def loadFullFashion_MNSIT(shift): imgs, y, num_train_data = data.mnist_load('Fashion_MNIST', shift=shift) imgs_t, y_t, num_train_data_t = data.mnist_load('Fashion_MNIST', dataset='test', shift=shift) imgs.shape = imgs.shape + (1, ) imgs_t.shape = imgs_t.shape + (1, ) return np.concatenate((imgs, imgs_t)), np.concatenate((y, y_t))
def getFashion_MNISTDatapool(batch_size, shift, keep=None): if keep is None: imgs, y, num_train_data = data.mnist_load('fmnist',shift=shift) else: imgs, y, num_train_data = data.mnist_load('fmnist', keep=keep, shift=shift) print "Total number of training dataset: " + str(num_train_data) imgs.shape = imgs.shape + (1,) data_pool = utils.MemoryData({'img': imgs, 'label':y}, batch_size) return data_pool
def getMNISTDatapool(batch_size, keep=None, shift=True): if keep is None: imgs, _, num_train_data = data.mnist_load('MNIST_data', shift=shift) else: imgs, _, num_train_data = data.mnist_load('MNIST_data', keep=keep, shift=shift) print "Total number of training data: " + str(num_train_data) imgs.shape = imgs.shape + (1, ) data_pool = utils.MemoryData({'img': imgs}, batch_size) return data_pool
def getFullFashion_MNISTDatapool(batch_size, shift,keep=None): if keep is None: imgs, y, num_train_data = data.mnist_load('fmnist',shift=shift) imgs_t, y_t, num_train_data_t = data.mnist_load('fmnist',dataset='test',shift=shift) else: imgs, y, num_train_data = data.mnist_load('fmnist', keep=keep, shift=shift) imgs_t, y_t, num_train_data_t = data.mnist_load('fmnist', keep=keep,dataset='test',shift = shift) print "Total number of training dataset: " + str(num_train_data + num_train_data_t) imgs.shape = imgs.shape + (1,) imgs_t.shape = imgs_t.shape + (1,) data_pool = utils.MemoryData({'img': np.concatenate((imgs,imgs_t)), 'label':np.concatenate((y,y_t))}, batch_size) return data_pool
import tensorflow as tf import data_mnist as data import models_mnist as models """ param """ epoch = 50 batch_size = 64 lr = 0.0002 z_dim = 100 gpu_id = 3 ''' data ''' utils.mkdir('./data/mnist/') data.mnist_download('./data/mnist') imgs, _, _ = data.mnist_load('./data/mnist') imgs.shape = imgs.shape + (1,) data_pool = utils.MemoryData({'img': imgs}, batch_size) """ graphs """ with tf.device('/gpu:%d' % gpu_id): ''' models ''' generator = models.generator discriminator = models.discriminator ''' graph ''' # inputs real = tf.placeholder(tf.float32, shape=[None, 28, 28, 1]) z = tf.placeholder(tf.float32, shape=[None, z_dim])
#test code snippet here import data_mnist as data import numpy as np from matplotlib import pyplot as plt import tensorflow as tf import my_utils #extracting subset of mnist if 0: imgs, labels, _ = data.mnist_load('MNIST_data') #one-hot = False for l in range(10): print labels[l] keep = [0, 9] X, Y = [], [] for x, y in zip(imgs, labels): if y in keep: X.append(x) Y.append(y) X = np.array(X) Y = np.array(Y) for l in range(10): img = np.reshape(X[l], [28, 28]) plt.imshow(img, cmap='gray') print Y[l] plt.show() #convert labels to one-hot if 0:
relu = tf.nn.relu fc_relu = partial(fc, activation_fn=relu) real = tf.placeholder(tf.float32, shape=[None, 784]) with tf.variable_scope("encoder", reuse=False): y = fc_relu(real, 500, scope='layer1') with tf.variable_scope("encoder", reuse=True): weights = tf.get_variable('layer1/weights') biases = tf.get_variable('layer1/biases') print(weights.shape) print(biases.shape) a = 0 if 0: imgs, _, _ = data.mnist_load('MNIST_data') a = 0 if 0: batch_size = 100 z_dim = 10 n_cen = 5 z = tf.get_variable('z', shape=(batch_size, z_dim)) theta_p = tf.get_variable('theta_p', shape=(n_cen)) u_p = tf.get_variable('u_p', shape=(n_cen, z_dim)) lambda_p = tf.get_variable('lambda_p', shape=(n_cen, z_dim)) theta_p_tensor = tf.expand_dims(theta_p, 0) theta_p_tensor = tf.expand_dims(theta_p_tensor, 2) theta_p_tensor = tf.tile(theta_p_tensor, [batch_size, 1, 1]) u_p_tensor = tf.tile(tf.expand_dims(u_p, 0), [batch_size, 1, 1])