def embedded_from_tsne(self, model_filename, num_components=2):
self._parse_model_parameters(model_filename)
params = autoenc_params.setupModelFromFlags()
sess = params[0]
autoenc = params[3]
model_filename = params[7]
output, encoding = sess.run([autoenc.getDecoding(True), autoenc.encoding_layers[-1]], feed_dict={autoenc.input_placeholder : self.real_images})
self.embedded_images = TSNE(n_components=num_components, perplexity=40, verbose=2).fit_transform(encoding)
def encodings_from_model(self, model_filename):
self._parse_model_parameters(model_filename)
params = autoenc_params.setupModelFromFlags()
sess = params[0]
autoenc = params[3]
model_filename = params[7]
output, encoding = sess.run([autoenc.getDecoding(True), autoenc.encoding_layers[-1]], feed_dict={autoenc.input_placeholder : self.real_images})
self.encoded_images = encoding
FLAGS = tf.app.flags.FLAGS
image_size = 20 if FLAGS.dataset.lower() == "smiley" else 28
app = QtGui.QApplication([])
## Create window with ImageView widget
win = QtGui.QWidget()
layout = QtGui.QGridLayout()
win.setLayout(layout)
win.resize(800,800)
imv = pg.ImageView()
print win.layout()
win.layout().addWidget(imv, 1, 0)
sess, global_step, train_step, autoenc, loss, writer, saver, model_filename, cheap_summaries, expensive_summaries, test_loss_summary, classifier_out, y = autoenc_params.setupModelFromFlags()
# Add sliders to manipulate the encoding
def displayImage(encodedActivations):
print autoenc.encoding_layers[-1].get_shape()
print autoenc.encoding_layers[-1]
enc = np.matrix(encodedActivations)
output = sess.run(autoenc.getDecoding(True), feed_dict={autoenc.encoding_layers[-1] : enc})
imv.setImage(output.reshape(image_size, image_size).T)
sliders = []
def sliderModev(val):
encAct = [sl.value()/10. for sl in sliders]
print encAct
displayImage(encAct)