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View Code? Open in Web Editor NEWDeep Learning Specialization by Andrew Ng on Coursera - My Completed Coursework Repo - All 5 Courses
Deep Learning Specialization by Andrew Ng on Coursera - My Completed Coursework Repo - All 5 Courses
对于生成图片的宽度和高度的求解,要用取整函数:
# Compute the dimensions of the CONV output volume using the formula given above.
# Hint: use int() to floor. (≈2 lines)
n_H = int(np.ceil((n_H_prev + 2*pad - f)/stride))+1
n_W = int(np.ceil((n_W_prev + 2*pad - f)/stride))+1
函数conv_single_step()实现中:
应该是 W*X 先求和,在加偏置b,而不是每一个wi * xi 都加一个b
# START CODE HERE ### (≈ 2 lines of code)
# Element-wise product between a_slice and W. Do not add the bias yet.
s = a_slice_prev * W
# 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 = Z + float(b)
# ## END CODE HERE ###
这样应该才对
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