suryabhupa

# To get these names:
#
# for tensor in graph_def.node:
#   print(tensor.name)
#
# where graph_def is the frozen graph def of the InceptionV1 network (either import, or construct as part of the default graph and then call:
# 
# for tensor in tf.get_default_graph().as_graph_def().node:
#   print(tensor.name)
#

suryabhupa

InceptionV3/Conv2d_1a_3x3/weights
InceptionV3/Conv2d_1a_3x3/weights/read
InceptionV3/InceptionV3/Conv2d_1a_3x3/convolution
InceptionV3/Conv2d_1a_3x3/BatchNorm/beta
InceptionV3/Conv2d_1a_3x3/BatchNorm/beta/read
InceptionV3/Conv2d_1a_3x3/BatchNorm/moving_mean
InceptionV3/Conv2d_1a_3x3/BatchNorm/moving_mean/read
InceptionV3/Conv2d_1a_3x3/BatchNorm/moving_variance
InceptionV3/Conv2d_1a_3x3/BatchNorm/moving_variance/read
InceptionV3/InceptionV3/Conv2d_1a_3x3/BatchNorm/batchnorm/add/y

suryabhupa

import numpy as np
import theano
import theano.tensor as T
import lasagne
from theano import printing

np.random.seed(1234)

class TTLayer(lasagne.layers.Layer):
    """

suryabhupa

#!/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.
"""

suryabhupa

" Make Vim more useful
set nocompatible

" Use the OS clipboard by default (on versions compiled with `+clipboard`)
" Set color scheme!
set clipboard=unnamed

" Enhance command-line completion
set wildmenu