Created
July 30, 2016 00:47
-
-
Save suryabhupa/df7c8dc6b63bbfec4f37b2d67332c304 to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| #!/usr/bin/env python | |
| """ | |
| Usage example employing Lasagne and TT-layer on the MNIST dataset. | |
| This is a simplified version of | |
| https://github.com/Lasagne/Lasagne/blob/master/examples/mnist.py | |
| with using the TT-layer. | |
| """ | |
| from __future__ import print_function | |
| import sys | |
| import os | |
| import time | |
| import numpy as np | |
| np.set_printoptions(threshold=np.nan) | |
| import theano | |
| import theano.tensor as T | |
| import lasagne | |
| from ttlayer import TTLayer | |
| np.random.seed(1234) | |
| size = 256 | |
| def load_dataset(): | |
| X = np.random.rand(size) | |
| X_train = np.array([X]*1) | |
| return X_train | |
| def build_simple_mlp(input_var=None): | |
| l_in = lasagne.layers.InputLayer(shape=(None, size), input_var=input_var) | |
| # Another 16-unit layer: | |
| l_hid1 = lasagne.layers.DenseLayer( | |
| l_in, num_units=size, W = np.random.rand(size, size), | |
| nonlinearity=lasagne.nonlinearities.identity) | |
| return l_hid1 | |
| def full_main(num_epochs=500): | |
| np.random.seed(1234) | |
| X_train = load_dataset() | |
| input_var = T.matrix('inputs') | |
| lhid_1 = build_simple_mlp(input_var) | |
| output = lasagne.layers.get_output(lhid_1) | |
| print_fn = theano.function([input_var], output) | |
| res = print_fn(X_train).reshape(size) | |
| print("\nFULL:\n", res) | |
| return res | |
| def build_mlp(input_var=None): | |
| l_in = lasagne.layers.InputLayer(shape=(None, size), input_var=input_var) | |
| l_hid1 = TTLayer(l_in, tt_input_shape=[4, 4, 4, 4], tt_output_shape=[4, 4, 4, 4], | |
| tt_ranks=[1, 10, 100, 10, 1], nonlinearity=lasagne.nonlinearities.identity) | |
| return l_hid1 | |
| def main(num_epochs=500): | |
| np.random.seed(1234) | |
| X_train = load_dataset() | |
| input_var = T.matrix('inputs') | |
| lhid_1 = build_mlp(input_var) | |
| output = lasagne.layers.get_output(lhid_1) | |
| print_fn = theano.function([input_var], output) | |
| res = print_fn(X_train).reshape(size) | |
| print("\nTT:\n", res) | |
| return res | |
| if __name__ == '__main__': | |
| np.random.seed(1234) | |
| X_tt = main() | |
| X_fc = full_main() | |
| print('\nNORM:', np.linalg.norm(X_tt - X_fc)) |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment