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GitHub Repository: y33-j3T/Coursera-Deep-Learning
 Path: blob/master/Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/week7/__pycache__/improv_utils.cpython-36.pyc
Views: 13377
3

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    Creates a list of random minibatches from (X, Y)
    
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    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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    n_y -- scalar, number of classes (from 0 to 5, so -> 6)
    
    Returns:
    X -- placeholder for the data input, of shape [n_x, None] and dtype "float"
    Y -- placeholder for the input labels, of shape [n_y, None] and dtype "float"
    
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                        b2 : [12, 1]
                        W3 : [6, 12]
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    Implements a three-layer tensorflow neural network: LINEAR->RELU->LINEAR->RELU->LINEAR->SOFTMAX.
    
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    Y_train -- test set, of shape (output size = 6, number of training examples = 1080)
    X_test -- training set, of shape (input size = 12288, number of training examples = 120)
    Y_test -- test set, of shape (output size = 6, number of test examples = 120)
    learning_rate -- learning rate of the optimization
    num_epochs -- number of epochs of the optimization loop
    minibatch_size -- size of a minibatch
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