CoCalc -- resnets_utils.cpython-36.pyc

GitHub Repository: y33-j3T/Coursera-Deep-Learning
Path: blob/master/Convolutional Neural Networks/dummy/__pycache__/resnets_utils.cpython-36.pyc
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    Train the network.
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    Construct a queued batch of images and labels.
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    Creates a list of random minibatches from (X, Y)
    
    Arguments:
    X -- input data, of shape (input size, number of examples) (m, Hi, Wi, Ci)
    Y -- true "label" vector (containing 0 if cat, 1 if non-cat), of shape (1, number of examples) (m, n_y)
    mini_batch_size - size of the mini-batches, integer
    seed -- this is only for the purpose of grading, so that you're "random minibatches are the same as ours.
    
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    Implements the forward propagation for the model: LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SOFTMAX
    
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