tmramalho

15 06
16 06
20 06
01 07
02 07
03 07
04 07
05 07
06 07
07 07

tmramalho

def image_grid(data, n_cols=None, zoom=1):
    if n_cols is None:
        n_cols = int(np.ceil(np.sqrt(data.shape[0])))
    n_rows = int(np.ceil(data.shape[0]/n_cols))
    target = np.zeros((data.shape[1]*n_rows, data.shape[2]*n_cols, data.shape[3]), dtype=data.dtype)
    flat_data = data.swapaxes(1,2).reshape((data.shape[0]*data.shape[1], data.shape[2], data.shape[3])).swapaxes(0, 1)
    for i in range(n_rows):
        start_y = i*data.shape[2]*n_cols
        end_y = (i+1)*data.shape[2]*n_cols
        start_x = i*data.shape[1]

tmramalho

import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA, FactorAnalysis, SparsePCA
from sklearn import datasets

dset = datasets.load_digits()
x = dset.data
y = dset.target

model = PCA(n_components=2)

tmramalho

import theano
import theano.tensor as T
import numpy as np
import cPickle
import random
import matplotlib.pyplot as plt

class RNN(object):

	def __init__(self, nin, n_hidden, nout):