Creating the TFLearn Layers

Let us learn how to create the layers of the neural network models in TFLearn:

1. Create an input layer first:

input_layer = tflearn.input_data(shape=[None,num_inputs]

1. Pass the input object to create further layers:

layer1 = tflearn.fully_connected(input_layer,10,
                                 activation='relu')
layer2 = tflearn.fully_connected(layer1,10,
                                 activation='relu')

1. Add the output layer:

output = tflearn.fully_connected(layer2,n_classes,
                                 activation='softmax')

1. Create the final net from the estimator layer such as regression:

net = tflearn.regression(output,
                         optimizer='adam',
                         metric=tflearn.metrics.Accuracy(),
                         loss='categorical_crossentropy'
                        )

The TFLearn provides several classes for layers that are described in following sub-sections.