def _save_samples(self, step, sess, filename_suffix, z_distribution=None):
if z_distribution is None:
z_distribution = self.z_generator
z_sample = z_distribution(self.batch_size, self.z_dim)
grid_shape = self._image_grid_shape()
samples = sess.run(self.fake_images_merged,
feed_dict={self.z: z_sample})
samples = samples.reshape((grid_shape[0] * self.input_height,
grid_shape[1] * self.input_width, -1)).squeeze()
out_folder = ops.check_folder(os.path.join(self.result_dir, self.model_dir))
full_path = os.path.join(out_folder, filename_suffix)
ops.save_images(samples, full_path)
def maybe_save_samples(self, idx):
"""Saves training results every 5000 steps."""
if np.mod(idx, 5000) != 0:
return
z_sample = self.z_generator(self.batch_size, self.z_dim)
samples = self.sess.run(self.fake_images_merged,
feed_dict={self.z: z_sample})
samples = samples.reshape(
(8 * self.input_height, 8 * self.input_width, -1)).squeeze()
out_folder = ops.check_folder(os.path.join(self.result_dir, self.model_dir))
suffix = "%s_train_%04d.png" % (self.model_name, idx)
full_path = os.path.join(out_folder, suffix)
ops.save_images(samples, full_path)
def visualize_results(self, step, z_distribution=None):
"""Generates and stores a set of fake images."""
if z_distribution is None:
z_distribution = self.z_generator
z_sample = z_distribution(self.batch_size, self.z_dim)
samples = self.sess.run(self.fake_images_merged,
feed_dict={self.z: z_sample})
samples = samples.reshape(
(8 * self.input_height, 8 * self.input_width, -1)).squeeze()
out_folder = ops.check_folder(os.path.join(self.result_dir, self.model_dir))
suffix = "%s_step%03d_test_all_classes.png" % (self.model_name, step)
full_path = os.path.join(out_folder, suffix)
ops.save_images(samples, full_path)
def maybe_save_samples(self, idx):
"""Saves training results every 5000 steps."""
if np.mod(idx, 5000) != 0:
return
z_sample = self.z_generator(self.batch_size, self.z_dim)
samples = self.sess.run(self.fake_images_merged,
feed_dict={self.z: z_sample})
samples = samples.reshape(
(8 * self.input_height, 8 * self.input_width, -1)).squeeze()
out_folder = ops.check_folder(
os.path.join(self.result_dir, self.model_dir))
suffix = "%s_train_%04d.png" % (self.model_name, idx)
full_path = os.path.join(out_folder, suffix)
ops.save_images(samples, full_path)
def visualize_results(self, step, z_distribution=None):
"""Generates and stores a set of fake images."""
if z_distribution is None:
z_distribution = self.z_generator
z_sample = z_distribution(self.batch_size, self.z_dim)
samples = self.sess.run(self.fake_images_merged,
feed_dict={self.z: z_sample})
samples = samples.reshape(
(8 * self.input_height, 8 * self.input_width, -1)).squeeze()
out_folder = ops.check_folder(
os.path.join(self.result_dir, self.model_dir))
suffix = "%s_step%03d_test_all_classes.png" % (self.model_name, step)
full_path = os.path.join(out_folder, suffix)
ops.save_images(samples, full_path)