AlexNet On Tensorflow
caffe的AlexNet可以到/models/bvlc_alexnet/train_val.prototxt 去看看具体的网络结构，这里我会弄点基于Tensorflow的AlexNet， 代码在：
- from numpy import
- import os
- from pylab import
- import numpy as np
- import matplotlib.pyplot as plt
- import matplotlib.cbook as cbook
- import time
- from scipy.misc import imread
- from scipy.misc import imresize
- import matplotlib.image as mpimg
- from scipy.ndimage import filters
- import urllib
- from numpy import random
- import tensorflow as tf
- from caffe_classes import class_names
- train_x = zeros((1, 227,227,3)).astype(float32)
- train_y = zeros((1, 1000))
- xdim = train_x.shape[1:]
- ydim = train_y.shape[1]
- net_data = load("bvlc_alexnet.npy").item()
- def conv(input, kernel, biases, k_h, k_w, c_o, s_h, s_w, padding="VALID", group=1):
- '''From [http://github.com/ethereon/caffe-tensorflow](http://github.com/ethereon/caffe-tensorflow)
- '''
- c_i = input.get_shape()[-1]
- assert c_i%group==0
- assert c_o%group==0
- convolve = lambda i, k: tf.nn.conv2d(i, k, [1, s_h, s_w, 1], padding=padding)
- if group==1:
- conv = convolve(input, kernel)
- else:
- input_groups = tf.split(3, group, input)
- kernel_groups = tf.split(3, group, kernel)
- output_groups = [convolve(i, k) for i,k in zip(input_groups, kernel_groups)]
- conv = tf.concat(3, output_groups)
- return tf.reshape(tf.nn.bias_add(conv, biases), conv.get_shape().as_list())
- x = tf.Variable(i)
- #conv1
- #conv(11, 11, 96, 4, 4, padding='VALID', name='conv1')
- k_h = 11; k_w = 11; c_o = 96; s_h = 4; s_w = 4
- conv1W = tf.Variable(net_data["conv1"][0])
- conv1b = tf.Variable(net_data["conv1"][1])
- conv1_in = conv(x, conv1W, conv1b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=1)
- conv1 = tf.nn.relu(conv1_in)
- #lrn1
- #lrn(2, 2e-05, 0.75, name='norm1')
- radius = 2; alpha = 2e-05; beta = 0.75; bias = 1.0
- lrn1 = tf.nn.local_response_normalization(conv1,
- depth_radius=radius,
- alpha=alpha,
- beta=beta,
- bias=bias)
- #maxpool1
- #max_pool(3, 3, 2, 2, padding='VALID', name='pool1')
- k_h = 3; k_w = 3; s_h = 2; s_w = 2; padding = 'VALID'
- maxpool1 = tf.nn.max_pool(lrn1, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding)
- #conv2
- #conv(5, 5, 256, 1, 1, group=2, name='conv2')
- k_h = 5; k_w = 5; c_o = 256; s_h = 1; s_w = 1; group = 2
- conv2W = tf.Variable(net_data["conv2"][0])
- conv2b = tf.Variable(net_data["conv2"][1])
- conv2_in = conv(maxpool1, conv2W, conv2b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=group)
- conv2 = tf.nn.relu(conv2_in)
- #lrn2
- #lrn(2, 2e-05, 0.75, name='norm2')
- radius = 2; alpha = 2e-05; beta = 0.75; bias = 1.0
- lrn2 = tf.nn.local_response_normalization(conv2,
- depth_radius=radius,
- alpha=alpha,
- beta=beta,
- bias=bias)
- #maxpool2
- #max_pool(3, 3, 2, 2, padding='VALID', name='pool2')
- k_h = 3; k_w = 3; s_h = 2; s_w = 2; padding = 'VALID'
- maxpool2 = tf.nn.max_pool(lrn2, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding)
- #conv3
- #conv(3, 3, 384, 1, 1, name='conv3')
- k_h = 3; k_w = 3; c_o = 384; s_h = 1; s_w = 1; group = 1
- conv3W = tf.Variable(net_data["conv3"][0])
- conv3b = tf.Variable(net_data["conv3"][1])
- conv3_in = conv(maxpool2, conv3W, conv3b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=group)
- conv3 = tf.nn.relu(conv3_in)
- #conv4
- #conv(3, 3, 384, 1, 1, group=2, name='conv4')
- k_h = 3; k_w = 3; c_o = 384; s_h = 1; s_w = 1; group = 2
- conv4W = tf.Variable(net_data["conv4"][0])
- conv4b = tf.Variable(net_data["conv4"][1])
- conv4_in = conv(conv3, conv4W, conv4b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=group)
- conv4 = tf.nn.relu(conv4_in)
- #conv5
- #conv(3, 3, 256, 1, 1, group=2, name='conv5')
- k_h = 3; k_w = 3; c_o = 256; s_h = 1; s_w = 1; group = 2
- conv5W = tf.Variable(net_data["conv5"][0])
- conv5b = tf.Variable(net_data["conv5"][1])
- conv5_in = conv(conv4, conv5W, conv5b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=group)
- conv5 = tf.nn.relu(conv5_in)
- #maxpool5
- #max_pool(3, 3, 2, 2, padding='VALID', name='pool5')
- k_h = 3; k_w = 3; s_h = 2; s_w = 2; padding = 'VALID'
- maxpool5 = tf.nn.max_pool(conv5, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding)
- #fc6
- #fc(4096, name='fc6')
- fc6W = tf.Variable(net_data["fc6"][0])
- fc6b = tf.Variable(net_data["fc6"][1])
- fc6 = tf.nn.relu_layer(tf.reshape(maxpool5, [1, int(prod(maxpool5.get_shape()[1:]))]), fc6W, fc6b)
- #fc7
- #fc(4096, name='fc7')
- fc7W = tf.Variable(net_data["fc7"][0])
- fc7b = tf.Variable(net_data["fc7"][1])
- fc7 = tf.nn.relu_layer(fc6, fc7W, fc7b)
- #fc8
- #fc(1000, relu=False, name='fc8')
- fc8W = tf.Variable(net_data["fc8"][0])
- fc8b = tf.Variable(net_data["fc8"][1])
- fc8 = tf.nn.xw_plus_b(fc7, fc8W, fc8b)
- #prob
- #softmax(name='prob'))
- prob = tf.nn.softmax(fc8)
- init = tf.initialize_all_variables()
- sess = tf.Session()
- sess.run(init)
- output = sess.run(prob)
- ################################################################################
- #Output:
- inds = argsort(output)[0,:]
- for i in range(5):
- print class_names[inds[-1-i]], output[0, inds[-1-i]]
