An equivalent for python tflearn library in matlab?

I am currently working on a reinforcement learning problem. I have the following code in python, however I am not sure how is it possible to translate it into matlab!

Python Code:

def create_network(self):
  inputs = tflearn.input_data(shape=[None, self.state_dimension])
  net = tflearn.fully_connected(inputs, 400)
  net = tflearn.layers.normalization.batch_normalization(net)
  net = tflearn.activations.relu(net)
  net = tflearn.fully_connected(net, 300)
  net = tflearn.layers.normalization.batch_normalization(net)
  net = tflearn.activations.relu(net)

    w_init = tflearn.initializations.uniform(minval=-0.003, maxval=0.003)
    out = tflearn.fully_connected(
        net, self.a_dim, activation='tanh', weights_init=w_init)

    scaled_out = tf.multiply(out, self.action_bound)
    return inputs, out, scaled_out

I know that Matlab has a toolbox for neural networks, however I still can not figure out how to translate such a code into a Matlab script. I must say I am pretty new to Python so my question might look trivial. I tried to install PyCharm to debug the code and understand what each attribute function is doing, but it still looks like a black box to me. If anyone has a suggestion or a solution, please share it with me. Also it would be nice if someone can explain what kind of calculations or assumptions these attributes are performing?

Thank you so much.

Answer 1

[Hari]

Hi Kyana,

I understand that you are working on a reinforcement learning problem and want to translate a Python neural network creation function using TFLearn into MATLAB code.

You can follow the below steps to convert the code into MATLAB

Define Input Layer:

Start by defining the input layer with the specified state dimension. This is equivalent to tflearn.input_data in Python.

layers = [
    imageInputLayer([self.state_dimension 1 1], 'Normalization', 'none', 'Name', 'input')
    ];

Add Fully Connected Layers:

Add fully connected layers with specified neuron counts, similar to tflearn.fully_connected. Use “fullyConnectedLayer” in MATLAB.

layers = [
    layers
    fullyConnectedLayer(400, 'Name', 'fc1')
    batchNormalizationLayer('Name', 'bn1')
    reluLayer('Name', 'relu1')
    fullyConnectedLayer(300, 'Name', 'fc2')
    batchNormalizationLayer('Name', 'bn2')
    reluLayer('Name', 'relu2')
    ];

Initialize Weights:

For the output layer, use a custom weight initialization similar to tflearn.initializations.uniform.

finalLayer = fullyConnectedLayer(self.a_dim, 'Name', 'output', 'WeightsInitializer', @(sz) unifrnd(-0.003, 0.003, sz));

Define Output Layer:

Use “tanhLayer” for the activation function and scale the output. MATLAB does not directly support scaling in layers, so it can be done post-processing.

layers = [
    layers
    finalLayer
    tanhLayer('Name', 'tanh')
    ];

Compile the Network:

Combine layers into a layer graph and compile using “dlnetwork” for further training or inference.

lgraph = layerGraph(layers);
dlnet = dlnetwork(lgraph);

Refer to the documentation of “fullyConnectedLayer” to know more about its properties: https://www.mathworks.com/help/deeplearning/ref/nnet.cnn.layer.fullyconnectedlayer.html

Hope this helps!