Week 2 - assignement 1: convolutional block

Course Q&A Deep Learning Specialization Convolutional Neural Networks
13

@dexcrew I too have the same error. I tried hard rectifying it but all in vain. I am posting my code here could you plz take a quick look and give me the hint to sort out the issue

MAIN PATH
# First component of main path glorot_uniform(seed=0)
X = Conv2D(filters = F1, kernel_size = (1,1), strides = (s, s), padding='valid', kernel_initializer = initializer(seed=0))(X)
X = BatchNormalization(axis = 3)(X, training = training)
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, training = training)
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, training = training)

##### SHORTCUT PATH ##### (≈2 lines)
X_shortcut = Conv2D(filters = F3, kernel_size = (1,1), strides = (s,s), padding='valid', kernel_initializer =initializer(seed=0))(X)
X_shortcut = BatchNormalization(axis = 3)(X_shortcut, training = training)


# 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
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