def visualize_tsne(args):
data_dir = os.path.abspath(args['<data>'])
data = h5.File(data_dir,'r')
TX = data['test_set/test_set'][:5000]
TZ = data['test_labels/real_test_labels'][:5000]
TZ = one_hot(TZ,13)
n_input = TX.shape[1]
print 'data loaded.'
tsne = Tsne(n_input, 2, perplexity=5)
print 'TSNE initialized.'
TX_r = tsne.fit_transform(TX)
print 'data TSNEd.'
fig = plt.figure(figsize=(16, 16))
ax = fig.add_subplot(111)
TZ_am = TZ.argmax(axis=1)
ax.scatter(TX_r[TZ_am==0, 0], TX_r[TZ_am==0, 1], c='g', lw=0, alpha=1, s=100, marker='o')
ax.scatter(TX_r[TZ_am==1, 0], TX_r[TZ_am==1, 1], c='b', lw=0, alpha=1, s=100, marker='v')
ax.scatter(TX_r[TZ_am==2, 0], TX_r[TZ_am==2, 1], c='yellow', lw=0, alpha=1, s=100, marker='^')
ax.scatter(TX_r[TZ_am==3, 0], TX_r[TZ_am==3, 1], c='r', lw=0, alpha=1, s=100, marker='<')
ax.scatter(TX_r[TZ_am==4, 0], TX_r[TZ_am==4, 1], c='g', lw=0, alpha=1, s=100, marker='>')
ax.scatter(TX_r[TZ_am==5, 0], TX_r[TZ_am==5, 1], c='m', lw=0, alpha=1, s=100, marker='8')
ax.scatter(TX_r[TZ_am==6, 0], TX_r[TZ_am==6, 1], c='crimson', lw=0, alpha=1, s=100, marker='s')
ax.scatter(TX_r[TZ_am==7, 0], TX_r[TZ_am==7, 1], c='lawngreen', lw=0, alpha=1, s=100, marker='p')
ax.scatter(TX_r[TZ_am==8, 0], TX_r[TZ_am==8, 1], c='gold', lw=0, alpha=1, s=100, marker='*')
ax.scatter(TX_r[TZ_am==9, 0], TX_r[TZ_am==9, 1], c='darkorange', lw=0, alpha=1, s=100, marker='h')
ax.scatter(TX_r[TZ_am==10, 0], TX_r[TZ_am==10, 1], c='k', lw=0, alpha=1, s=100, marker='H')
ax.scatter(TX_r[TZ_am==11, 0], TX_r[TZ_am==11, 1], c='magenta', lw=0, alpha=1, s=100, marker='d')
ax.scatter(TX_r[TZ_am==12, 0], TX_r[TZ_am==12, 1], c='turquoise', lw=0, alpha=1, s=100, marker='D')
plt.legend()
plt.savefig(os.path.join('/nthome/maugust/thesis',args['<output>']))
def test_tsne():
X = np.random.random((100, 3)).astype(theano.config.floatX)
X, = theano_floatx(X)
tsne = Tsne(n_inpt=3, n_lowdim=2, perplexity=40, early_exaggeration=50,
max_iter=10)
E = tsne.fit_transform(X)
def visualize_tsne(args):
model_dir = os.path.abspath(args['<model>'])
data_dir = os.path.abspath(args['<data>'])
os.chdir(model_dir)
cps = contrib.find_checkpoints('.')
if cps:
with gzip.open(cps[-1], 'rb') as fp:
trainer = cPickle.load(fp)
trainer.model.parameters.data[...] = trainer.best_pars
data = h5.File(data_dir,'r')
TX = data['test_set/test_set'][:5000]
TZ = data['test_labels/real_test_labels'][:5000]
TZ = one_hot(TZ,13)
print 'data loaded.'
if args['<mode>'] == 'cnn':
f_transformed = trainer.model.function(['inpt'],'mlp-layer-2-inpt')
print 'transform-function generated.'
data = minibatches(TX, trainer.model.batch_size, 0)
trans_TX = np.concatenate([f_transformed(element) for element in data], axis=0)
else:
f_transformed = trainer.model.function(['inpt'],'layer-2-inpt')
print 'transform-function generated.'
trans_TX = f_transformed(TX)
trans_TX = np.array(trans_TX, dtype=np.float32)
print 'data transformed'
trans_n_input = trans_TX.shape[1]
trans_tsne = Tsne(trans_n_input, 2, perplexity=5)
print 'TSNE initialized.'
trans_TX_r = trans_tsne.fit_transform(trans_TX)
print 'data TSNEd'
fig = plt.figure(figsize=(16, 16))
ax = fig.add_subplot(111)
TZ_am = TZ.argmax(axis=1)
ax.scatter(trans_TX_r[TZ_am==0, 0], trans_TX_r[TZ_am==0, 1], c='g', lw=0, alpha=1, s=100, marker='o')
ax.scatter(trans_TX_r[TZ_am==1, 0], trans_TX_r[TZ_am==1, 1], c='b', lw=0, alpha=1, s=100, marker='v')
ax.scatter(trans_TX_r[TZ_am==2, 0], trans_TX_r[TZ_am==2, 1], c='yellow', lw=0, alpha=1, s=100, marker='^')
ax.scatter(trans_TX_r[TZ_am==3, 0], trans_TX_r[TZ_am==3, 1], c='r', lw=0, alpha=1, s=100, marker='<')
ax.scatter(trans_TX_r[TZ_am==4, 0], trans_TX_r[TZ_am==4, 1], c='g', lw=0, alpha=1, s=100, marker='>')
ax.scatter(trans_TX_r[TZ_am==5, 0], trans_TX_r[TZ_am==5, 1], c='m', lw=0, alpha=1, s=100, marker='8')
ax.scatter(trans_TX_r[TZ_am==6, 0], trans_TX_r[TZ_am==6, 1], c='crimson', lw=0, alpha=1, s=100, marker='s')
ax.scatter(trans_TX_r[TZ_am==7, 0], trans_TX_r[TZ_am==7, 1], c='lawngreen', lw=0, alpha=1, s=100, marker='p')
ax.scatter(trans_TX_r[TZ_am==8, 0], trans_TX_r[TZ_am==8, 1], c='gold', lw=0, alpha=1, s=100, marker='*')
ax.scatter(trans_TX_r[TZ_am==9, 0], trans_TX_r[TZ_am==9, 1], c='darkorange', lw=0, alpha=1, s=100, marker='h')
ax.scatter(trans_TX_r[TZ_am==10, 0], trans_TX_r[TZ_am==10, 1], c='k', lw=0, alpha=1, s=100, marker='H')
ax.scatter(trans_TX_r[TZ_am==11, 0], trans_TX_r[TZ_am==11, 1], c='magenta', lw=0, alpha=1, s=100, marker='d')
ax.scatter(trans_TX_r[TZ_am==12, 0], trans_TX_r[TZ_am==12, 1], c='turquoise', lw=0, alpha=1, s=100, marker='D')
plt.savefig(os.path.join('/nthome/maugust/thesis',args['<output>']))
def test_tsne():
X = np.random.random((100, 3))
tsne = Tsne(n_inpt=3, n_lowdim=2, perplexity=40, early_exaggeration=50,
max_iter=10)
E = tsne.fit_transform(X)
