Face Difficulty when converting tensorflow model to Matlab

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Ze
Ze 2024-7-29
回答： William Rose 2024-7-29
I have a part of tensorflow code that I need to translate to matlab, but fail to do that. I have checked deep learning toolbox and unable to resolve the issue. If someone can help me this question, it is very helpful.
My tensorflow code (Python) is the following:
def get_grad3(model, t, x,a_data,u_data,a_data_n,mean_a,mean_u,w_upd,w1,w2,w3,p_data,stdt,stdx,xdim,ydim,twf,xwf,p_dataf):
% A tf.GradientTape is used to compute derivatives in TensorFlow
with tf.GradientTape(persistent=True) as tape:
% Split t and x to compute partial derivatives
tape.watch(model.trainable_variables)
loss, Lu, Lr, Lm, Pn,w1n,Lr1,Lr2,w_upd,kP = compute_loss3(model, t, x, a_data, u_data, a_data_n,mean_a,mean_u,w_upd,w1,w2,w3,p_data,stdt,stdx,xdim,ydim,twf,xwf,p_dataf)
grads_u = tape.gradient(loss, model.trainable_variables) % loss function
del tape
return grads_u, loss, Lu, Lr, Lm,Pn,w1n,Lr1,Lr2,w_upd,kP
I followed my previous question response: https://www.mathworks.com/matlabcentral/answers/2140906-face-difficulty-when-converting-tensorflow-model-to-matlab?s_tid=mlc_ans_men_view&mentions=true#answer_1491566
My matlab code is the following:
function [grads_u, loss, Lu, Lr, Lm, Pn, w1n, Lr1, Lr2, w_upd, kP] = get_grad3(model, t, x, a_data, u_data, a_data_n, mean_a, mean_u, w_upd, w1, w2, w3, p_data, stdt, stdx, xdim, ydim, twf, xwf, p_dataf)
% Define a function for computing loss and other required variables
function [loss, Lu, Lr, Lm, Pn, w1n, Lr1, Lr2, w_upd, kP] = computeLossGradients(model, t, x)
% Compute the loss and other outputs using the compute_loss3 function
[loss, Lu, Lr, Lm, Pn, w1n, Lr1, Lr2, w_upd, kP] = compute_loss3(model, t, x, a_data, u_data, a_data_n, mean_a, mean_u, w_upd, w1, w2, w3, p_data, stdt, stdx, xdim, ydim, twf, xwf, p_dataf);
% Compute gradients of the loss with respect to the model's learnable parameters
grads_u = dlgradient(loss, model.LearnableParameters, 'EnableHigherDerivatives', true);
end
% Use dlfeval to compute the loss and other variables
[loss, Lu, Lr, Lm, Pn, w1n, Lr1, Lr2, w_upd, kP] = dlfeval(@computeLossGradients, model, t, x);
end
My error message is
Unrecognized function or variable 'w_upd'.
Error in get_grad3/computeLossGradients (line 6)
[loss, Lu, Lr, Lm, Pn, w1n, Lr1, Lr2, w_upd, kP] = compute_loss3(model, t, x, a_data, u_data, a_data_n, mean_a, mean_u, w_upd, w1, w2, w3, p_data, stdt, stdx, xdim, ydim, twf, xwf, p_dataf);
Error in deep.internal.dlfeval (line 17)
[varargout{1:nargout}] = fun(x{:});
Error in deep.internal.dlfevalWithNestingCheck (line 19)
[varargout{1:nargout}] = deep.internal.dlfeval(fun,varargin{:});
Error in dlfeval (line 31)
[varargout{1:nargout}] = deep.internal.dlfevalWithNestingCheck(fun,varargin{:});
Error in get_grad3 (line 12)
[loss, Lu, Lr, Lm, Pn, w1n, Lr1, Lr2, w_upd, kP] = dlfeval(@computeLossGradients, model, t, x);
Error in PWIP_initialize_v2 (line 114)
[grads_u, loss, Lu, Lr, Lm, Pn, w1n, Lr1, Lr2, w_upd, kP] = get_grad3(model, tw, xw, a_data, u_data, a_data_n, mean_a, mean_u, epoch, w1, w2, w3, p_data, stdt, stdx, xdim, ydim, twf, xwf, p_dataf);
I have also tried the symbolic differentiation and it works okay.
function [grads_u, loss, Lu, Lr, Lm, Pn, w1n, Lr1, Lr2, w_upd, kP] = get_grad3(model, t, x, a_data, u_data, a_data_n, mean_a, mean_u, w_upd, w1, w2, w3, p_data, stdt, stdx, xdim, ydim, twf, xwf, p_dataf)
% Compute the loss and other required variables
[loss, Lu, Lr, Lm, Pn, w1n, Lr1, Lr2, w_upd, kP] = compute_loss3(model, t, x, a_data, u_data, a_data_n, mean_a, mean_u, w_upd, w1, w2, w3, p_data, stdt, stdx, xdim, ydim, twf, xwf, p_dataf);
% Define symbolic variables
num_vars = numel(model.trainable_variables);
syms model_sym [1 num_vars]
% Convert loss to symbolic
loss_sym = sym(loss);
% Calculate gradients
grads_u = sym('grads_u', [1 num_vars]);
for i = 1:num_vars
grads_u(i) = diff(loss_sym, model_sym(i));
end
end
Can anyone point out how can I improve the code by changing the function input and output?
Thanks for all suggestions.

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采纳的回答

William Rose
William Rose 2024-7-29
@Ze,
Your function compute_loss_gradients(...) calls function compute_loss3(). The call to compute_loss3(...) includes w_upd as one of the parameters passed to the function. (w_upd is also one of the outputs from the function.) The error occurs because w_upd has not been passed to compute_loss_gradients(...), so it is unknown, and cannot be passed to compute_loss3().

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