Minimal Implementation of TensorFlow models.

model.py

# -*- coding: utf-8 -*-
# file: models.py
# author: JinTian
# time: 11/05/2017 3:03 PM
# Copyright 2017 JinTian. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ------------------------------------------------------------------------
"""
Minimal Implementation of TensorFlow models.

"""
from tensorflow.contrib import slim
import tensorflow as tf


class FaNet(object):
    """
    FaNet, is a simple but powerful net implement by Jin FaGang
    FaNet consist of 6 CNN layers build on tiny data set reaches
    a state-of-art accuracy result.
    """

    def __init__(self, num_classes, lr=0.0001):
        self.num_classes = num_classes
        self.lr = lr

        self._make_graph()

    def _make_graph(self):
        self._init_inputs()
        self._create_net()
        self._init_optimizer()

    def _init_inputs(self):
        self.inputs = tf.placeholder(tf.float32, [None, 256, 256, 3])
        self.labels = tf.placeholder(tf.int32, [None, self.num_classes])

    def _create_net(self, is_train=True):
        # net definition here
        return net

    def _init_optimizer(self):
        self.loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=self.net, labels=self.labels))
        self.train_op = tf.train.AdamOptimizer(learning_rate=self.lr).minimize(self.loss)
        self.global_step = tf.Variable(initial_value=0)

    def inference_outputs(self, n_top=5):
        outputs = tf.nn.softmax(self._create_net(is_train=False))
        inference_index, inference_prob = tf.nn.top_k(outputs, k=n_top)
        return inference_index, inference_prob

    def make_train_inputs(self, inputs, labels):
        if isinstance(inputs, tf.Tensor):
            inputs = inputs.eval()
        if isinstance(labels, tf.Tensor):
            labels = labels.eval()
        return {
            self.inputs: inputs,
            self.labels: labels
        }

    def make_inference_inputs(self, inputs):
        return {
            self.inputs: inputs
        }