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  • tflearn alexnet iter 10

    他会自己下载数据:

    # -*- coding: utf-8 -*-
    
    """ AlexNet.
    Applying 'Alexnet' to Oxford's 17 Category Flower Dataset classification task.
    References:
        - Alex Krizhevsky, Ilya Sutskever & Geoffrey E. Hinton. ImageNet
        Classification with Deep Convolutional Neural Networks. NIPS, 2012.
        - 17 Category Flower Dataset. Maria-Elena Nilsback and Andrew Zisserman.
    Links:
        - [AlexNet Paper](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)
        - [Flower Dataset (17)](http://www.robots.ox.ac.uk/~vgg/data/flowers/17/)
    """
    
    from __future__ import division, print_function, absolute_import
    
    import tflearn
    from tflearn.layers.core import input_data, dropout, fully_connected
    from tflearn.layers.conv import conv_2d, max_pool_2d
    from tflearn.layers.normalization import local_response_normalization
    from tflearn.layers.estimator import regression
    
    import tflearn.datasets.oxflower17 as oxflower17
    X, Y = oxflower17.load_data(one_hot=True, resize_pics=(227, 227))
    
    # Building 'AlexNet'
    network = input_data(shape=[None, 227, 227, 3])
    network = conv_2d(network, 96, 11, strides=4, activation='relu')
    network = max_pool_2d(network, 3, strides=2)
    network = local_response_normalization(network)
    network = conv_2d(network, 256, 5, activation='relu')
    network = max_pool_2d(network, 3, strides=2)
    network = local_response_normalization(network)
    network = conv_2d(network, 384, 3, activation='relu')
    network = conv_2d(network, 384, 3, activation='relu')
    network = conv_2d(network, 256, 3, activation='relu')
    network = max_pool_2d(network, 3, strides=2)
    network = local_response_normalization(network)
    network = fully_connected(network, 4096, activation='tanh')
    network = dropout(network, 0.5)
    network = fully_connected(network, 4096, activation='tanh')
    network = dropout(network, 0.5)
    network = fully_connected(network, 17, activation='softmax')
    network = regression(network, optimizer='momentum',
                         loss='categorical_crossentropy',
                         learning_rate=0.001)
    
    # Training
    model = tflearn.DNN(network, checkpoint_path='model_alexnet',
                        max_checkpoints=1, tensorboard_verbose=2)
    #model.fit(X, Y, n_epoch=1000, validation_set=0.1, shuffle=True,
    model.fit(X, Y, n_epoch=10, validation_set=0.1, shuffle=True,
              show_metric=True, batch_size=64, snapshot_step=200,
    snapshot_epoch=False, run_id='alexnet_oxflowers17')
    model.save('flower-classifier')




    打开tensotboard: tensorboard --logdir=/tmp/tflearn_logs/


    通过tensorboard查看准确率变化以及loss变化,上图是跑了10个epoch的结果。

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  • 原文地址:https://www.cnblogs.com/bonelee/p/8486074.html
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