


##Finished
def spatial_batchnorm_backward(dout, cache):
    """
    Computes the backward pass for spatial batch normalization.

    Inputs:
    - dout: Upstream derivatives, of shape (N, C, H, W)
    - cache: Values from the forward pass

    Returns a tuple of:
    - dx: Gradient with respect to inputs, of shape (N, C, H, W)
    - dgamma: Gradient with respect to scale parameter, of shape (C,)
    - dbeta: Gradient with respect to shift parameter, of shape (C,)
    """
    dx, dgamma, dbeta = None, None, None


    dout_transpose = dout.transpose((0,2,3,1))
    dout_flat = np.reshape(dout_transpose, (-1, dout.shape[1]))
    dx, dgamma, dbeta = batchnorm_backward(dout_flat, cache)
    dx = dx.reshape(*dout_transpose.shape)
    dx = dx.transpose((0,3,1,2))

    return dx, dgamma, dbeta


##Finished
def spatial_groupnorm_forward(x, gamma, beta, G, gn_param):
    """
    Computes the forward pass for spatial group normalization.
    In contrast to layer normalization, group normalization splits each entry
    in the data into G contiguous pieces, which it then normalizes independently.
    Per feature shifting and scaling are then applied to the data, in a manner identical to that of batch normalization and layer normalization.

    Inputs:
    - x: Input data of shape (N, C, H, W)
    - gamma: Scale parameter, of shape (C,)
    - beta: Shift parameter, of shape (C,)
    - G: Integer mumber of groups to split into, should be a divisor of C
    - gn_param: Dictionary with the following keys:
      - eps: Constant for numeric stability

    Returns a tuple of:
    - out: Output data, of shape (N, C, H, W)
    - cache: Values needed for the backward pass
    """
    out, cache = None, None
    eps = gn_param.get('eps', 1e-5)

    N, C, H, W = x.shape
    x = np.reshape(x, [N, G, C//G, H, W])

    mean = np.mean(x, axis=(2,3,4), keepdims=True) #(N, G)
    var = np.var(x, axis=(2,3,4), keepdims=True)

    x_estimate = (x-mean)/np.sqrt(var+eps)
    x_estimate = np.reshape(x_estimate, [N, C, H, W])

    out = x_estimate*gamma + beta

    temp = 1 / np.sqrt(var + eps)
    cache = (x, x_estimate, G, temp, mean, gamma)
    return out, cache


##Finished
def spatial_groupnorm_backward(dout, cache):
    """
    Computes the backward pass for spatial group normalization.

    Inputs:
    - dout: Upstream derivatives, of shape (N, C, H, W)
    - cache: Values from the forward pass

    Returns a tuple of:
    - dx: Gradient with respect to inputs, of shape (N, C, H, W)
    - dgamma: Gradient with respect to scale parameter, of shape (C,)
    - dbeta: Gradient with respect to shift parameter, of shape (C,)
    """
    dx, dgamma, dbeta = None, None, None

    x, x_estimate, G, temp, mean, gamma = cache

    N, C, H, W = x_estimate.shape

    dgamma = np.sum(dout*x_estimate, axis=(0,2,3), keepdims=True)
    dbeta = np.sum(dout, axis=(0,2,3), keepdims=True)

    dy_xhat = dout*gamma

    ##x = np.reshape(x, [N, G, C//G, H, W])
    dy_x1 = dy_xhat.reshape([N, G, C//G, H, W])*temp

    dy_vari = dy_xhat.reshape([N, G, C//G, H, W])*(x-mean)*(-1/2)*(temp**3)
    dy_var = np.sum(dy_vari, axis=(2,3,4), keepdims=True) #(N, D)
    D = x.shape[2]*x.shape[3]*x.shape[4]
    dy_x2 = 2*(x-mean)*(1/D)*dy_var

    dy_mean = np.sum(-1*dy_x1, axis=(2,3,4), keepdims=True) + dy_var*(1/D)*(-2)*np.sum(x-mean, axis=(2,3,4), keepdims=True)
    dy_x3 = dy_mean/D

    dx = dy_x1 + dy_x2 + dy_x3
    dx = np.reshape(dx, [N,C,H,W])

    return dx, dgamma, dbeta


def svm_loss(x, y):
    """
    Computes the loss and gradient using for multiclass SVM classification.

    Inputs:
    - x: Input data, of shape (N, C) where x[i, j] is the score for the jth
      class for the ith input.
    - y: Vector of labels, of shape (N,) where y[i] is the label for x[i] and
      0 <= y[i] < C

    Returns a tuple of:
    - loss: Scalar giving the loss
    - dx: Gradient of the loss with respect to x
    """
    N = x.shape[0]
    correct_class_scores = x[np.arange(N), y]
    margins = np.maximum(0, x - correct_class_scores[:, np.newaxis] + 1.0)
    margins[np.arange(N), y] = 0
    loss = np.sum(margins) / N
    num_pos = np.sum(margins > 0, axis=1)
    dx = np.zeros_like(x)
    dx[margins > 0] = 1
    dx[np.arange(N), y] -= num_pos
    dx /= N
    return loss, dx


def softmax_loss(x, y):
    """
    Computes the loss and gradient for softmax classification.

    Inputs:
    - x: Input data, of shape (N, C) where x[i, j] is the score for the jth
      class for the ith input.
    - y: Vector of labels, of shape (N,) where y[i] is the label for x[i] and
      0 <= y[i] < C

    Returns a tuple of:
    - loss: Scalar giving the loss
    - dx: Gradient of the loss with respect to x
    """
    shifted_logits = x - np.max(x, axis=1, keepdims=True)
    Z = np.sum(np.exp(shifted_logits), axis=1, keepdims=True)
    log_probs = shifted_logits - np.log(Z)
    probs = np.exp(log_probs)
    N = x.shape[0]
    loss = -np.sum(log_probs[np.arange(N), y]) / N
    dx = probs.copy()
    dx[np.arange(N), y] -= 1
    dx /= N
    return loss, dx



##6/16/2018
