tf_base

High level API to make task of Tensorflow API coding, simplified.

Source Code

Requirements

• Tensorflow

Basic Usage

To make creation of Datasets and Graph a bit easier.

Library under development. Contains rough edges/unfinished functonality. API subject to changes.

Dictionary to Featues

This library has support for making features with a single call.

  from tf_base.file.record.protofy import protofy
  
  >>features = protofy(int_dict={'testing_int': [[1], [1, 3, 5]]}))
  >>print(features)
    # returns
  feature_list {
      key: "testing_int"
      value {
        feature {
          int64_list {
            value: 1
          }
        }
        feature {
          int64_list {
            value: 1
            value: 3
            value: 5
          }
        }
      }
    }
    }

Writing Images from Folder to TFRecord

With simple call tfrecord file for the images can be created, image folders will be taken as labels. Compression formats can be specified as boolen or types. Resizing of images also supported with size parameter.

  from tf_base.file.image import ImageTFRecordWriter
  
  images = ImageTFRecordWriter('/home/shivam/Documents/', ['jpg'],
                               size=(20, 20, 0), show=False)
  record = images.to_tfr(tfrecord_name='images',
                         save_folder='/home/shivam/Documents/', allow_compression=True)

Reading Images from TFRecord

This API is based on tf.dataset API so it can simply read the TFrecord

   from tf_base.file.image import ImageTFRecord
   
   reader = ImageTFRecordReader()
   tf_record_path = '/path/to/image_folder'
   data = reader.batch(tf_record_path=tf_record_path, batch_size=2, epochs_size=1)
   data = data.make_one_shot_iterator()
   sess = reader.session
   data = data.get_next()
   summarizer = reader.summary_writer('../summary', sess.graph)
   try:
       for _ in range(21):
           image, label = sess.run(data)
           print(image.shape, label)
       print('Completed!')
   except tf.errors.OutOfRangeError:
       print('Data Exhausted!')
   finally:
       summarizer.close()

Adding with tf.Graph functionality with GraphAPI to class

With GraphAPI classes and functions act as variable_scope to the graph. based on tf.sonnet backend

    from tf_base.graph import GraphAPI
    graph_api = GraphAPI(reuse_variables=True, log=False)

    @add_metaclass(graph_api())
    class Api(object):

        def __init__(self):
            super(Api, self).__init__()

        @property
        def graph(self):
            return super(Api, self).graph

        @property
        def session(self):
            return super(Api, self).session

    class Convolution(Api):

        def W(self, value, name='weight'):
            return tf.Variable(initial_value=value, name=name)

        def B(self, value, name='bias'):
            return tf.Variable(initial_value=value, name=name)
            
    convolution = Convolution()
    weight = convolution.W(tf.truncated_normal_initializer(mean=0.0, stddev=1.0))
    
    # is same as 
    with.variabl_scope('Convolution'+'/'+'W')
      one = tf.Variable(initial_value=
                        tf.truncated_normal_initializer(mean=0.0, stddev=1.0), 
                        name='weight'))