def train(
model_type,
path_args,
training_args,
model_args,
output_args,
load_dataset_args,
checkpoint_args,
extension_args,
):
"""
---------------------------------------------
Input: None
Output: None
Run the test harness for evaluating a model
---------------------------------------------
"""
pretrained = training_args.pop("pretrained")
results_folder = training_args.pop("results_folder")
data_format = load_dataset_args["data_format"]
checkpoint_path = checkpoint_args["checkpoint_path"]
paths = utils.get_paths(**path_args)
utils.check_folders(paths, **extension_args)
keras.backend.set_image_data_format(data_format)
callbacks = []
callbacks.append(utils.get_early_stopping_callback())
callbacks.append(utils.get_tensorboard_directory_callback())
callbacks.append(utils.get_checkpoint_callback(checkpoint_path))
if pretrained:
model = keras.models.load_model(checkpoint_path)
elif model_type == "unet":
model = unet.define_model(output_args, **model_args)
train, val = datagen.load_dataset(paths, load_dataset_args)
history = model.fit_generator(train,
validation_data=val,
callbacks=callbacks,
**training_args)
return (history, model)
import tensorflow as tf
import numpy as np
from models import build_model
from datagen import load_dataset, genxy
net_name = "Net"
output_path = 'output'
anno_file_path = '/Users/baulhoa/Documents/PythonProjects/datasets/faceali/train.txt'
image_dir_path = '/Users/baulhoa/Documents/PythonProjects/datasets/faceali/train'
ishape = [112, 112, 1]
total_epoches = 1000
batch_size = 100
dataset = load_dataset(anno_file_path=anno_file_path)
total_examples = len(dataset)
total_batches = total_examples // batch_size
model = build_model(ishape=ishape, mode='train', net_name=net_name)
# model.summary()
# model.load_weights('{}/weights_.h5'.format(output_path), by_name=True)
min_loss = 2**32
for epoch in range(total_epoches):
# tf.keras.backend.set_value(model.optimizer.learning_rate, 0.001)
gen = genxy(dataset=dataset,
image_dir_path=image_dir_path,
ishape=ishape,
total_batches=total_batches,
model = build_model(ishape=ishape,
resnet_settings=resnet_settings,
k=len(asizes),
total_classes=total_classes,
net_name=net_name)
# model.summary()
model.load_weights('{}/weights_.h5'.format(output_path), by_name=True)
min_loss = 2**32
max_precision = 0
max_recall = 0
max_precision_recall = 0
gendata = genxy_com if combine is True else genxy
train_dataset = load_dataset(anno_file_path=train_anno_file_path)
test_dataset = load_dataset(anno_file_path=test_anno_file_path)
for epoch in range(total_epoches):
# tf.keras.backend.set_value(model.optimizer.learning_rate, 0.001)
gen = gendata(dataset=train_dataset,
image_dir=train_image_dir,
ishape=ishape,
abox_2dtensor=abox_2dtensor,
iou_thresholds=iou_thresholds,
total_examples=total_train_examples,
total_classes=total_classes,
anchor_sampling=anchor_sampling)
print('\nTrain epoch {}'.format(epoch))
from datagen import load_dataset
# Global
import numpy as np
import random as rd
import matplotlib.pyplot as plt
from tensorflow import keras as ks
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LeakyReLU, Reshape, Conv2DTranspose, Conv2D, Flatten, Dropout
## Parameters and dataset
Ldim = 100
P = 10
Shape = (28, 28, 1)
X, Y = load_dataset()
## Gan
Gan = Gan(ldim=Ldim, p=P, shape=Shape)
Gan.load('C:/Users/meri2/Documents/Projects/MNSIT_GAN/Attempt_0')
Gan.make_gan()
losses, accuracies, times = Gan.train(
X,
Y,
epochs=0,
batch_size=256,
)
Gan.samples(7)