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tensoralexnet.py
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#coding=utf-8
#基于Tensorflow 的AlexNet实现
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):
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)
#conv 1
#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 , group2 , 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 , kszie = [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 = 2
conv3w = tf.Variable(net_data['conv3'][0])
conv3b = tf.Variable(net_data['conv3'][1])
conv3_in = conv(maxpool2 , conv3w , conv3b , k_j , 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 , 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()
tf.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]]