Week 4 Programming Exercise: ValueError: shapes (4,5) and (4,4) not aligned: 5 (dim 1) != 4 (dim 0) #9
Description
# GRADED FUNCTION: L_model_forward
def L_model_forward(X, parameters):
"""
Implement forward propagation for the [LINEAR->RELU]*(L-1)->LINEAR->SIGMOID computation
Arguments:
X -- data, numpy array of shape (input size, number of examples)
parameters -- output of initialize_parameters_deep()
Returns:
AL -- last post-activation value
caches -- list of caches containing:
every cache of linear_activation_forward() (there are L-1 of them, indexed from 0 to L-1)
"""
caches = []
A = X
L = len(parameters) // 2 # number of layers in the neural network
#print(parameters)
# Implement [LINEAR -> RELU]*(L-1). Add "cache" to the "caches" list.
for l in range(1, L):
A_prev = A
### START CODE HERE ### (≈ 2 lines of code)
A, cache = linear_activation_forward(A_prev, parameters['W'+str(1)], parameters['b'+str(1)], activation='relu')
caches.append(cache)
### END CODE HERE ###
# Implement LINEAR -> SIGMOID. Add "cache" to the "caches" list.
### START CODE HERE ### (≈ 2 lines of code)
AL, cache = linear_activation_forward(A, parameters['W'+str(L)], parameters['b'+str(L)], activation='sigmoid')
caches.append(cache)
### END CODE HERE ###
assert(AL.shape == (1,X.shape[1]))
return AL, caches
And then:
X, parameters = L_model_forward_test_case_2hidden()
AL, caches = L_model_forward(X, parameters)
print("AL = " + str(AL))
print("Length of caches list = " + str(len(caches)))
Error:
ValueError: shapes (4,5) and (4,4) not aligned: 5 (dim 1) != 4 (dim 0)
Any dea how to fix this?