Outline of the Assignment

  • Convolution functions, including:
    • Zero Padding
    • Convolve window
    • Convolution forward
  • Pooling functions, including:
    • Pooling forward
    • Create mask
    • Distribute value

Convolution function

Zero Padding

Firstly, we need to implement a function called Zero Padding, the code is as below.

def zero_pad(X, pad):
    X_pad = np.pad(X, ((0,0), (pad, pad), (pad, pad), (0,0)), mode='constant', constant_values = (0,0))
    return X_pad

We can see that with using numpy, it is very easy to be implement.

Single step of convolution

Secondly, we need to implement the single step of convolution.

def conv_single_step(a_slice_prev, W, b):
    """
    Apply one filter defined by parameters W on a single slice (a_slice_prev) of the output activation 
    of the previous layer.
    
    Arguments:
    a_slice_prev -- slice of input data of shape (f, f, n_C_prev)
    W -- Weight parameters contained in a window - matrix of shape (f, f, n_C_prev)
    b -- Bias parameters contained in a window - matrix of shape (1, 1, 1)
    
    Returns:
    Z -- a scalar value, the result of convolving the sliding window (W, b) on a slice x of the input data
    """
    # Element-wise product between a_slice_prev and W. Do not add the bias yet.
    s = np.multiply(W, a_slice_prev)
    # Sum over all entries of the volume s.
    Z = np.sum(s)
    # Add bias b to Z. Cast b to a float() so that Z results in a scalar value.
    Z = np.add(Z, float(b))

    return Z

Convolutional Neural Networks - Forward pass

Then, do the forward pass part, which is using the function before to do the whole layer.

Where:

$$ n_H = \lfloor \frac{n_{H_{prev}} - f + 2 \times pad}{stride} \rfloor +1 $$ $$ n_W = \lfloor \frac{n_{W_{prev}} - f + 2 \times pad}{stride} \rfloor +1 $$ $$ n_C = \text{number of filters used in the convolution}$$

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) = np.shape(A_prev)
    
    # Retrieve dimensions from W's shape (≈1 line)
    (f, f, n_C_prev, n_C) = np.shape(W)
    
    # Retrieve information from "hparameters" (≈2 lines)
    stride = hparameters['stride']
    pad = hparameters['pad']
    
    # 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 = int((n_H_prev - f + 2 * pad)/stride) + 1
    n_W = int((n_W_prev - f + 2 * pad)/stride) + 1
    
    # Initialize the output volume Z with zeros. (≈1 line)
    Z = np.zeros((m, n_H, n_W, n_C))
    
    # Create A_prev_pad by padding A_prev
    A_prev_pad = zero_pad(X=A_prev, pad=pad)
    
    for i in range(m):               # loop over the batch of training examples
        a_prev_pad = np.squeeze(A_prev_pad[i, :, :, :])               # Select ith training example's padded activation
        for h in range(n_H):           # loop over vertical axis of the output volume
            # Find the vertical start and end of the current "slice" (≈2 lines)
            vert_start = h * stride
            vert_end = h * stride + f
            
            for w in range(n_W):       # loop over horizontal axis of the output volume
                # Find the horizontal start and end of the current "slice" (≈2 lines)
                horiz_start = w * stride
                horiz_end = w * stride + f
                
                for c in range(n_C):   # 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 = a_prev_pad[vert_start:vert_end, horiz_start:horiz_end, :]
                    
                    # Convolve the (3D) slice with the correct filter W and bias b, to get back one output neuron. (≈3 line)
                    weights = W[: ,: , :, c]
                    biases = b[:, :, :, c]
                    Z[i, h, w, c] = conv_single_step(a_slice_prev=a_slice_prev, b=biases, W=weights)
                                  
    
    # Making sure your output shape is correct
    assert(Z.shape == (m, n_H, n_W, n_C))
    
    # Save information in "cache" for the backprop
    cache = (A_prev, W, b, hparameters)
    
    return Z, cache

DON’T FORGET TO MULTIPLY STRIDE!!!

Pooling Layer

def pool_forward(A_prev, hparameters, mode = "max"):
    """
    Implements the forward pass of the pooling layer
    
    Arguments:
    A_prev -- Input data, numpy array of shape (m, n_H_prev, n_W_prev, n_C_prev)
    hparameters -- python dictionary containing "f" and "stride"
    mode -- the pooling mode you would like to use, defined as a string ("max" or "average")
    
    Returns:
    A -- output of the pool layer, a numpy array of shape (m, n_H, n_W, n_C)
    cache -- cache used in the backward pass of the the input and h pooling layer, containsparameters 
    """
    
    # Retrieve dimensions from the input shape
    (m, n_H_prev, n_W_prev, n_C_prev) = A_prev.shape
    
    # Retrieve hyperparameters from "hparameters"
    f = hparameters["f"]
    stride = hparameters["stride"]
    
    # Define the dimensions of the output
    n_H = int(1 + (n_H_prev - f) / stride)
    n_W = int(1 + (n_W_prev - f) / stride)
    n_C = n_C_prev
    
    # Initialize output matrix A
    A = np.zeros((m, n_H, n_W, n_C))              
    
    for i in range(m):                         # loop over the training examples
        for h in range(n_H):                     # loop on the vertical axis of the output volume
            # Find the vertical start and end of the current "slice" (≈2 lines)
            vert_start = h * stride 
            vert_end = h * stride + f
            
            for w in range(n_W):                 # loop on the horizontal axis of the output volume
                # Find the vertical start and end of the current "slice" (≈2 lines)
                horiz_start = w * stride
                horiz_end = w * stride + f
                
                for c in range (n_C):            # loop over the channels of the output volume
                    
                    # Use the corners to define the current slice on the ith training example of A_prev, channel c. (≈1 line)
                    a_prev_slice = A_prev[i, vert_start:vert_end, horiz_start:horiz_end, c]
                    
                    # Compute the pooling operation on the slice. 
                    # Use an if statement to differentiate the modes. 
                    # Use np.max and np.mean.
                    if mode == "max":
                        A[i, h, w, c] = np.max(a_prev_slice)
                    elif mode == "average":
                        A[i[i, h, w, c] = np.mean(a_prev_slice)
    
    # Store the input and hparameters in "cache" for pool_backward()
    cache = (A_prev, hparameters)
    
    # Making sure your output shape is correct
    assert(A.shape == (m, n_H, n_W, n_C))
    
    return A, cache

The code here just like the conv_forward.