Error in Identity block in week 2

Course Q&A Deep Learning Specialization Convolutional Neural Networks
1

Returns: X – output of the identity block, tensor of shape (n_H, n_W, n_C) “”" # Retrieve Filters F1, F2, F3 = filters # Save the input value. You’ll need this later to add back to the main path. X_shortcut = X # First component of main path X = Conv2D(filters = F1, kernel_size = 1, strides = (1,1), padding = ‘valid’, kernel_initializer = initializer(seed=0))(X) X = BatchNormalization(axis = 3)(X, training = training) # Default axis X = Activation(‘relu’)(X) ### START CODE HERE ## Second component of main path (≈3 lines) X = Conv2D(filters = F2, kernel_size = (f, f), strides = (1,1), padding = ‘same’,kernel_initializer = initializer(seed=0))(X) X = BatchNormalization(axis = 3)(X) X = Activation(‘relu’)(X)​ ## Third component of main path (≈2 lines) X = Conv2D(filters = F3, kernel_size = (1, 1), strides = (1,1), padding = ‘valid’,kernel_initializer = initializer(seed=0))(X) X = BatchNormalization(axis = 3)(X)​ ## Final step: Add shortcut value to main path, and pass it through a RELU activation (≈2 lines) X = Add()([X, X_shortcut]) X = Activation(‘relu’)(X) ### END CODE HERE​ return XIn [19]:np.random.seed(1)X1 = np.ones((1, 4, 4, 3)) * -1X2 = np.ones((1, 4, 4, 3)) * 1X3 = np.ones((1, 4, 4, 3)) * 3​X = np.concatenate((X1, X2, X3), axis = 0).astype(np.float32)​A3 = identity_block(X, f=2, filters=[4, 4, 3], initializer=lambda seed=0:constant(value=1), training=False)print(’\033[1mWith training=False\033[0m\n’)A3np = A3.numpy()print(np.around(A3.numpy()[:,(0,-1),:,:].mean(axis = 3), 5))resume = A3np[:,(0,-1),:,:].mean(axis = 3)print(resume[1, 1, 0])​print(’\n\033[1mWith training=True\033[0m\n’)np.random.seed(1)A4 = identity_block(X, f=2, filters=[3, 3, 3], initializer=lambda seed=0:constant(value=1), training=True)print(np.around(A4.numpy()[:,(0,-1),:,:].mean(axis = 3), 5))​public_tests.identity_block_test(identity_block)With training=False

[[[ 0. 0. 0. 0. ]
[ 0. 0. 0. 0. ]]

[[192.71234 192.71234 192.71234 96.85617]
[ 96.85617 96.85617 96.85617 48.92808]]

[[578.1371 578.1371 578.1371 290.5685 ]
[290.5685 290.5685 290.5685 146.78426]]]
96.85617

With training=True

[[[ 0. 0. 0. 0. ]
[ 0. 0. 0. 0. ]]

[[ 1. 1. 1. 1. ]
[ 1. 1. 1. 1. ]]

[[47.04585 47.04585 47.04585 25.02293]
[25.02293 25.02293 25.02293 14.01146]]]
---------------------------------------------------------------------------AssertionError Traceback (most recent call last)
in 22 print(np.around(A4.numpy()[:,(0,-1),:,:].mean(axis = 3), 5)) 23
—> 24 public_tests.identity_block_test(identity_block)

/tf/W2A1/public_tests.py in identity_block_test(target) 54 [0.37394285, 0.37394285, 0.37394285, 0.37394285]],
55 [[3.2379014, 4.1394243, 4.1394243, 3.2379014 ],
—> 56 [3.2379014, 4.1394243, 4.1394243, 3.2379014 ]]]), atol = 1e-5 ), “Wrong values with training=True”
57
58 print(colored(“All tests passed!”, “green”))

AssertionError: Wrong values with training=True

I dont understand why i am getting this error, can someone please clarify

2

There appears to be an error in your code.
But I cannot read your code, the formatting is too confusing.
Maybe you could post a screen capture image instead.

4

Thanks. While I look at your code, can you please delete it from your message?
We need to keep tidy with the course Honor Code (which says students should not share their code).

5

Yeah did it, please let me know if you find any error

8

You need to pass (X, training=training) to all the BatchNorm layers. Not just the first one.

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