docstr: Building the Tiny yolov2 network
  
  Parameters
  ----------
  inputs: TensorFlow Tensor
      Images sent as input for the network.
      This is an input tensor of shape [batch, in_height, in_width, in_channels]
  init_weights: Dict of numpy arrays
      A mapping of layer names to init weights
  
  Returns
  -------
  net: TensorFlow Tensor
      output of the TinyYOLOv2 network stored in net
docstr_tok: ["Building","the","Tiny","yolov2","network","Parameters","--","--","--","--","--","inputs",":","TensorFlow","Tensor","Images","sent","as","input","for","the","network",".","This","is","an","input","tensor","of","shape","[","batch",",","in_height",",","in_width",",","in_channels","]","init_weights",":","Dict","of","numpy","arrays","A","mapping","of","layer","names","to","init","weights","Returns","--","--","--","-","net",":","TensorFlow","Tensor","output","of","the","TinyYOLOv2","network","stored","in","net"]
code: def tiny_yolo(self, inputs, init_weights):
      """
      Building the Tiny yolov2 network
  
      Parameters
      ----------
      inputs: TensorFlow Tensor
          Images sent as input for the network.
          This is an input tensor of shape [batch, in_height, in_width, in_channels]
      init_weights: Dict of numpy arrays
          A mapping of layer names to init weights
  
      Returns
      -------
      net: TensorFlow Tensor
          output of the TinyYOLOv2 network stored in net
      """
  
      filter_sizes = [16, 32, 64, 128, 256, 512, 1024, 1024]
  
      for idx, f in enumerate(filter_sizes, 1):
          batch_name = "batchnorm%d_" % (idx - 1)
          if idx == 1:
              net = self.conv_layer(
                  inputs,
                  [3, 3, 3, f],
                  "conv%d_" % (idx - 1),
                  batch_name,
                  init_weights,
                  batch_norm=True,
              )
          else:
              net = self.conv_layer(
                  net,
                  [3, 3, filter_sizes[idx - 2], filter_sizes[idx - 1]],
                  "conv%d_" % (idx - 1),
                  batch_name,
                  init_weights,
                  batch_norm=True,
              )
  
          if idx < 7:
              if idx < 6:
                  strides = [1, 2, 2, 1]
                  net = self.pooling_layer(
                      net,
                      pool_size=[1, 2, 2, 1],
                      strides=strides,
                      padding="VALID",
                      name="pool%d_" % idx,
                  )
              else:
                  strides = [1, 1, 1, 1]
                  net = self.pooling_layer(
                      net,
                      pool_size=[1, 2, 2, 1],
                      strides=strides,
                      padding="SAME",
                      name="pool%d_" % idx,
                  )
  
      if self.output_size is not None:
          net = self.conv_layer(
              net,
              [1, 1, filter_sizes[idx - 1], self.output_size],
              "conv8_",
              None,
              init_weights,
              batch_norm=False,
          )
  
      return net
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import numpy
  from .._tf_model import TensorFlowModel
  from turicreate._deps.minimal_package import _minimal_package_import_check
  import turicreate.toolkits._tf_utils
label_desc: Use image node_id 7 for calling the ODTensorFlowModel obj's underlying member method code with example usage: obj.tiny_yolo(inputs, init_weights) and returns: net