lukemetz

use libc::{c_long, size_t};
use std::c_str::CString;
pub use base::{PyObject, ToPyType, FromPyType, PyState, PyIterator};
pub use ffi::{PythonCAPI, PyObjectRaw};
pub use base::{PyError,
           FromTypeConversionError,
           ToTypeConversionError,
           NullPyObject};

macro_rules! prim_pytype (

lukemetz

from tsne import bh_sne
import numpy as np
from skimage.transform import resize

from matplotlib import pyplot as plt

def gray_to_color(img):
    if len(img.shape) == 2:
        img = np.dstack((img, img, img))
    return img

lukemetz

import theano
from theano import tensor as T
import numpy as np
from theano.sandbox.cuda.dnn import dnn_conv
from theano.sandbox.cuda import host_from_gpu
from contexttimer import Timer
import theano.compile.mode
import theano.printing

def speed_test(_func, input_shape):

lukemetz

# modified from slim
@scopes.add_arg_scope
def batch_norm(inputs,
               decay=0.999,
               scale=False,
               epsilon=0.001,
               moving_vars='moving_vars',
               activation=None,
               is_training=True,
               trainable=True,

lukemetz

import tensorflow as tf
import numpy as np
from tensorflow.python import gen_batchnorm_training_op

with tf.device("/gpu:1"):

    def tf_bn(shape, data_format):
        """ output tensor for tensorflow implemented batch norm"""
        data = np.random.randn(*shape)*10 + 5
        inp = tf.Variable(data.astype("float32"), tf.float32)

lukemetz

import subprocess
import time
import os

OUTPUT = os.path.expanduser("~/luke_output_network")
num_per_file = 1000
try:
    os.makedirs(OUTPUT)
except:
    print("dir already exists. continuing")