Hi,
I’m working with the convolutional forward prop and I’m getting this error:
25 s = np.multiply(a_slice_prev, W)
26 Z = np.sum(s)
—> 27 Z = Z + np.float(b)
28
29 # YOUR CODE ENDS HERE
TypeError: only size-1 arrays can be converted to Python scalars
It’s strange because the call conv_single_step(a_slice_prev, W[:,:,:,c], b) uses a function done and validated before as correct.
Where is the error?
Thxs!
Hey @Edu4rd , could you mention which week’s assignment is this ? Thanks.
Convolutional Model, Step by Step. The first one of the week 1 for this course.
Exercise 3.
Hi,
I’m really struggling with this exercise. I have been tinkering around, going back to the theory,…
I’m stuck with this error:
----> 8 Z, cache_conv = conv_forward(A_prev, W, b, hparameters)
9 print(“Z’s mean =\n”, np.mean(Z))
10 print(“Z[0,2,1] =\n”, Z[0, 2, 1])
in conv_forward(A_prev, W, b, hparameters)
105
106 # Z[i, h, w, c] = None
→ 107 Z[i,h,w,c] = conv_single_step(a_slice_prev, weights, biases)
108
109
in conv_single_step(a_slice_prev, W, b)
23 # Z = None
24 # YOUR CODE STARTS HERE
—> 25 s = np.multiply(a_slice_prev, W)
26 Z = np.sum(s)
27 Z = Z + np.float(b)
ValueError: operands could not be broadcast together with shapes (2,3,4) (3,3,4)
I would appreciate a hint on what is wrong. It seems like the last slice is not 3,3,4 and that’s causing the error.
Hello there!
I am not sure if it is ok to add to posts instead of creating a new one, but I am having exactly the same issue as @Edu4rd .
I also searched all similar comments and followed the advice provided by @TMosh here: Week 1 course 4 Programming Assignment 1 - #2 by TMosh (adding np. to the float).
Unfortunately, this did not work for me. I am also continuing to get the following message:
only size-1 arrays can be converted to Python scalars
However, the value I am getting back is already a scalar:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-22-182241fd5e53> in <module>
6 "stride": 2}
7
----> 8 Z, cache_conv = conv_forward(A_prev, W, b, hparameters)
9 print("Z's mean =\n", np.mean(Z))
10 print("Z[0,2,1] =\n", Z[0, 2, 1])
<ipython-input-21-7ac1ac8c5e0d> in conv_forward(A_prev, W, b, hparameters)
97
98 print(f'Z{i},{h},{w},{c}')
---> 99 print(f'conv_single_step func -> {conv_single_step(a_slice_prev, weights, biases)}')
100 Z[i, h, w, c] = conv_single_step(a_slice_prev, weights, biases)
101
<ipython-input-19-0dc5d6ead47d> in conv_single_step(a_slice_prev, W, b)
26 s = a_slice_prev * W
27 Z = np.sum(s, dtype=np.float)
---> 28 Z += np.float(b)
29
30 # YOUR CODE ENDS HERE
TypeError: only size-1 arrays can be converted to Python scalars
The value of Z at crashing time is:
conv_single_step func -> 1.5592957225103126
So why is the tester saying that the result of the function is a non-size-1 array?
(By the way, this previous function passed the tester before).
Thank you for any pointers. Also, I can create a different posting if this was the expectation.
The assert is being thrown by np.float(b) in conv_single_step().
This implies that ‘b’ isn’t a scalar.
The issue may be if your print statement at line 99 of conv_forward() is not passing the correct data shapes to conv_single_step().
What is the purpose of that print() statement in line 99? Just for debugging?
Hi @TMosh thanks for the reply. Yes, the print statement is only for debugging purposes.
I will take a look at the shapes…
Hi @TMosh , I checked the dimensions as you suggested and now I get at least a first result for Z.
However, I am still getting the “only size-1 arrays can be converted to Python scalars” error on the 2nd iteration:
TypeError Traceback (most recent call last)
<ipython-input-41-182241fd5e53> in <module>
6 "stride": 2}
7
----> 8 Z, cache_conv = conv_forward(A_prev, W, b, hparameters)
9 print("Z's mean =\n", np.mean(Z))
10 print("Z[0,2,1] =\n", Z[0, 2, 1])
<ipython-input-40-95aab82c1890> in conv_forward(A_prev, W, b, hparameters)
98 weights = W[ : , : , c : c+1 , c : c+1]
99 biases = b [ : , : , c : c+1 , c : c+1]
--> 100 print(f'conv_single_step func -> {conv_single_step(a_slice_prev, weights, biases)}')
101 Z[i, h, w, c] = conv_single_step(a_slice_prev, weights, biases)
102
<ipython-input-4-0dc5d6ead47d> in conv_single_step(a_slice_prev, W, b)
26 s = a_slice_prev * W
27 Z = np.sum(s, dtype=np.float)
---> 28 Z += np.float(b)
29
30 # YOUR CODE ENDS HERE
TypeError: only size-1 arrays can be converted to Python scalars
Maybe the problem is in the n_c vs n_c_prev dimensions. But I am stuck for several hours now. Any pointers would be appreciated (and the print is for debugging purposes only).
Thank you
Hi @TMosh , please ignore my comment. I posted a new thread. Thank you.
Looks like a problem in your code for weights and biases.
You’re just supposed to slice W and b by selecting ‘c’ in the last index.