gradient for neural network

nnGrad.py

def nnGrad(nn_params, input_layer_size, hidden_layer_size, num_labels, X, y, lmbda):
    
    initial_theta1 = np.reshape(nn_params[:hidden_layer_size*(input_layer_size+1)], (hidden_layer_size, input_layer_size+1), 'F')
    initial_theta2 = np.reshape(nn_params[hidden_layer_size*(input_layer_size+1):], (num_labels, hidden_layer_size+1), 'F')
    y_d = pd.get_dummies(y.flatten())
    delta1 = np.zeros(initial_theta1.shape)
    delta2 = np.zeros(initial_theta2.shape)
    m = len(y)
    
    for i in range(X.shape[0]):
        ones = np.ones(1)
        a1 = np.hstack((ones, X[i]))
        z2 = a1 @ initial_theta1.T
        a2 = np.hstack((ones, sigmoid(z2)))
        z3 = a2 @ initial_theta2.T
        a3 = sigmoid(z3)

        d3 = a3 - y_d.iloc[i,:][np.newaxis,:]
        z2 = np.hstack((ones, z2))
        d2 = np.multiply(initial_theta2.T @ d3.T, sigmoidGrad(z2).T[:,np.newaxis])
        delta1 = delta1 + d2[1:,:] @ a1[np.newaxis,:]
        delta2 = delta2 + d3.T @ a2[np.newaxis,:]
        
    delta1 /= m
    delta2 /= m
    #print(delta1.shape, delta2.shape)
    delta1[:,1:] = delta1[:,1:] + initial_theta1[:,1:] * lmbda / m
    delta2[:,1:] = delta2[:,1:] + initial_theta2[:,1:] * lmbda / m
        
    return np.hstack((delta1.ravel(order='F'), delta2.ravel(order='F')))