卷积神经网络代码 - 前向传递

如何解决卷积神经网络代码 - 前向传递

代码正在运行。但是，存在一些差异，我无法获得所需的输出量。有人可以帮忙吗？The actual output should be as given in image

A_prev -- 上一层的输出激活，numpy 数组的形状 (m,n_H_prev,n_W_prev,n_C_prev)

W -- 权重，形状的 numpy 数组 (f,f,n_C_prev,n_C)

b -- 偏差，形状为 (1,1,n_C) 的 numpy 数组

hparameters -- 包含“stride”和“pad”的python字典

返回： Z -- conv 输出，形状为 (m,n_H,n_W,n_C) 的 numpy 数组

np.random.seed(1)
A_prev = np.random.randn(10,5,7,4)
W = np.random.randn(3,3,4,8)
b = np.random.randn(1,8)
hparameters = {"pad" : 1,"stride": 2}

### START CODE HERE ###
# Retrieve dimensions from A_prev's shape   
(m,n_C_prev) = A_prev.shape
    
# Retrieve dimensions from W's shape 
(f,n_C) = W.shape
    
# Retrieve information from "hparameters" 
stride = hparameters['stride']
pad = hparameters['pad']


# Compute the dimensions of the CONV output volume using the formula.
n_H = int((n_H_prev+2*pad-f)/stride)+1
n_W = int((n_W_prev+2*pad-f)/stride)+1

    
# Initialize the output volume Z with zeros.
Z = np.zeros(shape=(m,n_C))
    
# Create A_prev_pad by padding A_prev
A_prev_pad = np.pad(A_prev,((0,0),(pad,pad),(0,0)),mode = 'constant',constant_values = (0,0))
    
for i in range(m):               # loop over the batch of training examples
    a_prev_pad = 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" 
        vert_start = h
        vert_end = h+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" 
            horiz_start = w
            horiz_end = w+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  
                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. 
                weights = W[:,c]
                biases = b[:,c]
                p = np.multiply(weights,a_slice_prev)
                Z[i,h,w,c] = np.sum(p) + float(biases)
               
                                        
### END CODE HERE ###