def train():
epochs = 300
n_classes = 5
batch_train = 64
batch_test = 20
image_w = 224
image_h = 224
# 导入数据
path = '../../../../datasets/traindatas/flower_data/train' # 读入数据的路径
path_t = '../../../../datasets/traindatas/flower_data/val'
################################自写数据生成方法###############################
'''
datagen = dataGenerator() # 实例化数据生成器
# 执行一次获取数据
datagen.readData(path)
samples_num = datagen.num_samples
train_datas = datagen.augment(True, path, batch_train, True, PreDataForVGG, image_w, image_h) # 导入数据并进行增强
datagen_t = dataGenerator()
# 执行一次获取数据
datagen_t.readData(path_t)
samples_num_t = datagen_t.num_samples
val_datas = datagen_t.augment(False, path_t, batch_test, False, PreDataForVGG, image_w, image_h)
'''
############################################################################
params = dict( # rescale=1./255,
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
vertical_flip=True,
preprocessing_function=PreDataForCaffe) # 配置参数
train_datagen = dataGenerator(**params)
test_datagen = dataGenerator(preprocessing_function=PreDataForCaffe)
train_generator = train_datagen.flow_from_directory(path,
batch_size=batch_train,
target_size=(image_w,
image_h),
shuffle=True)
test_generator = test_datagen.flow_from_directory(path_t,
batch_size=batch_test,
target_size=(image_w,
image_h),
shuffle=False)
train_num = train_generator.samples
test_num = test_generator.samples
############################################################################
inputs = Input(shape=[image_w, image_h, 3])
base_model = resnet50(
include_top=False,
weights=
'../../../../preweights/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5',
input_tensor=inputs)
# fla = Flatten()(base_model.output)
fla = GlobalAveragePooling2D()(base_model.output)
# x = Dense(1024, activation='relu')(fla)
# x = Dropout(rate=0.5)(x)
# x = Dense(128, activation='relu')(x)
# x = Dropout(rate=0.5)(x)
# x = Dense(128, activation='relu')(fla)
# x = Dropout(rate=0.5)(x)
output = Dense(n_classes, activation='softmax')(fla)
model = Model(inputs=inputs, outputs=output) # 完成Model构建
base_model.trainable = False
model.summary()
# 编译模型
optimizer = optimizers.Adam(lr=0.0002)
model.compile(optimizer=optimizer,
loss='categorical_crossentropy',
metrics=['acc'])
#设置callback
callbacks = [
# EarlyStopping(patience=5, verbose=1),
ModelCheckpoint('saveWeights/resnet18TrsModel.h5',
monitor='val_acc',
verbose=1,
save_best_only=True,
mode='max')
]
# 训练
'''
model.fit_generator(train_datas,
steps_per_epoch=math.ceil(samples_num/batch_train),
epochs=epochs,
validation_data=val_datas,
validation_steps=math.ceil(samples_num_t/batch_test),
verbose=2,
callbacks=callbacks)'''
model.fit_generator(train_generator,
steps_per_epoch=math.ceil(train_num / batch_train),
epochs=epochs,
validation_data=test_generator,
validation_steps=math.ceil(test_num / batch_test),
verbose=2,
callbacks=callbacks)
def train():
epochs = 300
n_classes = 5
batch_train = 32
batch_test = 20
image_w = 224
image_h = 224
# 导入数据
path = '../../../../datasets/traindatas/flower_data/train' # 读入数据的路径
path_t = '../../../../datasets/traindatas/flower_data/val'
Tcnt = get_nb_files(path)
Vcnt = get_nb_files(path_t)
################################自写数据生成方法###############################
'''
datagen = dataGenerator() # 实例化数据生成器
# 执行一次获取数据
datagen.readData(path)
samples_num = datagen.num_samples
train_datas = datagen.augment(True, path, batch_train, True, PreDataForVGG, image_w, image_h) # 导入数据并进行增强
datagen_t = dataGenerator()
# 执行一次获取数据
datagen_t.readData(path_t)
samples_num_t = datagen_t.num_samples
val_datas = datagen_t.augment(False, path_t, batch_test, False, PreDataForVGG, image_w, image_h)
'''
############################################################################
params = dict( # rescale=1./255,
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
vertical_flip=True,
preprocessing_function=PreDataForTF) # 配置参数
train_datagen = dataGenerator(**params)
test_datagen = dataGenerator(preprocessing_function=PreDataForTF)
train_generator = train_datagen.flow_from_directory(path,
batch_size=batch_train,
target_size=(image_w,
image_h),
shuffle=True)
test_generator = test_datagen.flow_from_directory(path_t,
batch_size=batch_test,
target_size=(image_w,
image_h),
shuffle=False)
############################################################################
inputs = Input(shape=[image_w, image_h, 3])
model = vgg11(inputs, n_classes)
model.summary()
# 编译模型
optimizer = optimizers.Adam(lr=0.0003)
model.compile(optimizer=optimizer,
loss='categorical_crossentropy',
metrics=['acc'])
#设置callback
callbacks = [
#EarlyStopping(patience=5, verbose=1),
ModelCheckpoint('saveWeights/vgg11Model.h5',
monitor='val_acc',
verbose=1,
save_best_only=True,
mode='max')
]
# 训练
'''
model.fit_generator(train_datas,
steps_per_epoch=math.ceil(samples_num/batch_train),
epochs=epochs,
validation_data=val_datas,
validation_steps=math.ceil(samples_num_t/batch_test),
verbose=2,
callbacks=callbacks)'''
model.fit_generator(train_generator,
steps_per_epoch=math.ceil(Tcnt / batch_train),
epochs=epochs,
validation_data=test_generator,
validation_steps=math.ceil(Vcnt / batch_test),
verbose=2,
callbacks=callbacks)