Problem with type mismatch -- MethodError: no method matching

New to Julia
1

I have a script that has two functions with the following relationship:

function optimize(w::Array{Float64,2}, b::Float64, X::Array{Float64,2}, Y::Array{Int64,2};
num_iterations::Int64=2000, learning_rate::Float64=0.5, print_cost::Bool=false)

…
end

and

GRADED FUNCTION: model

function model(X_train::Array{Float64,2}, Y_train::Array{Int64,2}, X_test::Array{Float64,2}, Y_test::Array{Int64,2};
num_iterations::Int64 = 2000, learning_rate::Float64 = 0.5, print_cost::Bool = false)
“”"
Builds the logistic regression model by calling the function you’ve implemented previously

Arguments:
X_train -- training set represented by a numpy array of shape (num_px * num_px * 3, m_train)
Y_train -- training labels represented by a numpy array (vector) of shape (1, m_train)
X_test -- test set represented by a numpy array of shape (num_px * num_px * 3, m_test)
Y_test -- test labels represented by a numpy array (vector) of shape (1, m_test)
num_iterations -- hyperparameter representing the number of iterations to optimize the parameters
learning_rate -- hyperparameter representing the learning rate used in the update rule of optimize()
print_cost -- Set to true to print the cost every 100 iterations

Returns:
d -- dictionary containing information about the model.
"""

### START CODE HERE ###

# initialize parameters with zeros (≈ 1 line of code)
w,b = initialize_with_zeros(size(X_train)[1])

# Gradient descent (≈ 1 line of code)
println(typeof(w))
println(typeof(b))
println(typeof(X_train))
println(typeof(Y_train))
println(typeof(num_iterations))
println(typeof(learning_rate))
println(typeof(print_cost))
parameters, grads, costs = optimize(w, b, X_train, Y_train, num_iterations, learning_rate, print_cost)

…
end


I then call

> d = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations=2000, learning_rate=0.005, print_cost=true) 
to get the intermediate output and crash
> Array{Float64,2}
> Float64
> Array{Float64,2}
> Array{Int64,2}
> Int64
> Float64
> Bool
> MethodError: no method matching optimize(::Array{Float64,2}, ::Float64, ::Array{Float64,2}, ::Array{Int64,2}, ::Int64, ::Float64, ::Bool)
> Closest candidates are:
>   optimize(::Array{Float64,2}, ::Float64, ::Array{Float64,2}, ::Array{Int64,2}; num_iterations, learning_rate, print_cost) at In[24]:5
> 
> Stacktrace:
>  [1] #model#8(::Int64, ::Float64, ::Bool, ::Function, ::Array{Float64,2}, ::Array{Int64,2}, ::Array{Float64,2}, ::Array{Int64,2}) at .\In[28]:34
>  [2] (::getfield(Main, Symbol("#kw##model")))(::NamedTuple{(:num_iterations, :learning_rate, :print_cost),Tuple{Int64,Float64,Bool}}, ::typeof(model), ::Array{Float64,2}, ::Array{Int64,2}, ::Array{Float64,2}, ::Array{Int64,2}) at .\none:0
>  [3] top-level scope at In[29]:1

Can someone help me understand why the types match and the function template is not recognized?
2

When you declare a function with keyword arguments like this:

function foo(x; y = 10)
end

then you must provide the keywords when you call the function:

foo(1, y = 2)  # NOT: foo(1, 2)

By the way, it will be easier to read your code (and help you) if you format it correctly with backticks:

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3

Should be

optimize(w, b, X_train, Y_train; num_iterations=num_iterations, learning_rate=learning_rate, print_cost =print_cost)

Since you declared those as keyword arguments in the definition of the function optimize.

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