Forward Propagation with Dropout - neural-network

Question

I am working through Andrew Ng new deep learning Coursera course.
We are implementing the following code :
def forward_propagation_with_dropout(X, parameters, keep_prob = 0.5):
np.random.seed(1)
# retrieve parameters
W1 = parameters["W1"]
b1 = parameters["b1"]
W2 = parameters["W2"]
b2 = parameters["b2"]
W3 = parameters["W3"]
b3 = parameters["b3"]
# LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID
Z1 = np.dot(W1, X) + b1
A1 = relu(Z1)
### START CODE HERE ### (approx. 4 lines) # Steps 1-4 below correspond to the Steps 1-4 described above.
D1 = np.random.rand(*A1.shape) # Step 1: initialize matrix D1 = np.random.rand(..., ...)
D1 = (D1 < 0.5) # Step 2: convert entries of D1 to 0 or 1 (using keep_prob as the threshold)
A1 = A1*D1 # Step 3: shut down some neurons of A1
A1 = A1 / keep_prob # Step 4: scale the value of neurons that haven't been shut down
### END CODE HERE ###
Z2 = np.dot(W2, A1) + b2
A2 = relu(Z2)
### START CODE HERE ### (approx. 4 lines)
D2 =np.random.rand(*A2.shape) # Step 1: initialize matrix D2 = np.random.rand(..., ...)
D2 = (D2 < 0.5) # Step 2: convert entries of D2 to 0 or 1 (using keep_prob as the threshold)
A2 = A2 * D2 # Step 3: shut down some neurons of A2
A2 = A2 / keep_prob # Step 4: scale the value of neurons that haven't been shut down
### END CODE HERE ###
Z3 = np.dot(W3, A2) + b3
A3 = sigmoid(Z3)
cache = (Z1, D1, A1, W1, b1, Z2, D2, A2, W2, b2, Z3, A3, W3, b3)
return A3, cache
Calling:
X_assess, parameters = forward_propagation_with_dropout_test_case()
A3, cache = forward_propagation_with_dropout(X_assess, parameters, keep_prob = 0.7)
print ("A3 = " + str(A3))
My output was :
A3 = [[ 0.36974721 0.49683389 0.04565099 0.49683389 0.36974721]]
The expected output should be :
A3 = [[ 0.36974721 0.00305176 0.04565099 0.49683389 0.36974721]]
Only one number difference. Any ideas why ?
I think it is because of the way I shaped D1 and D2.

Answer 1

I think it is because you put D1 = (D1 < 0.5) and D2 = (D2 < 0.5)
You need to put "keep_prob" instead of 0.5