import tensorflow as tf

from six.moves import urllib
import tensorflow as tf

# from tensorflow.models.image.cifar10 import cifar10_input
import convert_to_records as records
import numpy as np
FLAGS = tf.app.flags.FLAGS

# Basic model parameters.
tf.app.flags.DEFINE_float('learning_rate', 0.1, 'Initial learning rate.')
tf.app.flags.DEFINE_string('pm', '66661', 'pooling scheme across scales.  Each number specifies the number of scales remaining at each layer. The first number has to be the same as used in --num_scales.')
tf.app.flags.DEFINE_integer('conv_kernel', 5, 'Size of convolutional kernel')
tf.app.flags.DEFINE_integer('pool_kernel', 3, 'Size of spatial pooling kernel')
tf.app.flags.DEFINE_integer('feats_per_layer', 32, 'Number of feature channels at each layer')
tf.app.flags.DEFINE_boolean('total_pool', True, 'If true, pool all feature maps to 1x1 size in final layer')
tf.app.flags.DEFINE_integer('pool_stride', '1', 'If 2, we get progressive pooling - with overlap pooling, AlexNet style')


TRAIN_FILE = 'train_{}.tfrecords'.format(records.tfrecord_name())
VALIDATION_FILE = 'validation_{}.tfrecords'.format(records.tfrecord_name())

TEST_FILE = 'test_{}.tfrecords'.format(records.tfrecord_name())
