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import numpy as np  # import numpy library
from util.paramInitializer import initialize_parameters  # import function to initialize weights and biases


class LinearLayer:
    """
        This Class implements all functions to be executed by a linear layer
        in a computational graph

        Args:

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import keras

# import keras_retinanet
from object_detector_retinanet.keras_retinanet import models
from object_detector_retinanet.keras_retinanet.utils.image import read_image_bgr, preprocess_image, resize_image
from object_detector_retinanet.keras_retinanet.utils.visualization import draw_box, draw_caption
from object_detector_retinanet.keras_retinanet.utils.colors import label_color

# import for EM Merger and viz
from object_detector_retinanet.keras_retinanet.utils import EmMerger

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costs = []  # initially empty list, this will store all the costs after a certain number of epochs

# Start training
for epoch in range(number_of_epochs):

    # ------------------------- forward-prop -------------------------
    Z1.forward(X_train)
    A1.forward(Z1.Z)
    
    # ---------------------- Compute Cost ----------------------------

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# define training constants
learning_rate = 1
number_of_epochs = 5000

np.random.seed(48) # set seed value so that the results are reproduceable
                   # (weights will now be initailzaed to the same pseudo-random numbers, each time)

# Our network architecture has the shape: 
#                       (input)--> [Linear->Sigmoid] -->(output)  

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def compute_keras_like_bce_cost(Y, P_hat, from_logits=False):
    """
    This function computes the Binary Cross-Entropy(stable_bce) Cost function the way Keras
    implements it. Accepting either probabilities(P_hat) from the sigmoid neuron or values direct
    from the linear node(Z)

    Args:
        Y: labels of data
        P_hat: Probabilities from sigmoid function
        from_logits: flag to check if logits are being provided or not(Default: False)

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def compute_stable_bce_cost(Y, Z):
    """
    This function computes the "Stable" Binary Cross-Entropy(stable_bce) Cost and returns the Cost and its
    derivative w.r.t Z_last(the last linear node) .
    The Stable Binary Cross-Entropy Cost is defined as:
    => (1/m) * np.sum(max(Z,0) - ZY + log(1+exp(-|Z|)))
    Args:
        Y: labels of data
        Z: Values from the last linear node

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def compute_bce_cost(Y, P_hat):
    """
    This function computes Binary Cross-Entropy(bce) Cost and returns the Cost and its
    derivative.
    This function uses the following Binary Cross-Entropy Cost defined as:
    => (1/m) * np.sum(-Y*np.log(P_hat) - (1-Y)*np.log(1-P_hat))

    Args:
        Y: labels of data
        P_hat: Estimated output probabilities from the last layer, the output layer

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costs = [] # initially empty list, this will store all the costs after a certian number of epochs

# Start training
for epoch in range(number_of_epochs):
    
    # ------------------------- forward-prop -------------------------
    Z1.forward(X_train)
    A1.forward(Z1.Z)
    
    Z2.forward(A1.A)

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# define training constants
learning_rate = 1
number_of_epochs = 5000

np.random.seed(48) # set seed value so that the results are reproduceable
                  # (weights will now be initailzaed to the same pseudo-random numbers, each time)


# Our network architecture has the shape: 
#                   (input)--> [Linear->Sigmoid] -> [Linear->Sigmoid] -->(output)  

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def compute_cost(Y, Y_hat):
    """
    This function computes and returns the Cost and its derivative.
    The is function uses the Squared Error Cost function -> (1/2m)*sum(Y - Y_hat)^.2

    Args:
        Y: labels of data
        Y_hat: Predictions(activations) from a last layer, the output layer

    Returns: