Short description

Autoencoder implementation in TensorFlow. Visualizes the (up to 75x75 first) reconstructed values every 1000 training epochs. The background color corresponds to the absolute difference between the reconstructed value and the actual (target) value. These are typically in [0,1] by design (scaling is applied by default, but can be turned off) even though tanh is used as an activation function (so values in [-1,1] are possible in the reconstruction). We use bins corresponding to different errors, with bin 0 (dark green) being the best (small error) and red being the worst. A colored digit in the foreground indicates that the entry in question has moved to another bin (improved or worsened) during the last epoch. The digit indicates which bin it is in now.

Todo:

• choose layerwise training or not
• add weight decay
• changing the weight initialization
• specify random seed

Dependencies

- tensorflow - pandas - numpy

Usage

``` usage: ascii_autoencoder.py [-h] [-T] [-n N_HIDDEN_NODES [N_HIDDEN_NODES ...]] [-l LEARNING_RATE] [-d DROPOUT_PROB] [-c N_CYCLES] [-b BATCH_SIZE] [--no_scaling] [--no_output] [--decoded_file DECODED_FILE] [--encoded_file ENCODED_FILE] [--scaled_input_file SCALED_INPUT_FILE] fpath

Autoencoder implemented in tensorflow

positional arguments: fpath Path to file containing matrix with input examples for the autoencoder

optional arguments: -h, --help show this help message and exit -T, --transpose Transpose input -n N_HIDDEN_NODES [N_HIDDEN_NODES ...], --n_hidden_nodes N_HIDDEN_NODES [N_HIDDEN_NODES ...] Number of hidden nodes in each hidden layer -l LEARNING_RATE, --learning_rate LEARNING_RATE Learning rate -d DROPOUT_PROB, --dropout_prob DROPOUT_PROB Dropout probability -c N_CYCLES, --n_cycles N_CYCLES Number of training cycles -b BATCH_SIZE, --batch_size BATCH_SIZE Number of training examples per cycle --no_scaling Do not scale input (use this if you have already scaled it) --no_output Skip writing encoded and decoded values to file? --decoded_file DECODED_FILE Name of file to write decoded (reconstructed) values to --encoded_file ENCODED_FILE Name of file to write encoded values to --scaled_input_file SCALED_INPUT_FILE Name of file to write scaled input values to