def main(model, train_file, valid_file, ckpt_folder, optimizer, batch_size,
max_steps, lr, l2, fine_tuning):
if optimizer == 'Adam':
optimizer = Adam(lr=lr)
if l2 > 0:
regularizer = regularizers.l2(l2)
else:
regularizer = None
metrics = ['categorical_accuracy']
loss = 'categorical_crossentropy'
if model == 'VGG16Trunk':
model_fn = VGG16Trunk(IMAGE_SHAPE, INPUT_NAME, optimizer, loss,
metrics, fine_tuning)
elif model == 'XceptionTrunk':
model_fn = Xception(IMAGE_SHAPE, INPUT_NAME, optimizer, loss, metrics,
fine_tuning)
model_fn.summary()
# Start training
my_train_and_evaluate(model_fn=model_fn,
train_file=train_file,
valid_file=valid_file,
ckpt_folder=ckpt_folder,
batch_size=batch_size,
max_steps=max_steps)
def process_images_thru_tl(batch_size=32, input1=1024, input2=1024, model_name="vgg"):
with tf.device("/device:GPU:1"):
if model_name == "vgg":
model = VGG16(weights = "imagenet", include_top=False, input_shape = [224, 224, 3])
size = 224
elif model_name == "inceptionv3":
model = InceptionV3(weights = "imagenet", include_top=False, input_shape = [299, 299, 3])
size = 299
elif model_name == "resnet50":
model = ResNet50(weights = "imagenet", include_top=False, input_shape = [224, 224, 3])
size = 224
elif model_name == "mobilenet":
model = ResNet50(weights = "imagenet", include_top=False, input_shape = [224, 224, 3])
size = 224
elif model_name == "xception":
model = Xception(weights = "imagenet", include_top=False)
size = 299
print("%s %d %d %d" % (model_name, input1, input2, batch_size))
model.summary()
model.get_weights()
labels = []
batch = []
# input_ = Input(shape=(size,size,3),name = 'image_input')
output_ = model.output
with tf.device("/device:GPU:1"):
if model_name == "inceptionv3" or model_name == "xception":
x = GlobalAveragePooling2D(name='avg_pool')(output_)
else:
x = Flatten(name='flatten')(output_)
if input1 != 0:
x = Dense(input1, activation='relu', name='fc1')(x)
if input2 != 0:
x = Dense(input2, activation='relu', name='fc2')(x)
x = Dense(128, activation='softmax', name='predictions')(x)
for layer in model.layers:
layer.trainable = False
my_model = Model(inputs=output_, outputs=x)
my_model.summary()
if os.path.exists("weights_%s_%d_%d_%d.h5" % (model_name, input1, input2, batch_size)):
my_model.load_weights("weights_%s_%d_%d_%d.h5" % (model_name, input1, input2, batch_size))
my_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
train_generator = get_train_data.train_generator(img_size=size, batch_size=batch_size)
valid_generator = get_train_data.valid_generator(img_size=size, batch_size=8)
csv_logger = CSVLogger('log.csv', append=True, separator=',')
my_model.fit_generator(
train_generator,
steps_per_epoch=2000,#1000
epochs=10,
validation_data=valid_generator,
validation_steps=200,#200
callbacks=[csv_logger])
my_model.save_weights("weights_%s_%d_%d_%d.h5" % (model_name, input1, input2, batch_size))
