I have been writing simple autoencoder using tflearn.
net = tflearn.input_data (shape=[None, train.shape [1]])
net = tflearn.fully_connected (net, 500, activation = 'tanh', regularizer = None, name = 'fc_en_1')
#hidden state
net = tflearn.fully_connected (net, 100, activation = 'tanh', regularizer = 'L1', name = 'fc_en_2', weight_decay = 0.0001)
net = tflearn.fully_connected (net, 500, activation = 'tanh', regularizer = None, name = 'fc_de_1')
net = tflearn.fully_connected (net, train.shape [1], activation = 'linear', name = 'fc_de_2')
net = tflearn.regression(net, optimizer='adam', learning_rate=0.01, loss='mean_square', metric='default')
model = tflearn.DNN (net)
Model is trained well, but after training I want to use separately encoder and decoder.
How can I do it? Right now I can restore input, and I want to be able to convert input to hidden representation and restore input from arbitrary hidden representation.