from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow.contrib import layers
from tensorflow.contrib.slim import arg_scope
from tensorflow.contrib.slim.python.slim.nets import resnet_utils
from tensorflow.contrib.slim.python.slim.nets import resnet_v1
from tensorflow.contrib.slim.python.slim.nets.resnet_v1 import resnet_v1_block
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np


def resnet_v1_101_c4(inputs, num_classes=None, is_training=True,
    global_pool=True, output_stride=None, spatial_squeeze=True, reuse=None,
    scope='resnet_v1_101'):
    """ResNet-101 model of [1]. See resnet_v1() for arg and return description."""
    blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=
        1), resnet_v1_block('block2', base_depth=128, num_units=4, stride=2
        ), resnet_v1_block('block3', base_depth=256, num_units=23, stride=2)]
    return resnet_v1.resnet_v1(inputs, blocks, num_classes, is_training,
        global_pool=global_pool, output_stride=output_stride,
        include_root_block=True, reuse=reuse, scope=scope)


def resnet_v1_101_c1(inputs, num_classes=None, is_training=True,
    global_pool=True, output_stride=None, spatial_squeeze=True, reuse=None,
    scope='resnet_v1_101'):
    """ResNet-101 model of [1]. See resnet_v1() for arg and return description."""
    blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=1)]
    return resnet_v1.resnet_v1(inputs, blocks, num_classes, is_training,
        global_pool=global_pool, output_stride=output_stride,
        include_root_block=True, reuse=reuse, scope=scope)

95074a5297f39e1abd99814eb4afba6bd7550943
<mask token>

<mask token>


def resnet_v1_101_c4(inputs, num_classes=None, is_training=True,
    global_pool=True, output_stride=None, spatial_squeeze=True, reuse=None,
    scope='resnet_v1_101'):
    """ResNet-101 model of [1]. See resnet_v1() for arg and return description."""
    blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=
        1), resnet_v1_block('block2', base_depth=128, num_units=4, stride=2
        ), resnet_v1_block('block3', base_depth=256, num_units=23, stride=2)]
    return resnet_v1.resnet_v1(inputs, blocks, num_classes, is_training,
        global_pool=global_pool, output_stride=output_stride,
        include_root_block=True, reuse=reuse, scope=scope)


<mask token>

<mask token>


def resnet_v1_101_c4(inputs, num_classes=None, is_training=True,
    global_pool=True, output_stride=None, spatial_squeeze=True, reuse=None,
    scope='resnet_v1_101'):
    """ResNet-101 model of [1]. See resnet_v1() for arg and return description."""
    blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=
        1), resnet_v1_block('block2', base_depth=128, num_units=4, stride=2
        ), resnet_v1_block('block3', base_depth=256, num_units=23, stride=2)]
    return resnet_v1.resnet_v1(inputs, blocks, num_classes, is_training,
        global_pool=global_pool, output_stride=output_stride,
        include_root_block=True, reuse=reuse, scope=scope)


def resnet_v1_101_c1(inputs, num_classes=None, is_training=True,
    global_pool=True, output_stride=None, spatial_squeeze=True, reuse=None,
    scope='resnet_v1_101'):
    """ResNet-101 model of [1]. See resnet_v1() for arg and return description."""
    blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=1)]
    return resnet_v1.resnet_v1(inputs, blocks, num_classes, is_training,
        global_pool=global_pool, output_stride=output_stride,
        include_root_block=True, reuse=reuse, scope=scope)

from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow.contrib import layers
from tensorflow.contrib.slim import arg_scope
from tensorflow.contrib.slim.python.slim.nets import resnet_utils
from tensorflow.contrib.slim.python.slim.nets import resnet_v1
from tensorflow.contrib.slim.python.slim.nets.resnet_v1 import resnet_v1_block
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np


def resnet_v1_101_c4(inputs, num_classes=None, is_training=True,
    global_pool=True, output_stride=None, spatial_squeeze=True, reuse=None,
    scope='resnet_v1_101'):
    """ResNet-101 model of [1]. See resnet_v1() for arg and return description."""
    blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=
        1), resnet_v1_block('block2', base_depth=128, num_units=4, stride=2
        ), resnet_v1_block('block3', base_depth=256, num_units=23, stride=2)]
    return resnet_v1.resnet_v1(inputs, blocks, num_classes, is_training,
        global_pool=global_pool, output_stride=output_stride,
        include_root_block=True, reuse=reuse, scope=scope)


def resnet_v1_101_c1(inputs, num_classes=None, is_training=True,
    global_pool=True, output_stride=None, spatial_squeeze=True, reuse=None,
    scope='resnet_v1_101'):
    """ResNet-101 model of [1]. See resnet_v1() for arg and return description."""
    blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=1)]
    return resnet_v1.resnet_v1(inputs, blocks, num_classes, is_training,
        global_pool=global_pool, output_stride=output_stride,
        include_root_block=True, reuse=reuse, scope=scope)
