Python utils.one_hot方法代码示例

# 需要导入模块: import utils [as 别名]
# 或者: from utils import one_hot [as 别名]
def load_train(self):
        labels = utils.one_hot(data.labels_train, m=121).astype(np.float32)
        split = np.load(DEFAULT_VALIDATION_SPLIT_PATH)

        split = np.load(DEFAULT_VALIDATION_SPLIT_PATH)
        indices_train = split['indices_train']
        indices_valid = split['indices_valid']

        image_shapes = np.asarray([img.shape for img in data.load('train')]).astype(np.float32)
        moments = np.load("data/image_moment_stats_v1_train.pkl")

        centroid_distance = np.abs(moments["centroids"][:, [1, 0]] - image_shapes / 2)
        info = np.concatenate((centroid_distance, image_shapes, moments["angles"][:, None], moments["minor_axes"][:, None], moments["major_axes"][:, None]), 1).astype(np.float32)

        self.info_train = info[indices_train]
        self.info_valid = info[indices_valid]

        self.y_train = np.load(self.train_pred_file).astype(np.float32)
        self.y_valid = np.load(self.valid_pred_file).astype(np.float32)
        self.labels_train = labels[indices_train]
        self.labels_valid = labels[indices_valid] 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import one_hot [as 别名]
def _get_minibatch_feed_dict(self, target_q_values, 
                               non_terminal_minibatch, terminal_minibatch):
    """
    Helper to construct the feed_dict for train_op. Takes the non-terminal and 
    terminal minibatches as well as the max q-values computed from the target
    network for non-terminal states. Computes the expected q-values based on
    discounted future reward.

    @return: feed_dict to be used for train_op
    """
    assert len(target_q_values) == len(non_terminal_minibatch)

    states = []
    expected_q = []
    actions = []

    # Compute expected q-values to plug into the loss function
    minibatch = itertools.chain(non_terminal_minibatch, terminal_minibatch)
    for item, target_q in zip_longest(minibatch, target_q_values, fillvalue=0):
      state, action, reward, _, _ = item
      states.append(state)
      # target_q will be 0 for terminal states due to fillvalue in zip_longest
      expected_q.append(reward + self.config.reward_discount * target_q)
      actions.append(utils.one_hot(action, self.env.action_space.n))

    return {
      self.network.x_placeholder: states, 
      self.network.q_placeholder: expected_q,
      self.network.action_placeholder: actions,
    } 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import one_hot [as 别名]
def train(epoch):
    h_phi.train()
    to_average = []
    # train
    for query, candidates in zip(batched_query_train, batched_neighbor_train):
        optimizer.zero_grad()
        cand_x, cand_y = candidates
        query_x, query_y = query

        cand_x = cand_x.to(device=gpu)
        cand_y = cand_y.to(device=gpu)
        query_x = query_x.to(device=gpu)
        query_y = query_y.to(device=gpu)

        neighbor_e = h_phi(cand_x).reshape(NUM_TRAIN_NEIGHBORS, EMBEDDING_SIZE)
        query_e = h_phi(query_x).reshape(NUM_TRAIN_QUERIES, EMBEDDING_SIZE)

        neighbor_y_oh = one_hot(cand_y).reshape(NUM_TRAIN_NEIGHBORS, 10)
        query_y_oh = one_hot(query_y).reshape(NUM_TRAIN_QUERIES, 10)

        losses = dknn_loss(query_e, neighbor_e, query_y_oh, neighbor_y_oh)
        loss = losses.mean()
        loss.backward()
        optimizer.step()
        to_average.append((-loss).item() / k)

    print('Avg. train correctness of top k:',
          sum(to_average) / len(to_average))
    print('Avg. train correctness of top k:', sum(
        to_average) / len(to_average), file=logfile)
    logfile.flush() 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import one_hot [as 别名]
def main():
  """ Test an RNN trained for TIMIT phoneme recognition. """

  args, params_str, layer_kwargs = parse_args()

  _, _, test_inputs, test_labels = timitphonemerec.load_split(args.data_dir, val=False,
                                                              mfcc=True, normalize=True)

  # Input seqs have shape [length, INPUT_SIZE]. Label seqs are int8 arrays with shape [length],
  # but need to have shape [length, 1] for the batch generator.
  test_labels = [seq[:, np.newaxis] for seq in test_labels]

  test_batches = utils.full_bptt_batch_generator(test_inputs, test_labels, TEST_BATCH_SIZE,
                                                 num_epochs=1, shuffle=False)

  model = models.RNNClassificationModel(args.layer_type, INPUT_SIZE, TARGET_SIZE, args.num_hidden_units,
                                        args.activation_type, **layer_kwargs)

  def _error_rate(valid_predictions, valid_targets):
    incorrect_mask = tf.logical_not(tf.equal(tf.argmax(valid_predictions, 1), tf.argmax(valid_targets, 1)))
    return tf.reduce_mean(tf.to_float(incorrect_mask))
  model.error_rate = _error_rate(model.valid_predictions, model.valid_targets)

  config = tf.ConfigProto()
  config.gpu_options.allow_growth = False
  sess = tf.Session(config=config)

  saver = tf.train.Saver()
  saver.restore(sess, os.path.join(args.results_dir, 'model.ckpt'))

  batch_inputs, batch_labels = next(test_batches)
  batch_targets = utils.one_hot(np.squeeze(batch_labels, 2), TARGET_SIZE)

  valid_predictions, valid_targets, error_rate = sess.run(
    [model.valid_predictions, model.valid_targets, model.error_rate],
    feed_dict={model.inputs: batch_inputs,
               model.targets: batch_targets}
  )

  print('%f' % error_rate)
  with open(os.path.join(args.results_dir, 'test_result.txt'), 'w') as f:
    print('%f' % error_rate, file=f) 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import one_hot [as 别名]
def main():
  """ Test an RNN for sequential (possibly permuted) MNIST recognition. """

  args, params_str, layer_kwargs = parse_args()

  outs = mnist.load_split(args.data_dir, val=False, permute=args.permute, normalize=True, seed=0)
  _, _, test_images, test_labels = outs

  # Flatten the images.
  test_inputs = test_images.reshape([len(test_images), -1, INPUT_SIZE])

  # Align sequence-level labels with the appropriate time steps by padding with NaNs,
  # and to do so, first convert the labels to floats.
  length = test_inputs.shape[1]
  pad = lambda x: np.pad(x, [[0, 0], [length - 1, 0], [0, 0]], mode='constant', constant_values=np.nan)
  test_labels = pad(test_labels.reshape([-1, 1, 1]).astype(np.float))

  test_batches = utils.full_bptt_batch_generator(test_inputs, test_labels, TEST_BATCH_SIZE, num_epochs=1,
                                                 shuffle=False)

  model = models.RNNClassificationModel(args.layer_type, INPUT_SIZE, TARGET_SIZE, args.num_hidden_units,
                                        args.activation_type, **layer_kwargs)

  def _error_rate(valid_predictions, valid_targets):
    incorrect_mask = tf.logical_not(tf.equal(tf.argmax(valid_predictions, 1), tf.argmax(valid_targets, 1)))
    return tf.reduce_mean(tf.to_float(incorrect_mask))
  model.error_rate = _error_rate(model.valid_predictions, model.valid_targets)

  config = tf.ConfigProto()
  config.gpu_options.allow_growth = False
  sess = tf.Session(config=config)

  saver = tf.train.Saver()
  saver.restore(sess, os.path.join(args.results_dir, 'model.ckpt'))

  error_rates = []
  for batch_inputs, batch_labels in test_batches:

    batch_targets = utils.one_hot(np.squeeze(batch_labels, 2), TARGET_SIZE)
    valid_predictions, valid_targets, batch_error_rates = sess.run(
      [model.valid_predictions, model.valid_targets, model.error_rate],
      feed_dict={model.inputs: batch_inputs,
                 model.targets: batch_targets}
    )
    error_rates.append(batch_error_rates)

  error_rate = np.mean(error_rates, dtype=np.float)
  print('%f' % error_rate)
  with open(os.path.join(args.results_dir, 'test_result.txt'), 'w') as f:
    print('%f' % error_rate, file=f) 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import one_hot [as 别名]
def train_feature_generator(self):
	
	print 'Training sampler.'
        
	images, labels = self.load_svhn(self.svhn_dir, split='train')
	labels = utils.one_hot(labels, 10)
    
        # build a graph
        model = self.model
        model.build_model()

	noise_dim = 100
	epochs = 5000

        with tf.Session(config=self.config) as sess:
	    
            # initialize variables
            tf.global_variables_initializer().run()
            
	    # restore feature extractor trained on Step 0
            print ('Loading pretrained feature extractor.')
            variables_to_restore = slim.get_model_variables(scope='feature_extractor')
            restorer = tf.train.Saver(variables_to_restore)
            restorer.restore(sess, self.pretrained_feature_extractor)
	    print 'Loaded'
            
            summary_writer = tf.summary.FileWriter(logdir=self.log_dir, graph=tf.get_default_graph())
            saver = tf.train.Saver()
	    
	    for step in range(self.train_feature_generator_iters):

		i = step % int(images.shape[0] / self.batch_size)
		
		images_batch = images[i*self.batch_size:(i+1)*self.batch_size]
		labels_batch = labels[i*self.batch_size:(i+1)*self.batch_size]
		noise = utils.sample_Z(self.batch_size, noise_dim, 'uniform')


		feed_dict = {model.noise: noise, model.images: images_batch, model.labels: labels_batch}
		
		sess.run(model.d_train_op, feed_dict)
		sess.run(model.g_train_op, feed_dict)
		
		if (step+1) % 100 == 0:
		    avg_D_fake = sess.run(model.logits_fake, feed_dict)
		    avg_D_real = sess.run(model.logits_real, feed_dict)
		    summary, dl, gl = sess.run([model.summary_op, model.d_loss, model.g_loss], feed_dict)
		    summary_writer.add_summary(summary, step)
		    print ('Step: [%d/%d] d_loss: %.6f g_loss: %.6f avg_d_fake: %.2f avg_d_real: %.2f ' \
			       %(step+1, self.train_feature_generator_iters, dl, gl, avg_D_fake.mean(), avg_D_real.mean()))
		    
	    print 'Saving.'
	    saver.save(sess, self.pretrained_feature_generator)