diff --git a/assignment/a1/answers b/assignment/a1/answers
index eef7736..e03e7a1 100644
--- a/assignment/a1/answers
+++ b/assignment/a1/answers
@@ -20,10 +20,10 @@
 # ------------------------------------------------------------------
 
 # Question 1.1 (/1): What are the dimensions of W?  (Hint... don't change the dimensionality of the answer.)
-neural_network_basics_a_1_1: [d0]
+neural_network_basics_a_1_1: [1]
 
 # Question 1.2 (/1): What are the dimensions of b?  (Hint... don't change the dimensionality of the answer.)
-neural_network_basics_a_1_2: [d0]
+neural_network_basics_a_1_2: [1]
 
 
 # ------------------------------------------------------------------
@@ -31,16 +31,16 @@ neural_network_basics_a_1_2: [d0]
 # ------------------------------------------------------------------
 
 # Question 1 (/1): What are the dimensions of W?
-neural_network_basics_b_1: [d0]
+neural_network_basics_b_1: [11,1]
 
 # Question 2 (/1): What are the dimensions of b?
-neural_network_basics_b_2: [d0]
+neural_network_basics_b_2: [1,1]
 
 # Question 3 (/1): What are the dimensions of x?
-neural_network_basics_b_3: [d0, d1]
+neural_network_basics_b_3: [30,11]
 
 # Question 4 (/1): What are the dimensions of z?
-neural_network_basics_b_4: [d0]
+neural_network_basics_b_4: [30,1]
 
 
 # ------------------------------------------------------------------
@@ -48,10 +48,10 @@ neural_network_basics_b_4: [d0]
 # ------------------------------------------------------------------
 
 # Question 1 (/2): What is the probability of the positive class for [0, 0, 0, 0, 5]?
-neural_network_basics_c_1: 0.00000
+neural_network_basics_c_1: 0.50000
 
 # Question 2 (/2): What is the cross entropy loss (Base 2) if the second example is positive?
-neural_network_basics_c_2: 0
+neural_network_basics_c_2: 1.0000
 
 
 # ------------------------------------------------------------------
@@ -59,10 +59,10 @@ neural_network_basics_c_2: 0
 # ------------------------------------------------------------------
 
 # Question 1 (/2): What is the probability of the third example in the batch?
-neural_network_basics_d_1: 0.00000
+neural_network_basics_d_1: 0.36920
 
 # Question 2 (/2): What is the cross-entropy loss if its label is negative?
-neural_network_basics_d_2: 0.00000
+neural_network_basics_d_2: 0.66500
 
 
 # ------------------------------------------------------------------
@@ -70,16 +70,16 @@ neural_network_basics_d_2: 0.00000
 # ------------------------------------------------------------------
 
 # Question 1 (/2): What is the probability of the middle class?
-neural_network_basics_e_1: 0.00000
+neural_network_basics_e_1: 0.11730
 
 # Question 2 (/2): What is the cross-entropy loss (log base 2) if the correct class is the last (z=8)?
-neural_network_basics_e_2: 0.00000
+neural_network_basics_e_2: 0.20620
 
 # Question 3.1 (/2): What are the dimensions of W3 above if it were a three class problem instead of a binary one?
-neural_network_basics_e_3_1: [d0, d1]
+neural_network_basics_e_3_1: [10,3]
 
 # Question 3.2 (/2): What is the dimension of b3 above if it were a three class problem?
-neural_network_basics_e_3_2: [d0]
+neural_network_basics_e_3_2: [3]
 
 
 
@@ -98,19 +98,19 @@ neural_network_basics_e_3_2: [d0]
 tensorflow_1_1: 0
 
 # Question 2 (/2): What's the derivative of relu(z) with respect to z if z = 5
-tensorflow_1_2: 0
+tensorflow_1_2: 5
 
 # Question 3 (/2): Why do you still use a sigmoid at the top of the binary classification network?
 # (This question is multiple choice.  Delete all but the correct answer).
 tensorflow_1_3: 
  - Its range matches what is allowed for a probability.
- - Sigmoid is convenient, but you lose nothing by using a Relu or Tanh
+
 
 # Question 4 (/2): For the sequential model, what is the minimum number of hidden layers with the same number of neurons in each (n where n > 5) you can get away with and still achieve the desired loss on the training set?
-tensorflow_1_4: 0
+tensorflow_1_4: 1
 
 # Question 5 (/2): What is the smallest number of neurons (n) you can use in a layer in the network with a large number of layers (layers > 3) and still get the desired loss on the training set? (Assume all layers have the same number of neurons .)
-tensorflow_1_5: 0
+tensorflow_1_5: 8
 
 # Question 6 (/2): What is the accuracy score you get after training the functional model for 10 epochs?
-tensorflow_1_6: 0.00000
+tensorflow_1_6: 0.99650