Python flags.FLAGS属性代码示例

# 需要导入模块: from tensorflow import flags [as 别名]
# 或者: from tensorflow.flags import FLAGS [as 别名]
def main(unused_argv):
  logging.set_verbosity(tf.logging.INFO)

  # convert feature_names and feature_sizes to lists of values
  feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
      FLAGS.feature_names, FLAGS.feature_sizes)

  if FLAGS.frame_features:
    reader = readers.YT8MFrameFeatureReader(feature_names=feature_names,
                                            feature_sizes=feature_sizes)
  else:
    reader = readers.YT8MAggregatedFeatureReader(feature_names=feature_names,
                                                 feature_sizes=feature_sizes)

  if FLAGS.output_file is "":
    raise ValueError("'output_file' was not specified. "
      "Unable to continue with inference.")

  if FLAGS.input_data_pattern is "":
    raise ValueError("'input_data_pattern' was not specified. "
      "Unable to continue with inference.")

  inference(reader, FLAGS.train_dir, FLAGS.input_data_pattern,
    FLAGS.output_file, FLAGS.batch_size, FLAGS.top_k) 

# 需要导入模块: from tensorflow import flags [as 别名]
# 或者: from tensorflow.flags import FLAGS [as 别名]
def build_model(self, model, reader):
    """Find the model and build the graph."""

    label_loss_fn = find_class_by_name(FLAGS.label_loss, [losses])()
    optimizer_class = find_class_by_name(FLAGS.optimizer, [tf.train])
  
    build_graph(reader=reader,
                 model=model,
                 optimizer_class=optimizer_class,
                 clip_gradient_norm=FLAGS.clip_gradient_norm,
                 train_data_pattern=FLAGS.train_data_pattern,
                 label_loss_fn=label_loss_fn,
                 base_learning_rate=FLAGS.base_learning_rate,
                 learning_rate_decay=FLAGS.learning_rate_decay,
                 learning_rate_decay_examples=FLAGS.learning_rate_decay_examples,
                 regularization_penalty=FLAGS.regularization_penalty,
                 num_readers=FLAGS.num_readers,
                 batch_size=FLAGS.batch_size,
                 num_epochs=FLAGS.num_epochs)
  
    return tf.train.Saver(max_to_keep=0, keep_checkpoint_every_n_hours=5) 

# 需要导入模块: from tensorflow import flags [as 别名]
# 或者: from tensorflow.flags import FLAGS [as 别名]
def calculate_loss(self, predictions, labels, weights=None, **unused_params):
    with tf.name_scope("loss_xent"):
      epsilon = 10e-6
      if FLAGS.label_smoothing:
        float_labels = smoothing(labels)
      else:
        float_labels = tf.cast(labels, tf.float32)
      cross_entropy_loss = float_labels * tf.log(predictions + epsilon) + (
          1 - float_labels) * tf.log(1 - predictions + epsilon)
      cross_entropy_loss = tf.negative(cross_entropy_loss)
      if weights is not None:
        print cross_entropy_loss, weights
        weighted_loss = tf.einsum("ij,i->ij", cross_entropy_loss, weights)
        print "create weighted_loss", weighted_loss
        return tf.reduce_mean(tf.reduce_sum(weighted_loss, 1))
      else:
        return tf.reduce_mean(tf.reduce_sum(cross_entropy_loss, 1)) 

# 需要导入模块: from tensorflow import flags [as 别名]
# 或者: from tensorflow.flags import FLAGS [as 别名]
def get_input_evaluation_tensors(reader,
                                 data_pattern,
                                 batch_size=256):
  logging.info("Using batch size of " + str(batch_size) + " for evaluation.")
  with tf.name_scope("eval_input"):
    files = gfile.Glob(data_pattern)
    if not files:
      print data_pattern, files
      raise IOError("Unable to find the evaluation files.")
    logging.info("number of evaluation files: " + str(len(files)))
    files.sort()
    filename_queue = tf.train.string_input_producer(
        files, shuffle=False, num_epochs=1)
    eval_data = reader.prepare_reader(filename_queue)
    return tf.train.batch(
        eval_data,
        batch_size=batch_size,
        capacity=3 * FLAGS.batch_size,
        allow_smaller_final_batch=True,
        enqueue_many=True) 

# 需要导入模块: from tensorflow import flags [as 别名]
# 或者: from tensorflow.flags import FLAGS [as 别名]
def get_input_data_tensors(reader,
                           data_pattern,
                           batch_size=256,
                           num_epochs=None):
  logging.info("Using batch size of " + str(batch_size) + " for training.")
  with tf.name_scope("train_input"):
    files = gfile.Glob(data_pattern)
    if not files:
      raise IOError("Unable to find training files. data_pattern='" +
                    data_pattern + "'.")
    logging.info("Number of training files: %s.", str(len(files)))
    files.sort()
    filename_queue = tf.train.string_input_producer(
        files, num_epochs=num_epochs, shuffle=False)
    training_data = reader.prepare_reader(filename_queue)

    return tf.train.batch(
        training_data,
        batch_size=batch_size,
        capacity=FLAGS.batch_size * 4,
        allow_smaller_final_batch=True,
        enqueue_many=True) 

# 需要导入模块: from tensorflow import flags [as 别名]
# 或者: from tensorflow.flags import FLAGS [as 别名]
def create_model(self, model_input, vocab_size, l2_penalty=1e-8, **unused_params):
        """Creates a logistic model.

        Args:
          model_input: 'batch' x 'num_features' matrix of input features.
          vocab_size: The number of classes in the dataset.

        Returns:
          A dictionary with a tensor containing the probability predictions of the
          model in the 'predictions' key. The dimensions of the tensor are
          batch_size x num_classes."""

        input_size = vocab_size
        output_size = FLAGS.hidden_size
        with tf.name_scope("rbm"):
            self.weights = tf.Variable(tf.truncated_normal([input_size, output_size],
                                    stddev=1.0 / math.sqrt(float(input_size))), name="weights")
            self.v_bias = tf.Variable(tf.zeros([input_size]), name="v_bias")
            self.h_bias = tf.Variable(tf.zeros([output_size]), name="h_bias")
            tf.add_to_collection(name=tf.GraphKeys.REGULARIZATION_LOSSES, value=l2_penalty*tf.nn.l2_loss(self.weights))
            tf.add_to_collection(name=tf.GraphKeys.REGULARIZATION_LOSSES, value=l2_penalty*tf.nn.l2_loss(self.v_bias))
            tf.add_to_collection(name=tf.GraphKeys.REGULARIZATION_LOSSES, value=l2_penalty*tf.nn.l2_loss(self.h_bias)) 

# 需要导入模块: from tensorflow import flags [as 别名]
# 或者: from tensorflow.flags import FLAGS [as 别名]
def main(unused_argv):
  logging.set_verbosity(tf.logging.INFO)

  # convert feature_names and feature_sizes to lists of values
  feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
      FLAGS.feature_names, FLAGS.feature_sizes)

  if FLAGS.frame_features:
    reader = readers.YT8MFrameFeatureReader(feature_names=feature_names,
                                            feature_sizes=feature_sizes)
  else:
    reader = readers.YT8MAggregatedFeatureReader(feature_names=feature_names,
                                                 feature_sizes=feature_sizes)

  if FLAGS.output_dir is "":
    raise ValueError("'output_dir' was not specified. "
      "Unable to continue with inference.")

  if FLAGS.input_data_pattern is "":
    raise ValueError("'input_data_pattern' was not specified. "
      "Unable to continue with inference.")

  inference(reader, FLAGS.model_checkpoint_path, FLAGS.input_data_pattern,
      FLAGS.output_dir, FLAGS.batch_size, FLAGS.top_k) 

# 需要导入模块: from tensorflow import flags [as 别名]
# 或者: from tensorflow.flags import FLAGS [as 别名]
def calculate_loss_mix2(self, predictions, predictions_class, predictions_encoder, labels, **unused_params):
    with tf.name_scope("loss_mix2"):
      float_labels = tf.cast(labels, tf.float32)
      float_encoders = float_labels
      for i in range(FLAGS.encoder_layers):
        var_i = np.loadtxt(FLAGS.autoencoder_dir+'autoencoder_layer%d.model' % i)
        weight_i = tf.constant(var_i[:-1,:],dtype=tf.float32)
        bias_i = tf.reshape(tf.constant(var_i[-1,:],dtype=tf.float32),[-1])
        float_encoders = tf.nn.xw_plus_b(float_encoders,weight_i,bias_i)
        if i<FLAGS.encoder_layers-1:
          float_encoders = tf.nn.relu(float_encoders)
        else:
          hidden_mean = tf.reduce_mean(float_encoders,axis=1,keep_dims=True)
          hidden_std = tf.sqrt(tf.reduce_mean(tf.square(float_encoders-hidden_mean),axis=1,keep_dims=True))
          float_encoders = (float_encoders-hidden_mean)/(hidden_std+1e-6)
          #float_encoders = tf.nn.sigmoid(float_encoders)
      cross_entropy_encoder = 0.1*self.calculate_mseloss(predictions_encoder,float_encoders)
      cross_entropy_loss = self.calculate_loss(predictions,labels)
      return cross_entropy_encoder+cross_entropy_loss, float_encoders
      #return cross_entropy_encoder, float_encoders 

# 需要导入模块: from tensorflow import flags [as 别名]
# 或者: from tensorflow.flags import FLAGS [as 别名]
def calculate_loss(self, predictions, labels, **unused_params):
    bound = FLAGS.softmax_bound
    vocab_size_1 = bound
    with tf.name_scope("loss_softmax"):
      epsilon = 10e-8
      float_labels = tf.cast(labels, tf.float32)
      labels_1 = float_labels[:,:vocab_size_1]
      predictions_1 = predictions[:,:vocab_size_1]
      cross_entropy_loss = CrossEntropyLoss().calculate_loss(predictions_1,labels_1)
      lables_2 = float_labels[:,vocab_size_1:]
      predictions_2 = predictions[:,vocab_size_1:]
      # l1 normalization (labels are no less than 0)
      label_rowsum = tf.maximum(
          tf.reduce_sum(lables_2, 1, keep_dims=True),
          epsilon)
      label_append = 1.0-tf.reduce_max(lables_2, 1, keep_dims=True)
      norm_float_labels = tf.concat((tf.div(lables_2, label_rowsum),label_append),axis=1)
      predictions_append = 1.0-tf.reduce_sum(predictions_2, 1, keep_dims=True)
      softmax_outputs = tf.concat((predictions_2,predictions_append),axis=1)
      softmax_loss = norm_float_labels * tf.log(softmax_outputs + epsilon) + (
          1 - norm_float_labels) * tf.log(1 - softmax_outputs + epsilon)
      softmax_loss = tf.negative(tf.reduce_sum(softmax_loss, 1))
    return tf.reduce_mean(softmax_loss) + cross_entropy_loss 

# 需要导入模块: from tensorflow import flags [as 别名]
# 或者: from tensorflow.flags import FLAGS [as 别名]
def calculate_loss(self, predictions, labels, **unused_params):
        bound = FLAGS.softmax_bound
        vocab_size_1 = bound
        with tf.name_scope("loss_softmax"):
            epsilon = 10e-8
            float_labels = tf.cast(labels, tf.float32)
            labels_1 = float_labels[:,:vocab_size_1]
            predictions_1 = predictions[:,:vocab_size_1]
            cross_entropy_loss = CrossEntropyLoss().calculate_loss(predictions_1,labels_1)
            lables_2 = float_labels[:,vocab_size_1:]
            predictions_2 = predictions[:,vocab_size_1:]
            # l1 normalization (labels are no less than 0)
            label_rowsum = tf.maximum(
                tf.reduce_sum(lables_2, 1, keep_dims=True),
                epsilon)
            label_append = 1.0-tf.reduce_max(lables_2, 1, keep_dims=True)
            norm_float_labels = tf.concat((tf.div(lables_2, label_rowsum),label_append),axis=1)
            predictions_append = 1.0-tf.reduce_sum(predictions_2, 1, keep_dims=True)
            softmax_outputs = tf.concat((predictions_2,predictions_append),axis=1)
            softmax_loss = norm_float_labels * tf.log(softmax_outputs + epsilon) + (
                                                                                       1 - norm_float_labels) * tf.log(1 - softmax_outputs + epsilon)
            softmax_loss = tf.negative(tf.reduce_sum(softmax_loss, 1))
        return tf.reduce_mean(softmax_loss) + cross_entropy_loss 

# 需要导入模块: from tensorflow import flags [as 别名]
# 或者: from tensorflow.flags import FLAGS [as 别名]
def get_forward_parameters(vocab_size=4716):
    t_vars = tf.trainable_variables()
    h1_vars_weight = [var for var in t_vars if 'hidden_1' in var.name and 'weights' in var.name]
    h1_vars_biases = [var for var in t_vars if 'hidden_1' in var.name and 'biases' in var.name]
    h2_vars_weight = [var for var in t_vars if 'hidden_2' in var.name and 'weights' in var.name]
    h2_vars_biases = [var for var in t_vars if 'hidden_2' in var.name and 'biases' in var.name]
    o1_vars_weight = [var for var in t_vars if 'output_1' in var.name and 'weights' in var.name]
    o1_vars_biases = [var for var in t_vars if 'output_1' in var.name and 'biases' in var.name]
    o2_vars_weight = [var for var in t_vars if 'output_2' in var.name and 'weights' in var.name]
    o2_vars_biases = [var for var in t_vars if 'output_2' in var.name and 'biases' in var.name]
    h1_vars_biases = tf.reshape(h1_vars_biases[0],[1,FLAGS.hidden_size_1])
    h2_vars_biases = tf.reshape(h2_vars_biases[0],[1,FLAGS.hidden_size_2])
    o1_vars_biases = tf.reshape(o1_vars_biases[0],[1,FLAGS.hidden_size_1])
    o2_vars_biases = tf.reshape(o2_vars_biases[0],[1,vocab_size])
    vars_1 = tf.concat((h1_vars_weight[0],h1_vars_biases),axis=0)
    vars_2 = tf.concat((h2_vars_weight[0],h2_vars_biases),axis=0)
    vars_3 = tf.concat((o1_vars_weight[0],o1_vars_biases),axis=0)
    vars_4 = tf.concat((o2_vars_weight[0],o2_vars_biases),axis=0)
    return [vars_1,vars_2,vars_3,vars_4] 

# 需要导入模块: from tensorflow import flags [as 别名]
# 或者: from tensorflow.flags import FLAGS [as 别名]
def main(unused_argv):
  logging.set_verbosity(tf.logging.INFO)

  if not FLAGS.json_prediction_files_pattern:
    raise ValueError(
        "The flag --json_prediction_files_pattern must be specified.")

  if not FLAGS.csv_output_file:
    raise ValueError("The flag --csv_output_file must be specified.")

  logging.info("Looking for prediction files with pattern: %s", 
               FLAGS.json_prediction_files_pattern)

  file_paths = gfile.Glob(FLAGS.json_prediction_files_pattern)  
  logging.info("Found files: %s", file_paths)

  logging.info("Writing submission file to: %s", FLAGS.csv_output_file)
  with gfile.Open(FLAGS.csv_output_file, "w+") as output_file:
    output_file.write(get_csv_header())

    for file_path in file_paths:
      logging.info("processing file: %s", file_path)

      with gfile.Open(file_path) as input_file:

        for line in input_file: 
          json_data = json.loads(line)
          output_file.write(to_csv_row(json_data))

    output_file.flush()
  logging.info("done") 

# 需要导入模块: from tensorflow import flags [as 别名]
# 或者: from tensorflow.flags import FLAGS [as 别名]
def calculate_loss(self, predictions, labels, **unused_params):
    with tf.name_scope("loss_xent"):
      epsilon = 10e-6
      alpha = FLAGS.alpha

      float_labels = tf.cast(labels, tf.float32)
      cross_entropy_loss = 2*(alpha*float_labels * tf.log(predictions + epsilon) + (1-alpha)*(
          1 - float_labels) * tf.log(1 - predictions + epsilon))
      cross_entropy_loss = tf.negative(cross_entropy_loss)
      return tf.reduce_mean(tf.reduce_sum(cross_entropy_loss, 1)) 

# 需要导入模块: from tensorflow import flags [as 别名]
# 或者: from tensorflow.flags import FLAGS [as 别名]
def get_reader():
  # Convert feature_names and feature_sizes to lists of values.
  feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
      FLAGS.feature_names, FLAGS.feature_sizes)

  if FLAGS.frame_features:
    reader = readers.YT8MFrameFeatureReader(
        feature_names=feature_names, feature_sizes=feature_sizes)
  else:
    reader = readers.YT8MAggregatedFeatureReader(
        feature_names=feature_names, feature_sizes=feature_sizes)
    
  return reader 

# 需要导入模块: from tensorflow import flags [as 别名]
# 或者: from tensorflow.flags import FLAGS [as 别名]
def write_to_record(video_ids, video_labels, video_rgbs, video_audios, video_predictions, video_num_frames, filenum, num_examples_processed):
    writer = tf.python_io.TFRecordWriter(FLAGS.output_dir + '/' + 'predictions-%04d.tfrecord' % filenum)
    for i in range(num_examples_processed):
        video_id = video_ids[i]
        video_label = np.nonzero(video_labels[i,:])[0]
        video_rgb = video_rgbs[i,:]
        video_audio = video_audios[i,:]
        video_prediction = video_predictions[i,:]
        video_num_frame = video_num_frames[i]
        example = get_output_feature(video_id, video_label, video_rgb, video_audio, video_prediction, video_num_frame)
        serialized = example.SerializeToString()
        writer.write(serialized)
    writer.close()