TFLEARN multivariable regression does not converge (attempting to duplicate fitlab fitnet) - matlab

Question

I am trying to write a model in TFLEARN to fit to 16 parameters.
I have previously run this same experiment in Matlab using the "fitnet" function with 2 hidden layers of 2000 and 1500 nodes.
I am attempting to replicate these results in tensorflow before exploring other architectures/descent algos/hyperparameter tuning. I have done some research and determined the matlab fitnet function uses tanh nodes for hidden layers and linear for output. Also, the descent algorithm is defaulted to levenberg-Marquardt, but worked for me with other (sgd) algorithms as well.
It appears that the accuracy is maxing out around .2, and then oscillating below this over successive epochs. I did not see this behavior in matlab.
My TFLEARN code looks like:
tnorm = tflearn.initializations.uniform_scaling()
adam = tflearn.optimizers.Adam (learning_rate=0.1, beta1=0.9, beta2=0.999, epsilon=1e-08, use_locking=False, name='Adam')
# network building
input_data = tflearn.input_data(shape=[None, np.shape(prepared_x)[1]])
fc1 = tflearn.fully_connected(input_data, 2000,activation='tanh',weights_init=tnorm)
fc2 = tflearn.fully_connected(fc1,1500,activation='tanh',weights_init=tnorm)
output = tflearn.fully_connected(fc2, 16, activation='linear',weights_init=tnorm)
network = tflearn.regression(output, optimizer=adam, loss='mean_square')
#define model with checkpoints
model = tflearn.DNN(network, tensorboard_dir='output/', tensorboard_verbose=3, checkpoint_path='output')
#Train Model
model.fit(prepared_x, prepared_t, n_epoch=5, batch_size=100,shuffle=True, show_metric=True, snapshot_epoch=False,validation_set=0.1 )
#save
model.save('TFLEARN_FC_final.tfl')
The output of the traing session looks like:
Run id: UTSD6N
Log directory: output/
[?25l---------------------------------
Training samples: 43200
Validation samples: 4800
--
Training Step: 1
[2K
| Adam | epoch: 000 | loss: 0.00000 - acc: 0.0000 -- iter: 00100/43200
[A[ATraining Step: 2 | total loss: [1m[32m0.67871[0m[0m
[2K
| Adam | epoch: 000 | loss: 0.67871 - acc: 0.0455 -- iter: 00200/43200
[A[ATraining Step: 3 | total loss: [1m[32m33.14599[0m[0m
[2K
| Adam | epoch: 000 | loss: 33.14599 - acc: 0.0082 -- iter: 00300/43200
[A[ATraining Step: 4 | total loss: [1m[32m28.01067[0m[0m
[2K
| Adam | epoch: 000 | loss: 28.01067 - acc: 0.0021 -- iter: 00400/43200
[A[ATraining Step: 5 | total loss: [1m[32m17.35706[0m[0m
[2K
| Adam | epoch: 000 | loss: 17.35706 - acc: 0.0006 -- iter: 00500/43200
[A[ATraining Step: 6 | total loss: [1m[32m9.73368[0m[0m
[2K
| Adam | epoch: 000 | loss: 9.73368 - acc: 0.0002 -- iter: 00600/43200
[A[ATraining Step: 7 | total loss: [1m[32m5.19867[0m[0m
[2K
| Adam | epoch: 000 | loss: 5.19867 - acc: 0.0001 -- iter: 00700/43200
[A[ATraining Step: 8 | total loss: [1m[32m3.54779[0m[0m
[2K
| Adam | epoch: 000 | loss: 3.54779 - acc: 0.0113 -- iter: 00800/43200
[A[ATraining Step: 9 | total loss: [1m[32m3.80998[0m[0m
[2K
| Adam | epoch: 000 | loss: 3.80998 - acc: 0.0106 -- iter: 00900/43200
[A[ATraining Step: 10 | total loss: [1m[32m4.33370[0m[0m
[2K
| Adam | epoch: 000 | loss: 4.33370 - acc: 0.0053 -- iter: 01000/43200
[A[ATraining Step: 11 | total loss: [1m[32m4.24100[0m[0m
[2K
...
[2K
| Adam | epoch: 004 | loss: 0.02448 - acc: 0.1817 -- iter: 42800/43200
[A[ATraining Step: 2157 | total loss: [1m[32m0.02633[0m[0m
[2K
| Adam | epoch: 004 | loss: 0.02633 - acc: 0.1875 -- iter: 42900/43200
[A[ATraining Step: 2158 | total loss: [1m[32m0.02509[0m[0m
[2K
| Adam | epoch: 004 | loss: 0.02509 - acc: 0.1688 -- iter: 43000/43200
[A[ATraining Step: 2159 | total loss: [1m[32m0.02525[0m[0m
[2K
| Adam | epoch: 004 | loss: 0.02525 - acc: 0.1529 -- iter: 43100/43200
[A[ATraining Step: 2160 | total loss: [1m[32m0.02695[0m[0m
[2K
| Adam | epoch: 005 | loss: 0.02695 - acc: 0.1456 -- iter: 43200/43200
image of accuracy/loss from tensorboard
Any suggestions would be much appreciated.

Answer 1

For any future lurkers -- I solved my own problem by fixing the descent algorithm.
The default learning rate for the Adam optimizer is .001 but this was too high, I had to switch to .005 for convergence.