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。