for unit in range(autoenc.encoding_layers[-1].get_shape()[1]): slider = QtGui.QSlider(win) slider.setOrientation(QtCore.Qt.Horizontal) slider.setRange(-30, 200) slider.sliderMoved.connect(sliderModev) sliders.append(slider) win.layout().addWidget(slider) Add buttons to load some examples if FLAGS.dataset.lower() == "mnist": mnist = input_data.read_data_sets('MNIST_data', one_hot=False) elif FLAGS.dataset.lower() == "smiley": mnist = smiley.read_data_sets() else: assert False rawdatas = [] class QButton(QtGui.QWidget): def __init__(self, raw_image, parent=None): QtGui.QWidget.__init__(self, parent) self.button = QtGui.QPushButton('', self) self.size = int(math.sqrt(raw_image.shape[1])) self.name='me' self.raw_image = raw_image data = (raw_image*255).reshape(self.size, self.size).astype(np.uint8).T self.button.clicked.connect(self.propImage) img = QtGui.QImage(data.shape[0], data.shape[1], QtGui.QImage.Format_RGB32)
imagebox = offsetbox.AnnotationBbox( offsetbox.OffsetImage(clustered_images[i][representative].reshape(self.image_dim, self.image_dim), cmap=plt.cm.gray_r), clusters[i][representative]) ax = plt.axes(frameon=False) ax.add_artist(imagebox) plt.scatter(x, y, c=c, marker="x") plt.show() if __name__=="__main__": data = None num_of_classes = 0 embedded_file_path = "" remove_arguments = list() for i, argument in enumerate(sys.argv): if argument == "--smiley": data = smiley.read_data_sets() num_of_classes = 4 remove_arguments.append(sys.argv[i]) elif argument == "--mnist": data = input_data.read_data_sets('MNIST_data', one_hot=False) num_of_classes = 10 remove_arguments.append(sys.argv[i]) elif argument == "--embedded_file_path": embedded_file_path = sys.argv[i + 1] remove_arguments.append(sys.argv[i]) remove_arguments.append(sys.argv[i + 1]) elif argument == "--model_file_path": dir_path = os.path.dirname(sys.argv[i + 1]) model_filename = os.path.basename(os.path.normpath(sys.argv[i + 1])) remove_arguments.append(sys.argv[i]) remove_arguments.append(sys.argv[i + 1])
# -*- coding: utf-8 -*- import tensorflow as tf import numpy as np from tensorflow.examples.tutorials.mnist import input_data import smiley import time import autoenc_params FLAGS = tf.app.flags.FLAGS if __name__ == "__main__": # Prepare the training data if FLAGS.dataset.lower() == "mnist": traindata = input_data.read_data_sets('MNIST_data', one_hot=True) elif FLAGS.dataset.lower() == "smiley": traindata = smiley.read_data_sets(one_hot=True) else: raise ValueError("Unknown dataset: {}".format(FLAGS.dataset)) sess, global_step, train_step, autoenc, loss, writer, saver, model_filename, cheap_summaries, expensive_summaries, test_loss_summary, classifier_out, y = autoenc_params.setupModelFromFlags() last_cheap_summaries = 0 last_expensive_summaries = 0 test_batch = traindata.test.next_batch(FLAGS.batchsize)[0] with sess.as_default(): try: n_samples = traindata.train.images.shape[0] n_epochs = FLAGS.maxepochs batch_size = FLAGS.batchsize n_batches = n_samples // batch_size for epochnr in range(n_epochs): for batchnr in range(n_batches):