之前一篇文章中总结了CNN中图像分类的经典模型，包括论文解读和分析，但是不写个代码搞一把总觉得虚～ 啊哈哈 这个系列里准备把这些个经典模型用tensorflow实现一下。
参考之前引用的blog：深度学习AlexNet模型详细分析
上代码吧。参照着模型看更好读一些。
'''
图像分类模型的tensorflow实现之--AlexNet
Tensorflow Version: 1.4
Python Version: 3.6
Refs: https://blog.csdn.net/zyqdragon/article/details/72353420
bms
2018-10-25
'''
import tensorflow as tf
import numpy as np
class AlexNet(object):
'''
#use like this:
model = AlexNet(input, num_classes, keep_prob, is_training)
score = model.fc8
# then you can get loss op using score
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = score, labels = y))
'''
def __init__(self, input, num_classes, keep_prob=0.5, is_training=True):
self.INPUT = input
self.NUM_CLASSES = num_classes
self.KEEP_PROB = keep_prob
self.IS_TRAINING = is_training
self.default_image_size = 224
self.create()
def create(self):
# 1st Layer : conv -> pool -> lrn
conv1 = conv(self.INPUT, 11, 11, 96, 4, 4, padding='VALID', name='conv1')
pool1 = max_pool(conv1, 3, 3, 2, 2, padding='VALID', name='pool1')
norm1 = lrn(pool1, 2, 2e-05, 0.75, name='norm1')
# 2nd Layer: Conv -> Pool -> Lrn
conv2 = conv(norm1, 5, 5, 256, 1, 1, name='conv2')
pool2 = max_pool(conv2, 3, 3, 2, 2, padding='VALID', name='pool2')
norm2 = lrn(pool2, 2, 2e-05, 0.75, name='norm2')
# 3rd Layer: Conv
conv3 = conv(norm2, 3, 3, 384, 1, 1, name='conv3')
# 4th Layer: Conv
conv4 = conv(conv3, 3, 3, 384, 1, 1, name='conv4')
# 5th Layer: Conv -> Pool
conv5 = conv(conv4, 3, 3, 256, 1, 1, name='conv5')
pool5 = max_pool(conv5, 3, 3, 2, 2, padding='VALID', name='pool5')
# 6th Layer: Flatten -> FC -> Dropout
flattened = tf.reshape(pool5, [-1, 6 * 6 * 256])
fc6 = fc(flattened, 6 * 6 * 256, 4096, name='fc6')
dropout6 = dropout(fc6, self.KEEP_PROB, is_training=self.IS_TRAINING)
# 7th Layer: FC (w ReLu) -> Dropout
fc7 = fc(dropout6, 4096, 4096, name='fc7')
dropout7 = dropout(fc7, self.KEEP_PROB, is_training=self.IS_TRAINING)
# 8th Layer: FC and return unscaled activations (for tf.nn.softmax_cross_entropy_with_logits)
self.fc8 = fc(dropout7, 4096, self.NUM_CLASSES, name='fc8', is_relu=False)
def conv(input, filter_height, filter_width, num_filters, stride_x, stride_y, name, padding='SAME' ):
'''
先定义conv的通用模式
'''
input_channels = int(input.get_shape()[-1])
convolve = lambda i, k: tf.nn.conv2d(i, k,
strides=[1, stride_y, stride_x, 1],
padding=padding)
with tf.variable_scope(name) as scope:
weights = tf.get_variable('weights', shape=[filter_height, filter_width, input_channels, num_filters])
biases = tf.get_variable('biases', shape=[num_filters])
conv = tf.nn.conv2d(input, weights,
strides=[1, stride_y, stride_x, 1],
padding=padding)
bias = tf.nn.bias_add(conv, biases)
relu = tf.nn.relu(bias, name=scope.name)
return relu
def fc(input, num_input, num_output, name, is_relu=True):
'''定义全连接层'''
with tf.variable_scope(name) as scope:
weights = tf.get_variable('weights', shape=[num_input, num_output], trainable=True)
biases = tf.get_variable('biases', [num_output], trainable=True)
act = tf.nn.xw_plus_b(input, weights, biases, name=scope.name)
if is_relu:
return tf.nn.relu(act, name=scope.name)
else:
return act
def max_pool(input, filter_height, filter_width, stride_x, stride_y, name, padding='SAME'):
return tf.nn.max_pool(input,
ksize=[1, filter_height, filter_width, 1],
strides=[1, stride_x, stride_y, 1],
padding=padding,
name=name)
def lrn(input, radius=2, alpha=2e-05, beta=0.75, bias=1.0, name=''):
return tf.nn.local_response_normalization(input,
depth_radius=radius,
alpha=alpha,
beta=beta,
bias=bias,
name=name)
def dropout(input, keep_prob=0.5, is_training=True):
if is_training:
return tf.nn.dropout(input, keep_prob)
else:
return input
嗯 实现起来有点麻烦啊。看一下TF的slim的实现(https://github.com/tensorflow/models/blob/master/research/slim/nets/alexnet.py)：
def alexnet_v2(inputs,
num_classes=1000,
is_training=True,
dropout_keep_prob=0.5,
spatial_squeeze=True,
scope='alexnet_v2',
global_pool=False):
"""AlexNet version 2.
Described in: http://arxiv.org/pdf/1404.5997v2.pdf
Parameters from:
github.com/akrizhevsky/cuda-convnet2/blob/master/layers/
layers-imagenet-1gpu.cfg
Note: All the fully_connected layers have been transformed to conv2d layers.
To use in classification mode, resize input to 224x224 or set
global_pool=True. To use in fully convolutional mode, set
spatial_squeeze to false.
The LRN layers have been removed and change the initializers from
random_normal_initializer to xavier_initializer.
Args:
inputs: a tensor of size [batch_size, height, width, channels].
num_classes: the number of predicted classes. If 0 or None, the logits layer
is omitted and the input features to the logits layer are returned instead.
is_training: whether or not the model is being trained.
dropout_keep_prob: the probability that activations are kept in the dropout
layers during training.
spatial_squeeze: whether or not should squeeze the spatial dimensions of the
logits. Useful to remove unnecessary dimensions for classification.
scope: Optional scope for the variables.
global_pool: Optional boolean flag. If True, the input to the classification
layer is avgpooled to size 1x1, for any input size. (This is not part
of the original AlexNet.)
Returns:
net: the output of the logits layer (if num_classes is a non-zero integer),
or the non-dropped-out input to the logits layer (if num_classes is 0
or None).
end_points: a dict of tensors with intermediate activations.
"""
with tf.variable_scope(scope, 'alexnet_v2', [inputs]) as sc:
end_points_collection = sc.original_name_scope + '_end_points'
# Collect outputs for conv2d, fully_connected and max_pool2d.
with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
outputs_collections=[end_points_collection]):
net = slim.conv2d(inputs, 64, [11, 11], 4, padding='VALID',
scope='conv1')
net = slim.max_pool2d(net, [3, 3], 2, scope='pool1')
net = slim.conv2d(net, 192, [5, 5], scope='conv2')
net = slim.max_pool2d(net, [3, 3], 2, scope='pool2')
net = slim.conv2d(net, 384, [3, 3], scope='conv3')
net = slim.conv2d(net, 384, [3, 3], scope='conv4')
net = slim.conv2d(net, 256, [3, 3], scope='conv5')
net = slim.max_pool2d(net, [3, 3], 2, scope='pool5')
# Use conv2d instead of fully_connected layers.
with slim.arg_scope([slim.conv2d],
weights_initializer=trunc_normal(0.005),
biases_initializer=tf.constant_initializer(0.1)):
net = slim.conv2d(net, 4096, [5, 5], padding='VALID',
scope='fc6')
net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
scope='dropout6')
net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
# Convert end_points_collection into a end_point dict.
end_points = slim.utils.convert_collection_to_dict(
end_points_collection)
if global_pool:
net = tf.reduce_mean(net, [1, 2], keep_dims=True, name='global_pool')
end_points['global_pool'] = net
if num_classes:
net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
scope='dropout7')
net = slim.conv2d(net, num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
biases_initializer=tf.zeros_initializer(),
scope='fc8')
if spatial_squeeze:
net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
end_points[sc.name + '/fc8'] = net
return net, end_points
alexnet_v2.default_image_size = 224
只看模型构建部分，如此简洁。好吧，slim还是很方便的。
注意这个AlexNet是v2版本，最后三层的全连接换成了卷积层。这样输出的维度是一致的，不过由于使用了卷积，融合了多通道的信息，效果会更好些。