def model(inputs, is_training=False):
    'Creation of the model graph.'
    inputs = nn_ops.conv2d_fixed_padding(inputs=inputs, filters=64, kernel_size=7, strides=2, data_format=self._data_format)
    inputs = tf.identity(inputs, 'initial_conv')
    inputs = self._batch_norm_relu(inputs, is_training=is_training)
    inputs = tf.layers.max_pooling2d(inputs=inputs, pool_size=3, strides=2, padding='SAME', data_format=self._data_format)
    inputs = tf.identity(inputs, 'initial_max_pool')
    c2 = block_group(inputs=inputs, filters=64, strides=1, use_projection=True, block_fn=block_fn, block_repeats=layers[0], batch_norm_relu=self._batch_norm_relu, dropblock=self._dropblock, drop_connect_rate=get_drop_connect_rate(self._init_drop_connect_rate, 2, 5), name='block_group1', is_training=is_training)
    c3 = block_group(inputs=c2, filters=128, strides=2, use_projection=True, block_fn=block_fn, block_repeats=layers[1], batch_norm_relu=self._batch_norm_relu, dropblock=self._dropblock, drop_connect_rate=get_drop_connect_rate(self._init_drop_connect_rate, 3, 5), name='block_group2', is_training=is_training)
    c4 = block_group(inputs=c3, filters=256, strides=2, use_projection=True, block_fn=block_fn, block_repeats=layers[2], batch_norm_relu=self._batch_norm_relu, dropblock=self._dropblock, drop_connect_rate=get_drop_connect_rate(self._init_drop_connect_rate, 4, 5), name='block_group3', is_training=is_training)
    c5 = block_group(inputs=c4, filters=512, strides=2, use_projection=True, block_fn=block_fn, block_repeats=layers[3], batch_norm_relu=self._batch_norm_relu, dropblock=self._dropblock, drop_connect_rate=get_drop_connect_rate(self._init_drop_connect_rate, 5, 5), name='block_group4', is_training=is_training)
    return {2: c2, 3: c3, 4: c4, 5: c5}
Creation of the model graph.
def model(inputs, is_training=False):
    
    inputs = nn_ops.conv2d_fixed_padding(inputs=inputs, filters=64, kernel_size=7, strides=2, data_format=self._data_format)
    inputs = tf.identity(inputs, 'initial_conv')
    inputs = self._batch_norm_relu(inputs, is_training=is_training)
    inputs = tf.layers.max_pooling2d(inputs=inputs, pool_size=3, strides=2, padding='SAME', data_format=self._data_format)
    inputs = tf.identity(inputs, 'initial_max_pool')
    c2 = block_group(inputs=inputs, filters=64, strides=1, use_projection=True, block_fn=block_fn, block_repeats=layers[0], batch_norm_relu=self._batch_norm_relu, dropblock=self._dropblock, drop_connect_rate=get_drop_connect_rate(self._init_drop_connect_rate, 2, 5), name='block_group1', is_training=is_training)
    c3 = block_group(inputs=c2, filters=128, strides=2, use_projection=True, block_fn=block_fn, block_repeats=layers[1], batch_norm_relu=self._batch_norm_relu, dropblock=self._dropblock, drop_connect_rate=get_drop_connect_rate(self._init_drop_connect_rate, 3, 5), name='block_group2', is_training=is_training)
    c4 = block_group(inputs=c3, filters=256, strides=2, use_projection=True, block_fn=block_fn, block_repeats=layers[2], batch_norm_relu=self._batch_norm_relu, dropblock=self._dropblock, drop_connect_rate=get_drop_connect_rate(self._init_drop_connect_rate, 4, 5), name='block_group3', is_training=is_training)
    c5 = block_group(inputs=c4, filters=512, strides=2, use_projection=True, block_fn=block_fn, block_repeats=layers[3], batch_norm_relu=self._batch_norm_relu, dropblock=self._dropblock, drop_connect_rate=get_drop_connect_rate(self._init_drop_connect_rate, 5, 5), name='block_group4', is_training=is_training)
    return {2: c2, 3: c3, 4: c4, 5: c5}