实现inception4网络，这是googlenet与resnet结合的卷积神经网络。
本文按照论文复现，论文中的InceptionA，InceptionB，InceptionC，以及reduction模块已写入函数，此次也将卷积函数简化，具体见demo：
# -*- coding: utf-8 -*-
"""
Created on Fri May 31 2019
@author: Ruoyu Chen
The Inceptionv4 networks
"""
import tensorflow as tf
BATCH_SIZE = 10
def weight_variable(shape, name=None):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial, name)
def bias_variable(shape, name=None):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial, name)
def conv2d(input, filter, strides, padding="SAME", name=None):
# filters with shape [filter_height * filter_width * in_channels, output_channels]
# Must have strides[0] = strides[3] =1
# For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1]
'''
Args:
input: A Tensor. Must be one of the following types: float32, float64.
filter: A Tensor. Must have the same type as input.
strides: A list of ints. 1-D of length 4. The stride of the sliding window for each dimension of input.
padding: A string from: "SAME", "VALID". The type of padding algorithm to use.
use_cudnn_on_gpu: An optional bool. Defaults to True.
name: A name for the operation (optional).
'''
return tf.nn.conv2d(input, filter, strides, padding=padding, name=name) # padding="SAME"用零填充边界
def Conv(input, name, filter_size, bias_size, stride, padding = 'SAME'):
with tf.name_scope(name):
with tf.name_scope('Variable'):
filters = weight_variable(filter_size, name='filter')
bias = weight_variable(bias_size, name='bias')
with tf.name_scope("Convolution"):
layer = tf.nn.relu(conv2d(input, filters, strides=stride, padding = padding) + bias)
return layer
def stem(image):
# input shape(299,299,3)
with tf.name_scope('steam'):
net = Conv(input=image, name = 'Layer1', filter_size = [3,3,3,32], bias_size = [32],padding = 'VALID', stride = [1,2,2,1])
net = Conv(input=net, name = 'Layer2', filter_size = [3,3,32,32], bias_size = [32], padding = 'VALID', stride = [1,1,1,1])
net = Conv(input=net, name = 'Layer3', filter_size = [3,3,32,64], bias_size = [64], padding = 'SAME', stride = [1,1,1,1])
with tf.name_scope('MaxPool_1'):
maxpool = tf.nn.max_pool(net, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding="VALID",name='maxpool')
net = Conv(input=net, name = 'Layer4', filter_size = [3,3,64,96], bias_size = [96], padding="VALID", stride = [1,2,2,1])
with tf.name_scope('Filter_concat_1'):
net = tf.concat([maxpool, net], 3)
net_1 = Conv(input=net, name = 'Layer5_1', filter_size = [1,1,160,64], bias_size = [64], padding="SAME", stride = [1,1,1,1])
net_1 = Conv(input=net_1, name = 'Layer6_1', filter_size = [3,3,64,96], bias_size = [96], padding="VALID",stride = [1,1,1,1])
net_2 = Conv(input=net, name = 'Layer5_2', filter_size = [1,1,160,64], bias_size = [64], stride = [1,1,1,1])
net_2 = Conv(input=net_2, name = 'Layer6_2', filter_size = [7,1,64,64], bias_size = [64], stride = [1,1,1,1])
net_2 = Conv(input=net_2, name = 'Layer7_2', filter_size = [1,7,64,64], bias_size = [64], stride = [1,1,1,1])
net_2 = Conv(input=net_2, name = 'Layer8_2', filter_size = [3,3,64,96], bias_size = [96], padding="VALID", stride = [1,1,1,1])
with tf.name_scope('Filter_concat_2'):
net = tf.concat([net_1, net_2], 3)
net_1 = Conv(input=net, name = 'Layer9', filter_size = [3,3,192,192], bias_size = [192], padding="VALID", stride = [1,2,2,1])
with tf.name_scope('MaxPool_2'):
maxpool = tf.nn.max_pool(net, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="VALID",name='maxpool')
with tf.name_scope('Filter_concat_3'):
net = tf.concat([net_1, maxpool], 3)
return net
def inception_A(input, name):
input_shape=input.get_shape().as_list()
with tf.name_scope(name):
with tf.name_scope('Average_Pool'):
avg = tf.nn.avg_pool(input, ksize=[1, 2, 2, 1], strides=[1, 1, 1, 1], padding="SAME",name='avgpool')
net1 = Conv(input=avg, name = 'Layer1_1', filter_size = [1,1,input_shape[3],96], bias_size = [96],padding = 'SAME', stride = [1,1,1,1])
net2 = Conv(input=input, name = 'Layer2_1', filter_size = [1,1,input_shape[3],96], bias_size = [96],padding = 'SAME', stride = [1,1,1,1])
net3 = Conv(input=input, name = 'Layer3_1', filter_size = [1,1,input_shape[3],64], bias_size = [64],padding = 'SAME', stride = [1,1,1,1])
net3 = Conv(input=net3, name = 'Layer3_2', filter_size = [3,3,64,96], bias_size = [96],padding = 'SAME', stride = [1,1,1,1])
net4 = Conv(input=input, name = 'Layer4_1', filter_size = [1,1,input_shape[3],64], bias_size = [64],padding = 'SAME', stride = [1,1,1,1])
net4 = Conv(input=net4, name = 'Layer4_2', filter_size = [3,3,64,96], bias_size = [96],padding = 'SAME', stride = [1,1,1,1])
net4 = Conv(input=net4, name = 'Layer4_3', filter_size = [3,3,96,96], bias_size = [96],padding = 'SAME', stride = [1,1,1,1])
with tf.name_scope('Filter_concat'):
net = tf.concat([net1, net2, net3, net4], 3)
return net
def Reduction_A(input, n, k, I, m):
input_shape=input.get_shape().as_list()
with tf.name_scope('ReductionA'):
with tf.name_scope('MaxPool'):
maxpool = tf.nn.max_pool(input, ksize=[1, 3, 3, 1], strides=[1, 2,2, 1], padding="VALID",name='MaxPool')
net1 = Conv(input=input, name = 'Layer1', filter_size = [3,3,input_shape[3],n], bias_size = [n],padding = 'VALID', stride = [1,2,2,1])
net2 = Conv(input=input, name = 'Layer2_1', filter_size = [1,1,input_shape[3],k], bias_size = [k],padding = 'SAME', stride = [1,1,1,1])
net2 = Conv(input=net2, name = 'Layer2_2', filter_size = [3,3,k,I], bias_size = [I],padding = 'SAME', stride = [1,1,1,1])
net2 = Conv(input=net2, name = 'Layer2_3', filter_size = [3,3,I,m], bias_size = [m],padding = 'VALID', stride = [1,2,2,1])
with tf.name_scope('Filter_concat'):
net = tf.concat([maxpool ,net1, net2], 3)
return net
def inception_B(input, name):
input_shape=input.get_shape().as_list()
with tf.name_scope(name):
with tf.name_scope('Average_Pool'):
avg = tf.nn.avg_pool(input, ksize=[1, 2, 2, 1], strides=[1, 1, 1, 1], padding="SAME",name='avgpool')
net1 = Conv(input=avg, name = 'Layer1_1', filter_size = [1,1,input_shape[3],128], bias_size = [128],padding = 'SAME', stride = [1,1,1,1])
net2 = Conv(input=input, name = 'Layer2_1', filter_size = [1,1,input_shape[3],384], bias_size = [384],padding = 'SAME', stride = [1,1,1,1])
net3 = Conv(input=input, name = 'Layer3_1', filter_size = [1,1,input_shape[3],192], bias_size = [192],padding = 'SAME', stride = [1,1,1,1])
net3 = Conv(input=net3, name = 'Layer3_2', filter_size = [1,7,192,224], bias_size = [224],padding = 'SAME', stride = [1,1,1,1])
net3 = Conv(input=net3, name = 'Layer3_3', filter_size = [7,1,224,256], bias_size = [256],padding = 'SAME', stride = [1,1,1,1])
net4 = Conv(input=input, name = 'Layer4_1', filter_size = [1,1,input_shape[3],192], bias_size = [192],padding = 'SAME', stride = [1,1,1,1])
net4 = Conv(input=net4, name = 'Layer4_2', filter_size = [1,7,192,192], bias_size = [192],padding = 'SAME', stride = [1,1,1,1])
net4 = Conv(input=net4, name = 'Layer4_3', filter_size = [7,1,192,224], bias_size = [224],padding = 'SAME', stride = [1,1,1,1])
net4 = Conv(input=net4, name = 'Layer4_4', filter_size = [1,7,224,224], bias_size = [224],padding = 'SAME', stride = [1,1,1,1])
net4 = Conv(input=net4, name = 'Layer4_5', filter_size = [7,1,224,256], bias_size = [256],padding = 'SAME', stride = [1,1,1,1])
with tf.name_scope('Filter_concat'):
net = tf.concat([net1, net2, net3, net4], 3)
return net
def Reduction_B(input):
input_shape=input.get_shape().as_list()
with tf.name_scope('ReductionB'):
with tf.name_scope('MaxPool'):
maxpool = tf.nn.max_pool(input, ksize=[1, 3, 3, 1], strides=[1, 2,2, 1], padding="VALID",name='MaxPool')
net1 = Conv(input=input, name = 'Layer1_1', filter_size = [1,1,input_shape[3],192], bias_size = [192],padding = 'SAME', stride = [1,1,1,1])
net1 = Conv(input=net1, name = 'Layer1_2', filter_size = [3,3,192,192], bias_size = [192],padding = 'VALID', stride = [1,2,2,1])
net2 = Conv(input=input, name = 'Layer2_1', filter_size = [1,1,input_shape[3],256], bias_size = [256],padding = 'SAME', stride = [1,1,1,1])
net2 = Conv(input=net2, name = 'Layer2_2', filter_size = [1,7,256,256], bias_size = [256],padding = 'SAME', stride = [1,1,1,1])
net2 = Conv(input=net2, name = 'Layer2_3', filter_size = [7,1,256,320], bias_size = [320],padding = 'SAME', stride = [1,1,1,1])
net2 = Conv(input=net2, name = 'Layer2_4', filter_size = [3,3,320,320], bias_size = [320],padding = 'VALID', stride = [1,2,2,1])
with tf.name_scope('Filter_concat'):
net = tf.concat([maxpool, net1, net2], 3)
return net
def inception_C(input, name):
input_shape=input.get_shape().as_list()
with tf.name_scope(name):
with tf.name_scope('Average_Pool'):
avg = tf.nn.avg_pool(input, ksize=[1, 2, 2, 1], strides=[1, 1, 1, 1], padding="SAME",name='avgpool')
net1 = Conv(input=avg, name = 'Layer1_1', filter_size = [1,1,input_shape[3],256], bias_size = [256],padding = 'SAME', stride = [1,1,1,1])
net2 = Conv(input=input, name = 'Layer2_1', filter_size = [1,1,input_shape[3],256], bias_size = [256],padding = 'SAME', stride = [1,1,1,1])
net3 = Conv(input=input, name = 'Layer3_1', filter_size = [1,1,input_shape[3],384], bias_size = [384],padding = 'SAME', stride = [1,1,1,1])
net3_1 = Conv(input=net3, name = 'Layer3_2_1', filter_size = [1,3,384,256], bias_size = [256],padding = 'SAME', stride = [1,1,1,1])
net3_2 = Conv(input=net3, name = 'Layer3_2_2', filter_size = [3,1,384,256], bias_size = [256],padding = 'SAME', stride = [1,1,1,1])
net4 = Conv(input=input, name = 'Layer4_1', filter_size = [1,1,input_shape[3],384], bias_size = [384],padding = 'SAME', stride = [1,1,1,1])
net4 = Conv(input=net4, name = 'Layer4_2', filter_size = [1,3,384,448], bias_size = [448],padding = 'SAME', stride = [1,1,1,1])
net4 = Conv(input=net4, name = 'Layer4_3', filter_size = [3,1,448,512], bias_size = [512],padding = 'SAME', stride = [1,1,1,1])
net4_1 = Conv(input=net4, name = 'Layer4_4_1', filter_size = [3,1,512,256], bias_size = [256],padding = 'SAME', stride = [1,1,1,1])
net4_2 = Conv(input=net4, name = 'Layer4_4_2', filter_size = [1,3,512,256], bias_size = [256],padding = 'SAME', stride = [1,1,1,1])
with tf.name_scope('Filter_concat'):
net = tf.concat([net1, net2, net3_1, net3_2, net4_1, net4_2], 3)
return net
def layer(input, name, size):
input_shape=input.get_shape().as_list()
with tf.name_scope(name):
with tf.name_scope('Variable'):
W = weight_variable([input_shape[1], size], name='Weight')
bias = weight_variable([size], name='bias')
with tf.name_scope("layer"):
layer = tf.nn.relu(tf.matmul(input, W, name='layer') + bias)
return layer
def Inception_v4(input, keep_prob=0.8):
'''参考论文: Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning'''
# input_size:(299,299,3)
net = stem(input)
net = inception_A(net, 'Inception_A1')
net = inception_A(net, 'Inception_A2')
net = inception_A(net, 'Inception_A3')
net = inception_A(net, 'Inception_A4')
net = Reduction_A(net, n = 96, k = 96, I = 224, m = 256)
net = inception_B(net, 'Inception_B1')
net = inception_B(net, 'Inception_B2')
net = inception_B(net, 'Inception_B3')
net = inception_B(net, 'Inception_B4')
net = inception_B(net, 'Inception_B5')
net = inception_B(net, 'Inception_B6')
net = inception_B(net, 'Inception_B7')
net = Reduction_B(net)
net = inception_C(net, 'Inception_C1')
net = inception_C(net, 'Inception_C2')
net = inception_C(net, 'Inception_C3')
with tf.name_scope('Average_Pooling'):
net = tf.nn.avg_pool(net, ksize=[1, 8, 8, 1], strides=[1, 1, 1, 1], padding="VALID",name='avgpool')
net = tf.reshape(net,[-1,1536])
with tf.name_scope('Dropout'):
net = tf.nn.dropout(net, keep_prob)
with tf.name_scope('Softmax'):
net = layer(net, name='Layer', size = 1000)
return net
def backward(datasets, label, test_data, test_label):
with tf.name_scope('Input_data'):
X = tf.placeholder(tf.float32, [None, 299, 299, 3], name="Input")
Y_ = tf.placeholder(tf.float32, [None, 1], name='Estimation')
LEARNING_RATE_BASE = 0.00001 # 最初学习率
LEARNING_RATE_DECAY = 0.99 # 学习率的衰减率
LEARNING_RATE_STEP = 1000 # 喂入多少轮BATCH-SIZE以后，更新一次学习率。一般为总样本数量/BATCH_SIZE
gloabl_steps = tf.Variable(0, trainable=False) # 计数器，用来记录运行了几轮的BATCH_SIZE，初始为0，设置为不可训练
learning_rate = tf.train.exponential_decay(LEARNING_RATE_BASE, gloabl_steps, LEARNING_RATE_STEP,
LEARNING_RATE_DECAY, staircase=True)
keep_prob = tf.placeholder(tf.float32, name="keep_prob")
y = VGG16(X)
global_step = tf.Variable(0, trainable=False)
with tf.name_scope('Accuracy'):
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(Y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
with tf.name_scope('loss'):
loss_mse = tf.reduce_mean(-tf.reduce_sum(Y_ * tf.log(y), reduction_indices=[1]))
tf.summary.scalar('loss', loss)
with tf.name_scope('train'):
train_step = tf.train.AdamOptimizer(0.001).minimize(loss_mse)
saver = tf.train.Saver()
with tf.Session() as sess:
init_op = tf.global_variables_initializer()
writer = tf.summary.FileWriter("./logs", sess.graph)
sess.run(init_op)
# 训练模型。
STEPS = 500001
min_loss = 1
for i in range(STEPS):
start = (i * BATCH_SIZE) % len(datasets)
end = start + BATCH_SIZE
if i % 100 == 0:
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
summary_str, step, _ = sess.run([merged, gloabl_steps, train_step],
feed_dict={X: datasets[start:end], Y_: label[start:end], keep_prob:1.0},
options=run_options, run_metadata=run_metadata)
writer.add_summary(summary_str, i)
writer.add_run_metadata(run_metadata, 'step%d' % (i))
test_accuracy = accuracy.eval(feed_dict={X: test_data, Y_: test_label, keep_prob: 1.0})
print("After %d training step(s), accuracy is %g" % (i, test_accuracy))
saver.save(sess, './logs/variable', global_step=i)
else:
summary_str, step, _ = sess.run([merged, gloabl_steps, train_step],
feed_dict={X: datasets[start:end], Y_: label[start:end]})
writer.add_summary(summary_str, i)
def main():
with tf.name_scope('Input_data'):
X = tf.placeholder(tf.float32, [None, 299, 299, 3], name="Input")
keep_prob = tf.placeholder(tf.float32, name="keep_prob")
y = Inception_v4(X, keep_prob)
sess = tf.Session()
writer = tf.summary.FileWriter("./logs", sess.graph)
writer.close()
if __name__ == '__main__':
main()直接运行demo，将得到logs文件夹，运行tensorboard(注意，logs所在路径绝对不能带中文！)即可得到graph的具体图。由于图片过大无法下载，此处不再展示全部。
Steam模块的图：
InceptionA：
InceptionB：
InceptionC:
ReductionA:
ReductionB:
如果需要训练请具体修改main里面函数，运行backward函数。
