3.5 卷积神经网络进阶-Inception-mobile_ne
2018-10-07 本文已影响4人
9c0ddf06559c
4.2.5 Inception-mobile_net实战
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Inception-Net
Inception Net的思想是分组卷积，上一层分成几组卷积，卷积完成之后在把分组的结果拼接起来
可以进行扩展，每个组有很多层，这里只实现基本的分组卷积
# 定义 Inception-Net的分组结构 def inception_block(x, output_channel_for_each_path, name): """inception block implementation""" """ Args: - x: 输入数据 - output_channel_for_each_path: 每组的输出通道数目 eg: [10,20,30] - name: 每组的卷积命名 """ # variable_scope 在这个scope下命名不会有冲突 conv1 = 'conv1' => scope_name/conv1 with tf.variable_scope(name): conv1_1 = tf.layers.conv2d(x, output_channel_for_each_path[0], (1, 1), strides = (1,1), padding = 'same', activation = tf.nn.relu, name = 'conv1_1') conv3_3 = tf.layers.conv2d(x, output_channel_for_each_path[1], (3, 3), strides = (1,1), padding = 'same', activation = tf.nn.relu, name = 'conv3_3') conv5_5 = tf.layers.conv2d(x, output_channel_for_each_path[0], (5, 5), strides = (1,1), padding = 'same', activation = tf.nn.relu, name = 'conv5_5') max_pooling = tf.layers.max_pooling2d(x, (2,2), (2,2), name = 'max_pooling') # max_pooling 会使得图像变小，所以需要padding max_pooling_shape = max_pooling.get_shape().as_list()[1:] input_shape = x.get_shape().as_list()[1:] width_padding = (input_shape[0] - max_pooling_shape[0]) // 2 height_padding = (input_shape[1] - max_pooling_shape[1]) // 2 padded_pooling = tf.pad(max_pooling, [[0,0], [width_padding,width_padding], [height_padding,height_padding], [0,0]]) # 在第四个维度（通道数）上做拼接 concat_layer = tf.concat( [conv1_1, conv3_3, conv5_5, padded_pooling], axis = 3) return concat_layer x = tf.placeholder(tf.float32, [None, 3072]) y = tf.placeholder(tf.int64, [None]) # 将向量变成具有三通道的图片的格式 x_image = tf.reshape(x, [-1,3,32,32]) # 32*32 x_image = tf.transpose(x_image, perm = [0, 2, 3, 1]) # 先经过一个普通的卷积层和池化层 # conv1：神经元图,feature map,输出图像 conv1 = tf.layers.conv2d(x_image, 32, # output channel number (3,3), # kernal size padding = 'same', # same 代表输出图像的大小没有变化，valid 代表不做padding activation = tf.nn.relu, name = 'conv1') # 16*16 pooling1 = tf.layers.max_pooling2d(conv1, (2, 2), # kernal size (2, 2), # stride name = 'pool1' # name为了给这一层做一个命名，这样会让图打印出来的时候会是一个有意义的图 ) # 经过两个个分组卷积 inception_2a = inception_block(pooling1, [16, 16, 16], name = 'inception_2a') inception_2b = inception_block(inception_2a, [16, 16, 16], name = 'inception_2b') # 接一个池化 pooling2 = tf.layers.max_pooling2d(inception_2b, (2, 2), (2, 2), name = 'pool2' ) # 再经过两个分组卷积核一个池化 inception_3a = inception_block(pooling2, [16, 16, 16], name = 'inception_3a') inception_3b = inception_block(inception_3a, [16, 16, 16], name = 'inception_3b') pooling3 = tf.layers.max_pooling2d(inception_3b, (2, 2), (2, 2), name = 'pool3' ) # [None, 4*4*42] 将三通道的图形转换成矩阵 flatten = tf.layers.flatten(pooling3) y_ = tf.layers.dense(flatten, 10) # 交叉熵 loss = tf.losses.sparse_softmax_cross_entropy(labels=y, logits=y_) # y_-> softmax # y -> one_hot # loss = ylogy_ # bool predict = tf.argmax(y_, 1) # [1,0,1,1,1,0,0,0] correct_prediction = tf.equal(predict, y) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float64)) with tf.name_scope('train_op'): train_op = tf.train.AdamOptimizer(1e-3).minimize(loss) -
Mobile-Net
Mobile Net 的基本结构 深度可分类的卷积 -> BN ->RELU-> 1*1 的卷积 -> BN -> RELU
这里BN先不加，这是下节课的内容
image.png
def separable_conv_block(x,
output_channel_number,
name):
"""separable_conv block implementation"""
"""
Args:
- x: 输入数据
- output_channel_number: 经过深度可分离卷积之后，再经过1*1 的卷积生成的通道数目
- name: 每组的卷积命名
"""
# variable_scope 在这个scope下命名不会有冲突 conv1 = 'conv1' => scope_name/conv1
with tf.variable_scope(name):
input_channel = x.get_shape().as_list()[-1]
# 将x 在 第四个维度（axis+1） 上 拆分成 input_channel 份
# channel_wise_x: [channel1, channel2, ...]
channel_wise_x = tf.split(x, input_channel, axis = 3)
output_channels = []
for i in range(len(channel_wise_x)):
output_channel = tf.layers.conv2d(channel_wise_x[i],
1,
(3,3),
strides = (1,1),
padding = 'same',
activation = tf.nn.relu,
name = 'conv_%d' % i)
output_channels.append(output_channel)
concat_layers = tf.concat(output_channels, axis = 3)
conv1_1 = tf.layers.conv2d(concat_layers,
output_channel_number,
(1,1),
strides = (1,1),
padding = 'same',
activation = tf.nn.relu,
name = 'conv1_1')
return conv1_1
x = tf.placeholder(tf.float32, [None, 3072])
y = tf.placeholder(tf.int64, [None])
# 将向量变成具有三通道的图片的格式
x_image = tf.reshape(x, [-1,3,32,32])
# 32*32
x_image = tf.transpose(x_image, perm = [0, 2, 3, 1])
# conv1：神经元图,feature map,输出图像
conv1 = tf.layers.conv2d(x_image,
32, # output channel number
(3,3), # kernal size
padding = 'same', # same 代表输出图像的大小没有变化，valid 代表不做padding
activation = tf.nn.relu,
name = 'conv1')
# 16*16
pooling1 = tf.layers.max_pooling2d(conv1,
(2, 2), # kernal size
(2, 2), # stride
name = 'pool1' # name为了给这一层做一个命名，这样会让图打印出来的时候会是一个有意义的图
)
separable_2a = separable_conv_block(pooling1,
32,
name = 'separable_2a')
separable_2b = separable_conv_block(separable_2a,
32,
name = 'separable_2b')
pooling2 = tf.layers.max_pooling2d(separable_2b,
(2, 2),
(2, 2),
name = 'pool2'
)
separable_3a = separable_conv_block(pooling2,
32,
name = 'separable_3a')
separable_3b = separable_conv_block(separable_3a,
32,
name = 'separable_3b')
pooling3 = tf.layers.max_pooling2d(separable_3b,
(2, 2),
(2, 2),
name = 'pool3')
# [None, 4*4*42] 将三通道的图形转换成矩阵
flatten = tf.layers.flatten(pooling3)
y_ = tf.layers.dense(flatten, 10)
# 交叉熵
loss = tf.losses.sparse_softmax_cross_entropy(labels=y, logits=y_)
# y_-> softmax
# y -> one_hot
# loss = ylogy_
# bool
predict = tf.argmax(y_, 1)
# [1,0,1,1,1,0,0,0]
correct_prediction = tf.equal(predict, y)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float64))
with tf.name_scope('train_op'):
train_op = tf.train.AdamOptimizer(1e-3).minimize(loss)
这里的准确率是10000次百分之60，这是因为mobile net 的 参数减小和计算率减小影响了准确率。
- 这里的训练我们都使用的是一万次训练，真正的神经网络训练远不止于此，可能会达到100万次的规模
