Andrew Ng's DL Course CNN - Foward Prop Template

def conv_forward(A_prev, W, b, hparameters):
    """
    Implements the forward propagation for a convolution function
    
    Arguments:
    A_prev -- output activations of the previous layer, 
        numpy array of shape (m, n_H_prev, n_W_prev, n_C_prev)
    W -- Weights, numpy array of shape (f, f, n_C_prev, n_C)
    b -- Biases, numpy array of shape (1, 1, 1, n_C)
    hparameters -- python dictionary containing "stride" and "pad"
        
    Returns:
    Z -- conv output, numpy array of shape (m, n_H, n_W, n_C)
    cache -- cache of values needed for the conv_backward() function
    """
    
    # Retrieve dimensions from A_prev's shape (≈1 line)  
    # (m, n_H_prev, n_W_prev, n_C_prev) = None
    
    # Retrieve dimensions from W's shape (≈1 line)
    # (f, f, n_C_prev, n_C) = None
    
    # Retrieve information from "hparameters" (≈2 lines)
    # stride = None
    # pad = None
    
    # Compute the dimensions of the CONV output volume using the formula given above. 
    # Hint: use int() to apply the 'floor' operation. (≈2 lines)
    # n_H = None
    # n_W = None
    
    # Initialize the output volume Z with zeros. (≈1 line)
    # Z = None
    
    # Create A_prev_pad by padding A_prev
    # A_prev_pad = None
    
    # for i in range(None):               # loop over the batch of training examples
        # a_prev_pad = None               # Select ith training example's padded activation
        # for h in range(None):           # loop over vertical axis of the output volume
            # Find the vertical start and end of the current "slice" (≈2 lines)
            # vert_start = None
            # vert_end = None
            
            # for w in range(None):       # loop over horizontal axis of the output volume
                # Find the horizontal start and end of the current "slice" (≈2 lines)
                # horiz_start = None
                # horiz_end = None
                
                # for c in range(None):   # loop over channels (= #filters) of the output volume
                                        
                    # Use the corners to define the (3D) slice of a_prev_pad (See Hint above the cell). (≈1 line)
                    # a_slice_prev = None
                    
                    # Convolve the (3D) slice with the correct filter W and bias b, to get back one output neuron. (≈3 line)
                    # weights = None
                    # biases = None
                    # Z[i, h, w, c] = None