GoogLeNet 中 inception v2 (filter: 3*3) 的 tensorflow 的簡單實現(沒有使用 slim)
前言
本篇代碼與上一篇一樣，沒有使用 silm，實現 inception v2 的 3*3 的部分。
更新
後續還會更新，一下兩種(如果記得的話)。
代碼
卷積核爲 3*3
#!/usr/bin/env python
# coding: utf-8
# In[ ]:
import numpy as np
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "2"
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.system("rm -r logs")
import tensorflow as tf
get_ipython().run_line_magic('matplotlib', 'inline')
import matplotlib.pyplot as plt
from PIL import Image
# import multiprocessing
from multiprocessing import Process
import threading
import time
# In[ ]:
TrainPath = '/home/winsoul/disk/MyML/data/tfrecord/train.tfrecords'
ValPath = '/home/winsoul/disk/MyML/data/tfrecord/val.tfrecords'
# In[ ]:
def read_tfrecord(TFRecordPath):
with tf.Session() as sess:
feature = {
'image': tf.FixedLenFeature([], tf.string),
'label': tf.FixedLenFeature([], tf.int64)
}
# filename_queue = tf.train.string_input_producer([TFRecordPath], num_epochs = 1)
filename_queue = tf.train.string_input_producer([TFRecordPath])
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(serialized_example, features = feature)
image = tf.decode_raw(features['image'], tf.float32)
image = tf.reshape(image, [299, 299, 3])
label = tf.cast(features['label'], tf.int32)
return image, label
# In[ ]:
def conv_layer(X, k, s, channels_in, channels_out, is_training, name = 'CONV'):
with tf.name_scope(name):
W = tf.Variable(tf.truncated_normal([k, k, channels_in, channels_out], stddev = 0.1));
b = tf.Variable(tf.constant(0.1, shape = [channels_out]))
conv = tf.nn.conv2d(X, W, strides = [1, s, s, 1], padding = 'SAME')
conv_b = tf.nn.bias_add(conv, b)
# bn = tf.layers.batch_normalization(conv_b, training = is_training)
result = tf.nn.relu(conv_b)
tf.summary.histogram('weights', W)
tf.summary.histogram('biases', b)
tf.summary.histogram('activations', result)
return result
def pool_layer(X, k, s, strr = 'SAME', pool_type = 'MAX'):
if pool_type == 'MAX':
result = tf.nn.max_pool(X,
ksize = [1, k, k, 1],
strides = [1, s, s, 1],
padding = strr)
else:
result = tf.nn.avg_pool(X,
ksize = [1, k, k, 1],
strides = [1, s, s, 1],
padding = strr)
return result
def fc_layer(X, neurons_in, neurons_out, last = False, name = 'FC'):
with tf.name_scope(name):
W = tf.Variable(tf.truncated_normal([neurons_in, neurons_out], stddev = 0.1))
b = tf.Variable(tf.constant(0.1, shape = [neurons_out]))
tf.summary.histogram('weights', W)
tf.summary.histogram('biases', b)
if last == False:
result = tf.nn.relu(tf.matmul(X, W) + b)
else:
result = tf.matmul(X, W) + b
tf.summary.histogram('activations', result)
return result
# In[ ]:
def inception(X, channels_in, core_channels_out, is_training, name = 'inception'):
with tf.name_scope(name + '_1'):
conv1_1 = conv_layer(X, 1, 1, channels_in, core_channels_out, is_training, name = name + '_1-conv1_1_')
with tf.name_scope(name + '_2'):
conv2_1 = conv_layer(X, 1, 1, channels_in, core_channels_out, is_training, name = name + '_2-conv2_1')
conv2_2 = conv_layer(conv2_1, 3, 1, core_channels_out, core_channels_out, is_training, name = name + '_2-conv2_2')
with tf.name_scope(name + '_3'):
conv3_1 = conv_layer(X, 1, 1, channels_in, core_channels_out, is_training, name = name + '_3-conv3_1')
conv3_2 = conv_layer(conv3_1, 3, 1, core_channels_out, core_channels_out, is_training, name = name + '_3-conv3_2')
conv3_3 = conv_layer(conv3_2, 3, 1, core_channels_out, core_channels_out, is_training, name = name + '_3-conv3_3')
with tf.name_scope(name + '_2'):
pool4_1 = pool_layer(X, 3, 1, strr = 'SAME', pool_type = 'MAX')
conv4_2 = conv_layer(pool4_1, 1, 1, channels_in, core_channels_out, is_training, name = name + '_4-conv4_3')
result = tf.concat([conv1_1, conv2_2, conv3_3, conv4_2], 3)
return result
# In[ ]:
def Network(BatchSize, learning_rate):
tf.reset_default_graph()
with tf.Session() as sess:
is_training = tf.placeholder(dtype = tf.bool, shape=())
keep_prob = tf.placeholder('float32', name = 'keep_prob')
judge = tf.Print(is_training, ['is_training:', is_training])
image_train, label_train = read_tfrecord(TrainPath)
image_val, label_val = read_tfrecord(ValPath)
image_train_Batch, label_train_Batch = tf.train.shuffle_batch([image_train, label_train],
batch_size = BatchSize,
capacity = BatchSize*3 + 200,
min_after_dequeue = BatchSize)
image_val_Batch, label_val_Batch = tf.train.shuffle_batch([image_val, label_val],
batch_size = BatchSize,
capacity = BatchSize*3 + 200,
min_after_dequeue = BatchSize)
image_Batch = tf.cond(is_training, lambda: image_train_Batch, lambda: image_val_Batch)
label_Batch = tf.cond(is_training, lambda: label_train_Batch, lambda: label_val_Batch)
label_Batch = tf.one_hot(label_Batch, depth = 5)
X = tf.identity(image_Batch)
y = tf.identity(label_Batch)
with tf.name_scope('input_reshape'):
tf.summary.image('input', X, 32)
# conv1 = conv_layer(X, 7, 2, 3, 24, is_training, "conv1")
conv1_1 = conv_layer(X, 3, 2, 3, 8, is_training, "conv1_1")
conv1_2 = conv_layer(conv1_1, 3, 2, 8, 16, is_training, "conv1_2")
conv1_3 = conv_layer(conv1_2, 3, 2, 16, 24, is_training, "conv1_3")
max_pool1 = pool_layer(conv1_3, 3, 2)
#bn1
conv2 = conv_layer(max_pool1, 1, 1, 24, 16, is_training, "conv2")
conv3 = conv_layer(conv2, 3, 3, 16, 24, is_training, "conv3")
max_pool2 = pool_layer(conv3, 3, 2)
net1 = inception(max_pool2, 24, 16, is_training)
net2 = inception(net1, 4 * 16, 20, is_training)
max_pool3 = pool_layer(net2, 3, 2)
print(max_pool3.shape)
net3 = inception(max_pool3, 4 * 20, 24, is_training)
net4 = inception(net3, 4 * 24, 32, is_training)
net5 = inception(net4, 4 * 32, 38, is_training)
net6 = inception(net5, 4 * 38, 42, is_training)
net7 = inception(net6, 4 * 42, 56, is_training)
max_pool4 = pool_layer(net7, 3, 2)
print(max_pool4.shape)
net8 = inception(max_pool4, 4 * 56, 42, is_training)
net9 = inception(net8, 4 * 42, 38, is_training)
mean_pool1 = pool_layer(net9, 7, 1, pool_type = 'MEAN')
print(mean_pool1.shape)
drop1 = tf.nn.dropout(mean_pool1, keep_prob)
fc1 = fc_layer(tf.reshape(drop1, [-1, 1 * 1 * 152]), 1 * 1 * 152, 64)
drop2 = tf.nn.dropout(fc1, keep_prob)
y_result = fc_layer(drop2, 64, 5, True)
with tf.name_scope('summaries'):
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = y_result, labels = y))
train_step = tf.train.AdamOptimizer(learning_rate).minimize(cross_entropy)
#train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)
corrent_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_result, 1))
accuracy = tf.reduce_mean(tf.cast(corrent_prediction, 'float', name = 'accuracy'))
tf.summary.scalar("loss", cross_entropy)
tf.summary.scalar("accuracy", accuracy)
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
sess.run(init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord = coord)
merge_summary = tf.summary.merge_all()
summary__train_writer = tf.summary.FileWriter("./logs/train" + '_rate:' + str(learning_rate), sess.graph)
summary_val_writer = tf.summary.FileWriter("./logs/test" + '_rate:' + str(learning_rate))
try:
batch_index = 0
while not coord.should_stop():
sess.run([train_step], feed_dict = {keep_prob: 0.5, is_training: True})
if batch_index % 10 == 0:
summary_train, _, acc_train, loss_train = sess.run([merge_summary, train_step, accuracy, cross_entropy], feed_dict = {keep_prob: 1.0, is_training: True})
summary__train_writer.add_summary(summary_train, batch_index)
print(str(batch_index) + ' train:' + ' ' + str(acc_train) + ' ' + str(loss_train))
summary_val, acc_val, loss_val = sess.run([merge_summary, accuracy, cross_entropy], feed_dict = {keep_prob: 1.0, is_training: False})
summary_val_writer.add_summary(summary_val, batch_index)
print(str(batch_index) + ' val: ' + ' ' + str(acc_val) + ' ' + str(loss_val))
batch_index += 1;
# if batch_index > 1500:
# break
except tf.errors.OutOfRangeError:
print("OutofRangeError!")
finally:
print("Finish")
coord.request_stop()
coord.join(threads)
sess.close()
# In[ ]:
def main():
rate = 0.00001
while True:
try:
Network(64, rate)
except KeyboardInterrupt:
pass
# for rate in (0.00007, 0.00003):
# try:
# print("-----------------------------------------------")
# print(str(rate) + ':')
# Network(64, rate)
# except KeyboardInterrupt:
# pass
# In[ ]:
if __name__ == '__main__':
main()
代碼
卷積核爲 1*n
#!/usr/bin/env python
# coding: utf-8
# In[ ]:
import numpy as np
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "2"
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.system("rm -r logs")
import tensorflow as tf
get_ipython().run_line_magic('matplotlib', 'inline')
import matplotlib.pyplot as plt
from PIL import Image
# import multiprocessing
from multiprocessing import Process
import threading
import time
# In[ ]:
TrainPath = '/home/winsoul/disk/MyML/data/tfrecord/train.tfrecords'
ValPath = '/home/winsoul/disk/MyML/data/tfrecord/val.tfrecords'
# In[ ]:
def read_tfrecord(TFRecordPath):
with tf.Session() as sess:
feature = {
'image': tf.FixedLenFeature([], tf.string),
'label': tf.FixedLenFeature([], tf.int64)
}
# filename_queue = tf.train.string_input_producer([TFRecordPath], num_epochs = 1)
filename_queue = tf.train.string_input_producer([TFRecordPath])
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(serialized_example, features = feature)
image = tf.decode_raw(features['image'], tf.float32)
image = tf.reshape(image, [299, 299, 3])
label = tf.cast(features['label'], tf.int32)
return image, label
# In[ ]:
def conv_layer(X, k, s, channels_in, channels_out, is_training, name = 'CONV', use_bn = False):
with tf.name_scope(name):
W = tf.Variable(tf.truncated_normal([k[0], k[1], channels_in, channels_out], stddev = 0.1));
b = tf.Variable(tf.constant(0.1, shape = [channels_out]))
conv = tf.nn.conv2d(X, W, strides = [1, s, s, 1], padding = 'SAME')
conv_b = tf.nn.bias_add(conv, b)
bn = tf.layers.batch_normalization(conv_b, training = is_training)
if use_bn == True:
result = tf.nn.relu(bn)
else:
result = tf.nn.relu(conv_b)
tf.summary.histogram('weights', W)
tf.summary.histogram('biases', b)
tf.summary.histogram('activations', result)
return result
def pool_layer(X, k, s, strr = 'SAME', pool_type = 'MAX'):
if pool_type == 'MAX':
result = tf.nn.max_pool(X,
ksize = [1, k, k, 1],
strides = [1, s, s, 1],
padding = strr)
else:
result = tf.nn.avg_pool(X,
ksize = [1, k, k, 1],
strides = [1, s, s, 1],
padding = strr)
return result
def fc_layer(X, neurons_in, neurons_out, last = False, name = 'FC'):
with tf.name_scope(name):
W = tf.Variable(tf.truncated_normal([neurons_in, neurons_out], stddev = 0.1))
b = tf.Variable(tf.constant(0.1, shape = [neurons_out]))
tf.summary.histogram('weights', W)
tf.summary.histogram('biases', b)
if last == False:
result = tf.nn.relu(tf.matmul(X, W) + b)
else:
result = tf.matmul(X, W) + b
tf.summary.histogram('activations', result)
return result
# In[ ]:
def inception(X, channels_in, core_channels_out, is_training, name = 'inception'):
with tf.name_scope(name + '_1'):
conv1_1 = conv_layer(X, [1, 1], 1, channels_in, core_channels_out, is_training, name = name + '_1-conv1_1_')
with tf.name_scope(name + '_2'):
conv2_1 = conv_layer(X, [1, 1], 1, channels_in, core_channels_out, is_training, name = name + '_2-conv2_1')
conv2_2 = conv_layer(conv2_1, [1, 3], 1, core_channels_out, core_channels_out, is_training, name = name + '_2-conv2_2')
conv2_3 = conv_layer(conv2_2, [3, 1], 1, core_channels_out, core_channels_out, is_training, name = name + '_2-conv2_3')
with tf.name_scope(name + '_3'):
conv3_1 = conv_layer(X, [1, 1], 1, channels_in, core_channels_out, is_training, name = name + '_3-conv3_1')
conv3_2 = conv_layer(conv3_1, [1, 3], 1, core_channels_out, core_channels_out, is_training, name = name + '_3-conv3_2')
conv3_3 = conv_layer(conv3_2, [3, 1], 1, core_channels_out, core_channels_out, is_training, name = name + '_3-conv3_3')
conv3_4 = conv_layer(conv3_3, [1, 3], 1, core_channels_out, core_channels_out, is_training, name = name + '_3-conv3_4')
conv3_5 = conv_layer(conv3_4, [3, 1], 1, core_channels_out, core_channels_out, is_training, name = name + '_3-conv3_5')
with tf.name_scope(name + '_2'):
pool4_1 = pool_layer(X, 3, 1, strr = 'SAME', pool_type = 'MAX')
conv4_2 = conv_layer(pool4_1, [1, 1], 1, channels_in, core_channels_out, is_training, name = name + '_4-conv4_3')
result = tf.concat([conv1_1, conv2_3, conv3_5, conv4_2], 3)
return result
# In[ ]:
def Network(BatchSize, learning_rate):
tf.reset_default_graph()
with tf.Session() as sess:
is_training = tf.placeholder(dtype = tf.bool, shape=())
keep_prob = tf.placeholder('float32', name = 'keep_prob')
judge = tf.Print(is_training, ['is_training:', is_training])
image_train, label_train = read_tfrecord(TrainPath)
image_val, label_val = read_tfrecord(ValPath)
image_train_Batch, label_train_Batch = tf.train.shuffle_batch([image_train, label_train],
batch_size = BatchSize,
capacity = BatchSize*3 + 200,
min_after_dequeue = BatchSize)
image_val_Batch, label_val_Batch = tf.train.shuffle_batch([image_val, label_val],
batch_size = BatchSize,
capacity = BatchSize*3 + 200,
min_after_dequeue = BatchSize)
image_Batch = tf.cond(is_training, lambda: image_train_Batch, lambda: image_val_Batch)
label_Batch = tf.cond(is_training, lambda: label_train_Batch, lambda: label_val_Batch)
label_Batch = tf.one_hot(label_Batch, depth = 5)
X = tf.identity(image_Batch)
y = tf.identity(label_Batch)
with tf.name_scope('input_reshape'):
tf.summary.image('input', X, 32)
# conv1 = conv_layer(X, 7, 2, 3, 24, is_training, "conv1")
conv1 = conv_layer(X, [7, 7], 2, 3, 24, is_training, "conv1", use_bn = False)
max_pool1 = pool_layer(conv1, 3, 2)
#bn1
conv2 = conv_layer(max_pool1, [1, 1], 1, 24, 16, is_training, "conv2", use_bn = False)
conv3 = conv_layer(conv2, [3, 3], 3, 16, 24, is_training, "conv3", use_bn = False)
max_pool2 = pool_layer(conv3, 3, 2)
net1 = inception(max_pool2, 24, 16, is_training)
net2 = inception(net1, 4 * 16, 20, is_training)
max_pool3 = pool_layer(net2, 3, 2)
print(max_pool3.shape)
net3 = inception(max_pool3, 4 * 20, 24, is_training)
net4 = inception(net3, 4 * 24, 32, is_training)
net5 = inception(net4, 4 * 32, 38, is_training)
net6 = inception(net5, 4 * 38, 42, is_training)
net7 = inception(net6, 4 * 42, 56, is_training)
max_pool4 = pool_layer(net7, 3, 2)
print(max_pool4.shape)
net8 = inception(max_pool4, 4 * 56, 42, is_training)
net9 = inception(net8, 4 * 42, 38, is_training)
mean_pool1 = pool_layer(net9, 7, 1, pool_type = 'MEAN')
print(mean_pool1.shape)
drop1 = tf.nn.dropout(mean_pool1, keep_prob)
fc1 = fc_layer(tf.reshape(drop1, [-1, 4 * 4 * 152]), 4 * 4 * 152, 256)
drop2 = tf.nn.dropout(fc1, keep_prob)
y_result = fc_layer(drop2, 256, 5, True)
with tf.name_scope('summaries'):
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = y_result, labels = y))
train_step = tf.train.AdamOptimizer(learning_rate).minimize(cross_entropy)
#train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)
corrent_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_result, 1))
accuracy = tf.reduce_mean(tf.cast(corrent_prediction, 'float', name = 'accuracy'))
tf.summary.scalar("loss", cross_entropy)
tf.summary.scalar("accuracy", accuracy)
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
sess.run(init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord = coord)
merge_summary = tf.summary.merge_all()
summary__train_writer = tf.summary.FileWriter("./logs/train" + '_rate:' + str(learning_rate), sess.graph)
summary_val_writer = tf.summary.FileWriter("./logs/test" + '_rate:' + str(learning_rate))
try:
batch_index = 0
while not coord.should_stop():
sess.run([train_step], feed_dict = {keep_prob: 0.5, is_training: True})
if batch_index % 10 == 0:
summary_train, _, acc_train, loss_train = sess.run([merge_summary, train_step, accuracy, cross_entropy], feed_dict = {keep_prob: 1.0, is_training: True})
summary__train_writer.add_summary(summary_train, batch_index)
print(str(batch_index) + ' train:' + ' ' + str(acc_train) + ' ' + str(loss_train))
summary_val, acc_val, loss_val = sess.run([merge_summary, accuracy, cross_entropy], feed_dict = {keep_prob: 1.0, is_training: False})
summary_val_writer.add_summary(summary_val, batch_index)
print(str(batch_index) + ' val: ' + ' ' + str(acc_val) + ' ' + str(loss_val))
batch_index += 1;
# if batch_index > 1500:
# break
except tf.errors.OutOfRangeError:
print("OutofRangeError!")
finally:
print("Finish")
coord.request_stop()
coord.join(threads)
sess.close()
# In[ ]:
def main():
rate = 0.00001
while True:
try:
Network(64, rate)
except KeyboardInterrupt:
pass
# for rate in (0.00007, 0.00003):
# try:
# print("-----------------------------------------------")
# print(str(rate) + ':')
# Network(64, rate)
# except KeyboardInterrupt:
# pass
# In[ ]:
if __name__ == '__main__':
main()
