vis_func.py

# from __future__ import absolute_import
# from __future__ import print_function

import os
os.environ['THEANO_FLAGS']='mode=FAST_RUN,device=gpu,floatX=float32'
# This gives a 'perform' error in compile
#os.environ['THEANO_FLAGS']='mode=FAST_COMPILE,device=gpu1,floatX=float32'
#os.environ['THEANO_FLAGS']='device=gpu0'
import theano
print theano.config.device

import pylab as pl
import matplotlib.cm as cm
import numpy as np
from numpy import *
from pylab import *
np.random.seed(1337) # for reproducibility

from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.utils import np_utils

from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy.ma as ma


def make_mosaic(imgs, nrows, ncols, border=1):
    """
    Given a set of images with all the same shape, makes a
    mosaic with nrows and ncols
    """
    nimgs = imgs.shape[0]
    imshape = imgs.shape[1:]

    mosaic = ma.masked_all((nrows * imshape[0] + (nrows - 1) * border,
                            ncols * imshape[1] + (ncols - 1) * border),
                            dtype=np.float32)

    paddedh = imshape[0] + border
    paddedw = imshape[1] + border
    for i in xrange(nimgs):
        row = int(np.floor(i / ncols))
        col = i % ncols

        mosaic[row * paddedh:row * paddedh + imshape[0],
               col * paddedw:col * paddedw + imshape[1]] = imgs[i]
    return mosaic

#pl.imshow(make_mosaic(np.random.random((9, 10, 10)), 3, 3, border=1))
def nice_imshow(ax, data, vmin=None, vmax=None, cmap=None):
    """Wrapper around pl.imshow"""
    if cmap is None:
        cmap = cm.jet
    if vmin is None:
        vmin = data.min()
    if vmax is None:
        vmax = data.max()
    divider = make_axes_locatable(ax)
    cax = divider.append_axes("right", size="5%", pad=0.05)
    im = ax.imshow(data, vmin=vmin, vmax=vmax, interpolation='nearest', cmap=cmap)
    pl.colorbar(im, cax=cax)