import tensorflow as tf
import input_pipeline as ip
import model as m
from configuration import Configuration


def build_model(session, x, exp_name, a_function):

    config = Configuration(
        input_size=3072,
        output_size=10,
        examples_per_epoches=50000,
        batch_size=100,
        a_function=a_function
    )

    dropout = tf.placeholder(tf.float32)

    model = m.ConvoModel(config)
    model.initialize(x, dropout)

    saver = tf.train.Saver(tf.all_variables())

    checkpoint_path = tf.train.latest_checkpoint("experiments/%s" % exp_name)
    saver.restore(session, checkpoint_path)

    return model

88477f77220181d12db0b575f7e5f9251f966f08
<mask token>

<mask token>


def build_model(session, x, exp_name, a_function):
    config = Configuration(input_size=3072, output_size=10,
        examples_per_epoches=50000, batch_size=100, a_function=a_function)
    dropout = tf.placeholder(tf.float32)
    model = m.ConvoModel(config)
    model.initialize(x, dropout)
    saver = tf.train.Saver(tf.all_variables())
    checkpoint_path = tf.train.latest_checkpoint('experiments/%s' % exp_name)
    saver.restore(session, checkpoint_path)
    return model

import tensorflow as tf
import input_pipeline as ip
import model as m
from configuration import Configuration


def build_model(session, x, exp_name, a_function):
    config = Configuration(input_size=3072, output_size=10,
        examples_per_epoches=50000, batch_size=100, a_function=a_function)
    dropout = tf.placeholder(tf.float32)
    model = m.ConvoModel(config)
    model.initialize(x, dropout)
    saver = tf.train.Saver(tf.all_variables())
    checkpoint_path = tf.train.latest_checkpoint('experiments/%s' % exp_name)
    saver.restore(session, checkpoint_path)
    return model

import tensorflow as tf
import input_pipeline as ip
import model as m
from configuration import Configuration


def build_model(session, x, exp_name, a_function):

    config = Configuration(
        input_size=3072,
        output_size=10,
        examples_per_epoches=50000,
        batch_size=100,
        a_function=a_function
    )

    dropout = tf.placeholder(tf.float32)

    model = m.ConvoModel(config)
    model.initialize(x, dropout)

    saver = tf.train.Saver(tf.all_variables())

    checkpoint_path = tf.train.latest_checkpoint("experiments/%s" % exp_name)
    saver.restore(session, checkpoint_path)

    return model
