Alexnet训练mnist和cfar10数据集

TensorFlow实现训练Alexnet网络，训练mnist数据集合cfar数据集。mnist数据集测试准确度0.986.不需要下载权重，因为输出大小不一样。

import tensorflow as tf
import numpy as np
import os

os.environ[‘TF_CPP_MIN_LOG_LEVEL‘] = ‘2‘  


class AlexNet（object）:
    “““Implementation of the AlexNet.“““

    def __init__（self x keep_prob num_classes）:
        “““Create the graph of the AlexNet model.

        Args:
            x: Placeholder for the input tensor.
            keep_prob: Dropout probability.
            num_classes: Number of classes in the dataset.
            skip_layer: List of names of the layer that get trained from
                scratch
            weights_path: Complete path to the pretrained weight file if it
                isn‘t in the same folder as this code
        “““
        # Parse input arguments into class variables
        self.X = x
        self.NUM_CLASSES = num_classes
        self.KEEP_PROB = keep_prob
        # self.SKIP_layer = skip_layer
       # self.WEIGHTS_PATH = weights_path

        # Call the create function to build the computational graph of AlexNet
        self.create（）

    def create（self）:
        “““Create the network graph.“““
        # 1st layer: Conv （w ReLu） -> Lrn -> Pool
        conv1 = conv（self.X [11396] [1111] padding=‘VALID‘ name=‘conv1‘）
        norm1 = lrn（conv1 2 1e-05 0.75 name=‘norm1‘）
        pool1 = max_pool（norm1 1 1 1 1 padding=‘VALID‘ name=‘pool1‘）

        # 2nd layer: Conv （w ReLu）  -> Lrn -> Pool with 2 groups
        conv2 = conv（pool1 [3 396256] [1 111] padding=‘VALID‘name=‘conv2‘）
        norm2 = lrn（conv2 2 1e-05 0.75 name=‘norm2‘）
        pool2 = max_pool（norm2 2 2 2 2 padding=‘VALID‘ name=‘pool2‘）

        # 3rd layer: Conv （w ReLu）
        conv3 = conv（pool2 [3 3256 384] [1111] name=‘conv3‘）

        # 4th layer: Conv （w ReLu） splitted into two groups
        conv4 = conv（conv3 [3 3 384 384] [1 1 1 1]  name=‘conv4‘）

        # 5th layer: Conv （w ReLu） -> Pool splitted into two groups
        conv5 = conv（conv4 [3 3384 256] [1 111] name=‘conv5‘）
        pool5 = max_pool（conv5 3 3 2 2 padding=‘VALID‘ name=‘pool5‘）

        # 6th layer: Flatten -> FC （w ReLu） -> Dropout
        flattened = tf.reshape（pool5 [-1 6 * 6 * 256]）
        fc6 = fc（flattened 6 * 6 * 256 4096 name=‘fc6‘）
        dropout6 = dropout（fc6 self.KEEP_PROB）

        # 7th layer: FC （w ReLu） -> Dropout
        fc7 = fc（dropout6 4096 4096 name=‘fc7‘）
        dropout7 = dropout（fc7 self.KEEP_PROB）

        # 8th layer: FC and return unscaled activations
        self.fc8 = fc（dropout7 4096 self.NUM_CLASSES relu = Falsename=‘fc8‘）


def conv（input_tensorfilterstridesnamepadding = “SAME“）:
    with tf.variable_scope（name）:
        weights = tf.get_variable（‘weights‘filterinitializer=tf.truncated_normal_initializer（stddev=0.1））
        biases = tf.get_variable（‘biases‘filter[3]initializer=tf.constant_initializer（0.0））
        conv1 = tf.nn.conv2d（input_tensorweightsstride

 属性            大小     日期    时间   名称
----------- ---------  ---------- -----  ----

     文件       4615  2018-04-29 16:44  Alexnet\Alexnet.py

     文件        104  2018-04-24 21:23  Alexnet\classes.py

     文件       9075  2018-03-16 09:29  Alexnet\eval.py

     文件       6838  2018-04-27 10:52  Alexnet\small_test.py

     文件     130839  2018-04-24 22:27  Alexnet\tensorboard\events.out.tfevents.1524580059.OS-201709090818

     文件     130839  2018-04-24 22:37  Alexnet\tensorboard\events.out.tfevents.1524580658.OS-201709090818

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............此处省略12个文件信息