tensorflow2 keras 调用官方提供的模型训练分类与测试
目录
本任务为分类分类属性的8个类别。
dict_gender = {'f':0,
'm':1
}
dict_age = {'children':0,
'young':1,
'adult':2,
'older':3
}
用的keras的datagen_train.flow_from_directory。
train_generator=datagen_train.flow_from_directory('/home/nfs/em1/train_data/age_gender/train-dir',#类别子文件夹的上一级文件夹
batch_size=BATCH_SIZE,
shuffle=True,
target_size=[SIZE, SIZE],
class_mode='categorical'
)
文件夹train-dir下放8个文件夹，分别表示8类。自动制作标签。
print(train_generator.class_indices)
print(valid_generator.class_indices)
可以显示类别
resnet50
import os,sys
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from PIL import Image
import random
from tensorflow.keras.callbacks import ModelCheckpoint
print(tf.__version__)
print(sys.version_info)
HEIGHT = 224
WIDTH = 224
BATCH_SIZE = 64
SIZE = HEIGHT
NUM_TRAIN = 342155
NUM_VAL = 1981
model = ResNet50(
weights=None, # weights='imagenet' 这里也可以预训练自己的模型 写模型路径即可
classes=8
)
model.compile(optimizer=tf.train.AdamOptimizer(0.001),
loss='categorical_crossentropy',
metrics=['accuracy'])
print(model.summary())
#model.load_weights( "my_net.hdf5" ) #加载预训练模型继续训练
datagen_train = ImageDataGenerator(
rescale=1./255.0,
rotation_range=1.5,
width_shift_range=0.05,
height_shift_range=0.05,
shear_range=0.1,
zoom_range=0.1,
horizontal_flip=True,
fill_mode='nearest')
datagen_val = ImageDataGenerator(
rescale=1./255.0)
train_generator=datagen_train.flow_from_directory('/home/train',#类别子文件夹的上一级文件夹
batch_size=BATCH_SIZE,
shuffle=True,
target_size=[SIZE, SIZE],
class_mode='categorical'
)
valid_generator=datagen_val.flow_from_directory('/home/test',#类别子文件夹的上一级文件夹
batch_size=BATCH_SIZE,
shuffle=False,
target_size=[SIZE, SIZE],
class_mode='categorical'
)
print(train_generator.class_indices)
print(valid_generator.class_indices)
epochs = 10000
filepath = "./model/resnet50-keras_model_{epoch:03d}-{val_acc:.4f}.h5" #避免文件名称重复
checkpoint = ModelCheckpoint(filepath=filepath, monitor='val_acc', verbose=1,
save_best_only=False, mode='max')
history = model.fit_generator(generator = train_generator,
steps_per_epoch=NUM_TRAIN // BATCH_SIZE,
epochs=epochs,
validation_data=valid_generator,
validation_steps=NUM_VAL // BATCH_SIZE,
verbose=1,callbacks=[checkpoint])
resnet50测试代码
import os,sys
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from PIL import Image
import random
from tensorflow.keras.callbacks import ModelCheckpoint
print(tf.__version__)
print(sys.version_info)
HEIGHT = 224
WIDTH = 224
BATCH_SIZE = 64
SIZE = HEIGHT
NUM_TRAIN = 342155
NUM_VAL = 1981
model = ResNet50(
weights=None,
classes=8
)
model.compile(optimizer=tf.train.AdamOptimizer(0.001),
loss='categorical_crossentropy',
metrics=['accuracy'])
print(model.summary())
model.load_weights("/resnet50/fuwuqi/model/resnet50-keras_model_017-0.8536.h5")
dict_label_tijiao = {"f_children":"0",
"f_young":"1",
"f_adult":"2",
"f_older":"3",
"m_children":"4",
"m_young":"5",
"m_adult":"6",
"m_older":"7"
}
own_label = {'f_adult': 0, 'f_children': 1, 'f_older': 2, 'f_young': 3, 'm_adult': 4, 'm_children': 5, 'm_older': 6, 'm_young': 7}
map_own_label2tijiao = {0:'f_adult',1:'f_children',2:'f_older',3:'f_young',4:'m_adult',5:'m_children',6:'m_older',7:'m_young'}
root_dir_test = "/data_2/big-data/compete/20200323/src_data/test-tijiao/"
with open(root_dir_test + 'result.txt','w')as fw:
for root, dirs, files in os.walk(root_dir_test):
if 0 == len(files):
continue
for img_name_ in files:
print(img_name_)
if img_name_.endswith(".jpg") or img_name_.endswith(".jpeg") or img_name_.endswith(".png"):
pos = img_name_.find(".")
name = img_name_[0:pos]
img_path = os.path.join(root,img_name_)
img = image.load_img(img_path, target_size=(SIZE, SIZE))
img = image.img_to_array(img) / 255.0
img = np.expand_dims(img, axis=0) # 为batch添加第四维
predictions = model.predict(img)
label = np.argmax(predictions, axis=1)
print(predictions)
print("label=", label)
print('*' * 100)
# cv2.imshow("img", img[0][:, :, ::-1])
# cv2.waitKey(0)
InceptionResNetV2
import os
import sys
import tensorflow as tf
import time
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Model
from tensorflow.keras.layers import GlobalAveragePooling2D, GlobalMaxPooling2D, Reshape, Dense, multiply, Permute, Concatenate, Conv2D, Add, Activation, Lambda
from tensorflow.keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.applications.inception_v3 import InceptionV3, preprocess_input
from tensorflow.keras.optimizers import SGD
print(tf.__version__)
print(sys.version_info)
HEIGHT = 160
WIDTH = 160
BATCH_SIZE = 20
SIZE = HEIGHT
NUM_TRAIN = 342155
NUM_VAL = 1981
nb_classes = 8
model = tf.keras.applications.InceptionResNetV2(weights=None,classes=nb_classes,input_shape=(HEIGHT, WIDTH, 3))
model.compile(
optimizer=tf.keras.optimizers.RMSprop(),
loss='categorical_crossentropy',
metrics=['accuracy'])
model.summary()
datagen_train = ImageDataGenerator(
rescale=1./255.0,
rotation_range=1.5,
width_shift_range=0.05,
height_shift_range=0.05,
shear_range=0.1,
zoom_range=0.1,
horizontal_flip=True,
fill_mode='nearest')
datagen_val = ImageDataGenerator(
rescale=1./255.0)
train_generator=datagen_train.flow_from_directory('/home/train_data/age_gender/my_aug_chengguang/train',#类别子文件夹的上一级文件夹
batch_size=BATCH_SIZE,
shuffle=True,
target_size=[SIZE, SIZE],
class_mode='categorical'
)
valid_generator=datagen_val.flow_from_directory('/home/train_data/age_gender/my_aug_chengguang/test',#类别子文件夹的上一级文件夹
batch_size=BATCH_SIZE,
shuffle=False,
target_size=[SIZE, SIZE],
class_mode='categorical'
)
print(train_generator.class_indices)
print(valid_generator.class_indices)
epochs = 10000
filepath = "./model/inception-resnet-model_{epoch:03d}-{val_acc:.4f}.h5" #避免文件名称重复
checkpoint = ModelCheckpoint(filepath=filepath, monitor='val_acc', verbose=1,
save_best_only=False, mode='max')
history = model.fit_generator(generator = train_generator,
steps_per_epoch=NUM_TRAIN // BATCH_SIZE,
epochs=epochs,
validation_data=valid_generator,
validation_steps=NUM_VAL // BATCH_SIZE,
verbose=1,callbacks=[checkpoint])
测试代码
import os
import sys
import tensorflow as tf
import time
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.preprocessing import image
from tensorflow.keras.models import Model
from tensorflow.keras.layers import GlobalAveragePooling2D, GlobalMaxPooling2D, Reshape, Dense, multiply, Permute, Concatenate, Conv2D, Add, Activation, Lambda
from tensorflow.keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.applications.inception_v3 import InceptionV3, preprocess_input
from tensorflow.keras.optimizers import SGD
from get_acc_fun import *
import numpy as np
print(tf.__version__)
print(sys.version_info)
HEIGHT = 160
WIDTH = 160
BATCH_SIZE = 500
SIZE = HEIGHT
NUM_TRAIN = 342155
NUM_VAL = 1981
nb_classes = 8
#model = tf.keras.applications.InceptionResNetV2(weights='imagenet',include_top=False,classes=nb_classes,input_shape=(HEIGHT, WIDTH, 3))
model = tf.keras.applications.InceptionResNetV2(weights=None,classes=nb_classes,input_shape=(HEIGHT, WIDTH, 3))
model.compile(
optimizer=tf.keras.optimizers.RMSprop(),
loss='categorical_crossentropy',
metrics=['accuracy'])
model.summary()
model.load_weights("/data_1/Yang/project_new/2020/tf_study/tf_xception/Inception-ResNetV2/model/inception-resnet-model_020-0.8573.h5")
dict_label_tijiao = {"f_children":"0",
"f_young":"1",
"f_adult":"2",
"f_older":"3",
"m_children":"4",
"m_young":"5",
"m_adult":"6",
"m_older":"7"
}
own_label = {'f_adult': 0, 'f_children': 1, 'f_older': 2, 'f_young': 3, 'm_adult': 4, 'm_children': 5, 'm_older': 6, 'm_young': 7}
map_own_label2tijiao = {0:'f_adult',1:'f_children',2:'f_older',3:'f_young',4:'m_adult',5:'m_children',6:'m_older',7:'m_young'}
root_dir_test = "/data_2/big-data/compete/20200323/src_data/test-tijiao/"
with open(root_dir_test + 'result.txt','w')as fw:
for root, dirs, files in os.walk(root_dir_test):
if 0 == len(files):
continue
for img_name_ in files:
print(img_name_)
if img_name_.endswith(".jpg") or img_name_.endswith(".jpeg") or img_name_.endswith(".png"):
pos = img_name_.find(".")
name = img_name_[0:pos]
img_path = os.path.join(root,img_name_)
img = image.load_img(img_path, target_size=(SIZE, SIZE))
img = image.img_to_array(img) / 255.0
img = np.expand_dims(img, axis=0) # 为batch添加第四维
predictions = model.predict(img)
label = np.argmax(predictions, axis=1)
label_describe = map_own_label2tijiao[label[0]]
label_tijiao_val = dict_label_tijiao[label_describe]
content = name + " " + label_tijiao_val
fw.write(content + '\n')
# print(predictions)
# print("label=", label)
# print('*' * 100)
# cv2.imshow("img", img[0][:, :, ::-1])
# cv2.waitKey(0)
efficientnet 自定义
efficientnet.py
import tensorflow as tf
import math
NUM_CLASSES = 8
def round_filters(filters, multiplier):
depth_divisor = 8
min_depth = None
min_depth = min_depth or depth_divisor
filters = filters * multiplier
new_filters = max(min_depth, int(filters + depth_divisor / 2) // depth_divisor * depth_divisor)
if new_filters < 0.9 * filters:
new_filters += depth_divisor
return int(new_filters)
def round_repeats(repeats, multiplier):
if not multiplier:
return repeats
return int(math.ceil(multiplier * repeats))
class SEBlock(tf.keras.layers.Layer):
def __init__(self, input_channels, ratio=0.25):
super(SEBlock, self).__init__()
self.num_reduced_filters = max(1, int(input_channels * ratio))
self.pool = tf.keras.layers.GlobalAveragePooling2D()
self.reduce_conv = tf.keras.layers.Conv2D(filters=self.num_reduced_filters,
kernel_size=(1, 1),
strides=1,
padding="same")
self.expand_conv = tf.keras.layers.Conv2D(filters=input_channels,
kernel_size=(1, 1),
strides=1,
padding="same")
def call(self, inputs, **kwargs):
branch = self.pool(inputs)
branch = tf.expand_dims(input=branch, axis=1)
branch = tf.expand_dims(input=branch, axis=1)
branch = self.reduce_conv(branch)
branch = tf.nn.swish(branch)
branch = self.expand_conv(branch)
branch = tf.nn.sigmoid(branch)
output = inputs * branch
return output
class MBConv(tf.keras.layers.Layer):
def __init__(self, in_channels, out_channels, expansion_factor, stride, k, drop_connect_rate):
super(MBConv, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.stride = stride
self.drop_connect_rate = drop_connect_rate
self.conv1 = tf.keras.layers.Conv2D(filters=in_channels * expansion_factor,
kernel_size=(1, 1),
strides=1,
padding="same",
use_bias=False)
self.bn1 = tf.keras.layers.BatchNormalization()
self.dwconv = tf.keras.layers.DepthwiseConv2D(kernel_size=(k, k),
strides=stride,
padding="same",
use_bias=False)
self.bn2 = tf.keras.layers.BatchNormalization()
self.se = SEBlock(input_channels=in_channels * expansion_factor)
self.conv2 = tf.keras.layers.Conv2D(filters=out_channels,
kernel_size=(1, 1),
strides=1,
padding="same",
use_bias=False)
self.bn3 = tf.keras.layers.BatchNormalization()
self.dropout = tf.keras.layers.Dropout(rate=drop_connect_rate)
def call(self, inputs, training=None, **kwargs):
x = self.conv1(inputs)
x = self.bn1(x, training=training)
x = tf.nn.swish(x)
x = self.dwconv(x)
x = self.bn2(x, training=training)
x = self.se(x)
x = tf.nn.swish(x)
x = self.conv2(x)
x = self.bn3(x, training=training)
if self.stride == 1 and self.in_channels == self.out_channels:
if self.drop_connect_rate:
x = self.dropout(x, training=training)
x = tf.keras.layers.add([x, inputs])
return x
def build_mbconv_block(in_channels, out_channels, layers, stride, expansion_factor, k, drop_connect_rate):
block = tf.keras.Sequential()
for i in range(layers):
if i == 0:
block.add(MBConv(in_channels=in_channels,
out_channels=out_channels,
expansion_factor=expansion_factor,
stride=stride,
k=k,
drop_connect_rate=drop_connect_rate))
else:
block.add(MBConv(in_channels=out_channels,
out_channels=out_channels,
expansion_factor=expansion_factor,
stride=1,
k=k,
drop_connect_rate=drop_connect_rate))
return block
class EfficientNet(tf.keras.Model):
def __init__(self, width_coefficient, depth_coefficient, dropout_rate, drop_connect_rate=0.2):
super(EfficientNet, self).__init__()
self.conv1 = tf.keras.layers.Conv2D(filters=round_filters(32, width_coefficient),
kernel_size=(3, 3),
strides=2,
padding="same",
use_bias=False)
self.bn1 = tf.keras.layers.BatchNormalization()
self.block1 = build_mbconv_block(in_channels=round_filters(32, width_coefficient),
out_channels=round_filters(16, width_coefficient),
layers=round_repeats(1, depth_coefficient),
stride=1,
expansion_factor=1, k=3, drop_connect_rate=drop_connect_rate)
self.block2 = build_mbconv_block(in_channels=round_filters(16, width_coefficient),
out_channels=round_filters(24, width_coefficient),
layers=round_repeats(2, depth_coefficient),
stride=2,
expansion_factor=6, k=3, drop_connect_rate=drop_connect_rate)
self.block3 = build_mbconv_block(in_channels=round_filters(24, width_coefficient),
out_channels=round_filters(40, width_coefficient),
layers=round_repeats(2, depth_coefficient),
stride=2,
expansion_factor=6, k=5, drop_connect_rate=drop_connect_rate)
self.block4 = build_mbconv_block(in_channels=round_filters(40, width_coefficient),
out_channels=round_filters(80, width_coefficient),
layers=round_repeats(3, depth_coefficient),
stride=2,
expansion_factor=6, k=3, drop_connect_rate=drop_connect_rate)
self.block5 = build_mbconv_block(in_channels=round_filters(80, width_coefficient),
out_channels=round_filters(112, width_coefficient),
layers=round_repeats(3, depth_coefficient),
stride=1,
expansion_factor=6, k=5, drop_connect_rate=drop_connect_rate)
self.block6 = build_mbconv_block(in_channels=round_filters(112, width_coefficient),
out_channels=round_filters(192, width_coefficient),
layers=round_repeats(4, depth_coefficient),
stride=2,
expansion_factor=6, k=5, drop_connect_rate=drop_connect_rate)
self.block7 = build_mbconv_block(in_channels=round_filters(192, width_coefficient),
out_channels=round_filters(320, width_coefficient),
layers=round_repeats(1, depth_coefficient),
stride=1,
expansion_factor=6, k=3, drop_connect_rate=drop_connect_rate)
self.conv2 = tf.keras.layers.Conv2D(filters=round_filters(1280, width_coefficient),
kernel_size=(1, 1),
strides=1,
padding="same",
use_bias=False)
self.bn2 = tf.keras.layers.BatchNormalization()
self.pool = tf.keras.layers.GlobalAveragePooling2D()
self.dropout = tf.keras.layers.Dropout(rate=dropout_rate)
self.fc = tf.keras.layers.Dense(units=NUM_CLASSES,
activation=tf.keras.activations.softmax)
def call(self, inputs, training=None, mask=None):
x = self.conv1(inputs)
x = self.bn1(x, training=training)
x = tf.nn.swish(x)
x = self.block1(x)
x = self.block2(x)
x = self.block3(x)
x = self.block4(x)
x = self.block5(x)
x = self.block6(x)
x = self.block7(x)
x = self.conv2(x)
x = self.bn2(x, training=training)
x = tf.nn.swish(x)
x = self.pool(x)
x = self.dropout(x, training=training)
x = self.fc(x)
return x
def get_efficient_net(width_coefficient, depth_coefficient, resolution, dropout_rate):
net = EfficientNet(width_coefficient=width_coefficient,
depth_coefficient=depth_coefficient,
dropout_rate=dropout_rate)
return net
def efficient_net_b0():
return get_efficient_net(1.0, 1.0, 224, 0.2)
def efficient_net_b1():
return get_efficient_net(1.0, 1.1, 240, 0.2)
def efficient_net_b2():
return get_efficient_net(1.1, 1.2, 260, 0.3)
def efficient_net_b3():
return get_efficient_net(1.2, 1.4, 300, 0.3)
def efficient_net_b4():
return get_efficient_net(1.4, 1.8, 380, 0.4)
def efficient_net_b5():
return get_efficient_net(1.6, 2.2, 456, 0.4)
def efficient_net_b6():
return get_efficient_net(1.8, 2.6, 528, 0.5)
def efficient_net_b7():
return get_efficient_net(2.0, 3.1, 600, 0.5)
efficientnet_train.py
import os,sys
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from PIL import Image
import random
from tensorflow.keras.callbacks import ModelCheckpoint
import efficientnet
print(tf.__version__)
print(sys.version_info)
HEIGHT = 224
WIDTH = 224
BATCH_SIZE = 20
SIZE = HEIGHT
NUM_TRAIN = 17786
NUM_VAL = 1981
CHANNELS = 3
IMAGE_HEIGHT = HEIGHT
IMAGE_WIDTH = WIDTH
model = efficientnet.efficient_net_b0()
model.build(input_shape=(None, IMAGE_HEIGHT, IMAGE_WIDTH, CHANNELS))
model.summary()
optimizer_rmsp = tf.keras.optimizers.RMSprop()
model.compile(optimizer=optimizer_rmsp,
loss='categorical_crossentropy',
metrics=['accuracy'])
datagen_train = ImageDataGenerator(
rescale=1./255.0,
rotation_range=1.5,
width_shift_range=0.05,
height_shift_range=0.05,
shear_range=0.1,
zoom_range=0.1,
horizontal_flip=True,
fill_mode='nearest')
datagen_val = ImageDataGenerator(
rescale=1./255.0)
train_generator=datagen_train.flow_from_directory('/data_2/big-data/compete/20200323/src_data/age_gender1/train',#类别子文件夹的上一级文件夹
batch_size=BATCH_SIZE,
shuffle=True,
target_size=[SIZE, SIZE],
class_mode='categorical'
)
valid_generator=datagen_val.flow_from_directory('/data_2/big-data/compete/20200323/src_data/age_gender1/test',#类别子文件夹的上一级文件夹
batch_size=BATCH_SIZE,
shuffle=False,
target_size=[SIZE, SIZE],
class_mode='categorical'
)
print(train_generator.class_indices)
print(valid_generator.class_indices)
epochs = 10000
filepath = "./model/efficient-keras_model_{epoch:03d}-{val_acc:.4f}.h5" #避免文件名称重复
checkpoint = ModelCheckpoint(filepath=filepath, monitor='val_acc', verbose=1,
save_best_only=False, mode='max')
history = model.fit_generator(generator = train_generator,
steps_per_epoch=NUM_TRAIN // BATCH_SIZE,
epochs=epochs,
validation_data=valid_generator,
validation_steps=NUM_VAL // BATCH_SIZE,
verbose=1,callbacks=[checkpoint])
efficientnet_test.py
import os,sys
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from PIL import Image
import random
from tensorflow.keras.callbacks import ModelCheckpoint
import efficientnet
from tensorflow.keras.preprocessing import image
print(tf.__version__)
print(sys.version_info)
HEIGHT = 224
WIDTH = 224
BATCH_SIZE = 20
SIZE = HEIGHT
NUM_TRAIN = 17786
NUM_VAL = 1981
CHANNELS = 3
IMAGE_HEIGHT = HEIGHT
IMAGE_WIDTH = WIDTH
model = efficientnet.efficient_net_b0()
model.build(input_shape=(None, IMAGE_HEIGHT, IMAGE_WIDTH, CHANNELS))
model.summary()
optimizer_rmsp = tf.keras.optimizers.RMSprop()
model.compile(optimizer=optimizer_rmsp,
loss='categorical_crossentropy',
metrics=['accuracy'])
print(model.summary())
model.load_weights("/data_1/Yang/project_new/2020/tf_study/tf_xception/efficientnet/model/efficient-keras_model_001-0.3758.h5")
dict_label_tijiao = {"f_children":"0",
"f_young":"1",
"f_adult":"2",
"f_older":"3",
"m_children":"4",
"m_young":"5",
"m_adult":"6",
"m_older":"7"
}
own_label = {'f_adult': 0, 'f_children': 1, 'f_older': 2, 'f_young': 3, 'm_adult': 4, 'm_children': 5, 'm_older': 6, 'm_young': 7}
map_own_label2tijiao = {0:'f_adult',1:'f_children',2:'f_older',3:'f_young',4:'m_adult',5:'m_children',6:'m_older',7:'m_young'}
root_dir_test = "/data_2/big-data/compete/20200323/src_data/test-tijiao/"
with open(root_dir_test + 'result.txt','w')as fw:
for root, dirs, files in os.walk(root_dir_test):
if 0 == len(files):
continue
for img_name_ in files:
print(img_name_)
if img_name_.endswith(".jpg") or img_name_.endswith(".jpeg") or img_name_.endswith(".png"):
pos = img_name_.find(".")
name = img_name_[0:pos]
img_path = os.path.join(root,img_name_)
img = image.load_img(img_path, target_size=(SIZE, SIZE))
img = image.img_to_array(img) / 255.0
img = np.expand_dims(img, axis=0) # 为batch添加第四维
predictions = model.predict(img)
label = np.argmax(predictions, axis=1)
#label = 5
label_describe = map_own_label2tijiao[label[0]]
label_tijiao_val = dict_label_tijiao[label_describe]
content = name + " " + label_tijiao_val
fw.write(content + '\n')
# print(predictions)
# print("label=", label)
# print('*' * 100)
# cv2.imshow("img", img[0][:, :, ::-1])
# cv2.waitKey(0)
inceptionV3
import os
import sys
import tensorflow as tf
import time
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Model
from tensorflow.keras.layers import GlobalAveragePooling2D, GlobalMaxPooling2D, Reshape, Dense, multiply, Permute, Concatenate, Conv2D, Add, Activation, Lambda
from tensorflow.keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.applications.inception_v3 import InceptionV3, preprocess_input
from tensorflow.keras.optimizers import SGD
print(tf.__version__)
print(sys.version_info)
HEIGHT = 160
WIDTH = 160
BATCH_SIZE = 20
SIZE = HEIGHT
NUM_TRAIN = 17786
NUM_VAL = 1981
# 数据准备
IM_WIDTH, IM_HEIGHT = 160, 160 # InceptionV3指定的图片尺寸
FC_SIZE = 1024 # 全连接层的节点个数
NB_IV3_LAYERS_TO_FREEZE = 172 # 冻结层的数量
nb_classes = 8
# 添加新层
def add_new_last_layer(base_model, nb_classes):
"""
添加最后的层
输入
base_model和分类数量
输出
新的keras的model
"""
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(FC_SIZE, activation='relu')(x) # new FC layer, random init
predictions = Dense(nb_classes, activation='softmax')(x) # new softmax layer
model = tf.keras.models.Model(inputs=base_model.input, outputs=predictions)
return model
# 冻上NB_IV3_LAYERS之前的层
def setup_to_finetune(model):
"""Freeze the bottom NB_IV3_LAYERS and retrain the remaining top layers.
note: NB_IV3_LAYERS corresponds to the top 2 inception blocks in the inceptionv3 arch
Args:
model: keras model
"""
for layer in model.layers[:NB_IV3_LAYERS_TO_FREEZE]:
layer.trainable = False
for layer in model.layers[NB_IV3_LAYERS_TO_FREEZE:]:
layer.trainable = True
model.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy', metrics=['accuracy'])
# 设置网络结构
model = InceptionV3(weights='imagenet', include_top=False,input_shape=(HEIGHT, WIDTH, 3))
model = add_new_last_layer(model, nb_classes)
setup_to_finetune(model)
model.summary()
datagen_train = ImageDataGenerator(
rescale=1./255.0,
rotation_range=1.5,
width_shift_range=0.05,
height_shift_range=0.05,
shear_range=0.1,
zoom_range=0.1,
horizontal_flip=True,
fill_mode='nearest')
datagen_val = ImageDataGenerator(
rescale=1./255.0)
train_generator=datagen_train.flow_from_directory('/data_2/train',#类别子文件夹的上一级文件夹
batch_size=BATCH_SIZE,
shuffle=True,
target_size=[SIZE, SIZE],
class_mode='categorical'
)
valid_generator=datagen_val.flow_from_directory('/data_2/test',#类别子文件夹的上一级文件夹
batch_size=BATCH_SIZE,
shuffle=False,
target_size=[SIZE, SIZE],
class_mode='categorical'
)
print(train_generator.class_indices)
print(valid_generator.class_indices)
epochs = 10000
filepath = "./model/inception-keras_model_{epoch:03d}-{val_acc:.4f}.h5" #避免文件名称重复
checkpoint = ModelCheckpoint(filepath=filepath, monitor='val_acc', verbose=1,
save_best_only=False, mode='max')
history = model.fit_generator(generator = train_generator,
steps_per_epoch=NUM_TRAIN // BATCH_SIZE,
epochs=epochs,
validation_data=valid_generator,
validation_steps=NUM_VAL // BATCH_SIZE,
verbose=1,callbacks=[checkpoint])
xception 注意这里调用官方的模型之后又接了自己的全连接层，前提include_top=False，不用官方的最后的全连接层 tf.keras.applications.Xception(weights='imagenet', include_top=False, input_shape=(HEIGHT, WIDTH, 3))
import os
import sys
import tensorflow as tf
import time
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Model
from tensorflow.keras.layers import GlobalAveragePooling2D, GlobalMaxPooling2D, Reshape, Dense, multiply, Permute, Concatenate, Conv2D, Add, Activation, Lambda
from tensorflow.keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
from tensorflow.keras.callbacks import ModelCheckpoint
print(tf.__version__)
print(sys.version_info)
HEIGHT = 128
WIDTH = 128
BATCH_SIZE = 20
SIZE = HEIGHT
NUM_TRAIN = 17786
NUM_VAL = 1981
xception = tf.keras.applications.Xception(weights='imagenet', include_top=False, input_shape=(HEIGHT, WIDTH, 3))
# xception.trainable = False ###表示不训练，直接用训练好的
inputs = tf.keras.layers.Input(shape=(HEIGHT, WIDTH, 3))
x = xception(inputs)
x = tf.keras.layers.GlobalAveragePooling2D()(x)
x1 = tf.keras.layers.Dense(512, activation='relu')(x)
x1 = tf.keras.layers.Dense(256, activation='relu')(x1)
out_x = tf.keras.layers.Dense(8, name='out_x')(x1)
predictions = out_x
model = tf.keras.models.Model(inputs=inputs, outputs=predictions,name='xception-keras')
print(model.summary())
model.compile(loss="categorical_crossentropy",
optimizer="adam", metrics=['accuracy'])
datagen_train = ImageDataGenerator(
rescale=1./255.0,
rotation_range=2,
width_shift_range=0.1,
height_shift_range=0.1,
shear_range=0.1,
zoom_range=0.1,
horizontal_flip=True,
fill_mode='nearest')
datagen_val = ImageDataGenerator(
rescale=1./255.0)
train_generator=datagen_train.flow_from_directory('/data_2/train',#类别子文件夹的上一级文件夹
batch_size=BATCH_SIZE,
shuffle=True,
target_size=[SIZE, SIZE],
class_mode='categorical'
)
valid_generator=datagen_val.flow_from_directory('/data_2/test',#类别子文件夹的上一级文件夹
batch_size=BATCH_SIZE,
shuffle=False,
target_size=[SIZE, SIZE],
class_mode='categorical'
)
print(train_generator.class_indices)
print(valid_generator.class_indices)
epochs = 10000
filepath = "./model/0keras_model_{epoch:03d}-{val_acc:.4f}.h5" #避免文件名称重复
checkpoint = ModelCheckpoint(filepath=filepath, monitor='val_acc', verbose=1,
save_best_only=False, mode='max')
history = model.fit_generator(generator = train_generator,
steps_per_epoch=NUM_TRAIN // BATCH_SIZE,
epochs=epochs,
validation_data=valid_generator,
validation_steps=NUM_VAL // BATCH_SIZE,
verbose=1,callbacks=[checkpoint])
其他网络可以参考官方，
或者参考github： https://github.com/calmisential/Basic_CNNs_TensorFlow2
好记性不如烂键盘---点滴、积累、进步！
