def train_factory(MODEL_NAME):
config = tf.ConfigProto()
config.gpu_options.allocator_type = 'BFC'
config.gpu_options.allow_growth = True
set_session(tf.Session(config=config))
# model = CCR(input_shape=(img_width,img_height,1),classes=charset_size)
# model = LeNet.build(width=img_width, height=img_height, depth=1, classes=charset_size)
# model = ResNet.build_model(SHAPE=(img_width,img_height,1), classes=charset_size)
# vgg net 5
# MODEL_PATH='trained_model/vggnet5.hdf5'
# model=VGGNet5.vgg(input_shape=(img_width,img_height,1),classes=charset_size)
model=None
if(MODEL_NAME=='inception_resnet_v2'):
model=InceptionResNetV2.inception_resnet_v2(input_shape=(img_width,img_height,3),classes=charset_size,weights='./trained_model/inception_resnet_v2/inception_resnet_v2.12-0.8244.hdf5')
elif(MODEL_NAME=='xception'):
# xeception
model=Xception.Xception((img_width,img_height,3),classes=charset_size)
elif(MODEL_NAME=='mobilenet_v2'):
#mobilenet v2
model=MobileNetv2.MobileNet_v2((img_width,img_height,3),classes=charset_size)
elif(MODEL_NAME=='inception_v3'):
#mobilenet v2
model=Inception_v3.inception((img_width,img_height,3),classes=charset_size)
elif(MODEL_NAME=='vgg16'):
model=VGGNet.vgg(input_shape=(img_width,img_height,3),classes=charset_size)
elif(MODEL_NAME=='vgg19'):
model=VGG19.VGG19(input_shape=(img_width,img_height,3),classes=charset_size,weights='weights/vgg19_weights_tf_dim_ordering_tf_kernels.h5')
elif(MODEL_NAME=='resnet50'):
model=ResNet50.resnet(input_shape=(img_width,img_height,3),classes=charset_size)
elif(MODEL_NAME=='inception_v4'):
model=inception_v4.inception_v4(input_shape=(img_width,img_height,3),classes=charset_size)
elif(MODEL_NAME=='resnet34'):
model=ResNet34.ResNet34(input_shape=(img_width,img_height,3),classes=charset_size)
elif(MODEL_NAME=='densenet121'):
model=DenseNet.DenseNet(input_shape=(img_width,img_height,3),classes=charset_size)
elif(MODEL_NAME=='densenet161'):
model=DenseNet.DenseNet(input_shape=(img_width,img_height,3),classes=charset_size)
elif(MODEL_NAME=='shufflenet_v2'):
model=ShuffleNetV2.ShuffleNetV2(input_shape=(img_width,img_height,3),classes=charset_size)
elif(MODEL_NAME=='resnet_attention_56'):
model=Resnet_Attention_56.Resnet_Attention_56(input_shape=(img_width,img_height,3),classes=charset_size)
elif(MODEL_NAME=='squeezenet'):
model=SqueezeNet.SqueezeNet(input_shape=(img_width,img_height,3),classes=charset_size)
elif(MODEL_NAME=='seresnet50'):
model=SEResNet50.SEResNet50(input_shape=(img_width,img_height,3),classes=charset_size)
elif(MODEL_NAME=='se_resnext'):
model=SEResNext.SEResNext(input_shape=(img_width,img_height,3),classes=charset_size)
elif(MODEL_NAME=='nasnet'):
model=NASNet.NASNetLarge(input_shape=(img_width,img_height,3),classes=charset_size)
elif(MODEL_NAME=='custom'):
model=Custom_Network.Custom_Network(input_shape=(img_width,img_height,3),classes=charset_size)
elif(MODEL_NAME=='resnet18'):
model=ResnetBuilder.build_resnet_18(input_shape=(img_width,img_height,3),num_outputs=charset_size)
print(model.summary())
train(model,MODEL_NAME)
num_epochs=configs['num_epochs']
data_path='data/HARRISON/preprocessed_data/'
# image_path='/home/eric/data/social_images/'
image_path='/home/eric/data/HARRISON/'
model_name='resnet50'
object_image_features_filename='resnet50_image_name_to_features.h5'
generator=Generator(data_path=data_path,batch_size=batch_size,image_features_filename=object_image_features_filename)
config = tf.ConfigProto()
config.gpu_options.allocator_type = 'BFC'
config.gpu_options.allow_growth = True
set_session(tf.Session(config=config))
# model=VGGNet.vgg(input_shape=(224,224,3),classes=generator.VOCABULARY_SIZE)
model=ResNet50.resnet(input_shape=(configs['img_width'],configs['img_height'],configs['channel']), classes=generator.VOCABULARY_SIZE)
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=["accuracy",fmeasure,precision,recall])
print(model.summary())
plot_model(model,show_shapes=True,to_file=model_name+'.png')
save_path=os.path.join('trained_model',model_name)
if(not os.path.exists(save_path)):
os.makedirs(save_path)
model_names = (os.path.join(save_path,'hashtag_weights.{epoch:02d}-{val_loss:.4f}.hdf5'))
model_checkpoint = ModelCheckpoint(filepath=model_names,
monitor='val_loss',verbose=1, save_best_only=True, mode='max')
callbacks = [model_checkpoint]
num_training_samples = generator.training_dataset.shape[0]
num_validation_samples = generator.validation_dataset.shape[0]
print('Number of training samples:', num_training_samples)
