initial commit for resnet50

"""

ResNet-50 implementation

Deep Residual Learning for Image Recognition,

Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun

https://arxiv.org/abs/1512.03385

"""

import tensorflow as tf

import deepchem as dc

from deepchem.models import Sequential

from deepchem.models.tensorgraph.layers import Conv2D, MaxPool2D, Conv2DTranspose, Concat, Feature, Label, BatchNorm

from deepchem.models.tensorgraph.layers import SoftMaxCrossEntropy, ReduceMean, SoftMax

from deepchem.models import TensorGraph

class ResNet50(TensorGraph):

  """

        ResNet50 architecture implementation.

        Parameters

        ----------

        img_rows : int

         number of rows of the image.

        img_cols: int

         number of columns of the image

        weights: string

         if "imagenet" - weights are initialized with the pretrained values.

        classes: int

         specifies number of classes

    """

  def __init__(self,

               img_rows=224,

               img_cols=224,

               weights="imagenet",

               classes=1000,

               **kwargs):

    super(ResNet50, self).__init__(use_queue=False, **kwargs)

    self.img_cols = img_cols

    self.img_rows = img_rows

    self.weights = weights

    input = Feature(shape=(None, self.img_rows, self.img_cols, 3))

    labels = Label(shape=(None, self.classes))

    conv1 = Conv2D(

        num_outputs=64,

        kernel_size=7,

        stride=2,

        activation='relu',

        padding='same',

        in_layers=[input])

    bn1 = BatchNorm(in_layers=[conv1])

    pool1 = MaxPool2D(ksize=[1, 2, 2, 1], in_layers=[bn1])