def __init__(self, backend):
self.train_data = dataset.train("/tmp/mnist_data")
self.test_data = dataset.test("/tmp/mnist_data")
self.backend = backend
self.train_images = np.reshape(
self.train_data['images'][:TRAIN_SIZE],
(-1, IMAGE_SIZE, IMAGE_SIZE, IMAGE_DEPTH))
self.train_labels = self.train_data['labels'][:TRAIN_SIZE]
self.test_images = np.reshape(
self.test_data['images'][:TRAIN_SIZE],
(-1, IMAGE_SIZE, IMAGE_SIZE, IMAGE_DEPTH))
self.test_labels = self.test_data['labels'][:TRAIN_SIZE]
if backend == 'gpu':
self.device = "/device:GPU:0"
else:
assert backend == 'cpu', 'Invalid backend specified: %s' % backend
self.device = "/cpu:0"
print("Creating model")
self.model = MNIST.create_model()
def main(disable=0, device="cpu", cycles=100, D=32, N=128, name="evo"):
disable = int(disable)
cycles = int(cycles)
print("Using device: %s" % device)
N = int(N)
D = int(D)
data = dataset.create(N, D)
test = dataset.test(N, D)
if name == "evo":
net = model.EvolutionaryModel(D, disable=disable).to(device)
try:
for i in range(cycles):
net.do_cycle(*data, *test)
except KeyboardInterrupt:
pass
best = net.select_best()
print(best.net[0].weight.data)
train.visualize(net.select_best(), outf="results.png", D=D)
else:
net = model.Model(D)
train.train(*data, *test, net)
print(net.net[0].weight)
def eval_input_fn(params):
batch_size = params["batch_size"]
data_dir = params["data_dir"]
ds = dataset.test(data_dir).apply(
tf.contrib.data.batch_and_drop_remainder(batch_size))
images, labels = ds.make_one_shot_iterator().get_next()
return images, labels
def main(unused):
if FLAGS.run_gpu:
backend = "/device:GPU:0"
else:
backend = "/cpu:0"
mnist_classifier = tf.estimator.Estimator(model_fn=cnn_model_fn,
model_dir=FLAGS.model_dir,
params={
'backend': backend,
})
if FLAGS.mode == 'train' or FLAGS.mode == 'both':
ds = dataset.train(FLAGS.data_dir)
# Train the model
train_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": ds['images']},
y=ds['labels'],
batch_size=FLAGS.batch_size,
num_epochs=FLAGS.train_epochs,
shuffle=True)
mnist_classifier.train(input_fn=train_input_fn, steps=200)
if FLAGS.mode == 'predict' or FLAGS.mode == 'both':
ds = dataset.test(FLAGS.data_dir)
# Predict test set
pred_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": ds['images']}, shuffle=False)
mnist_classifier.predict(input_fn=pred_input_fn)
def eval_input_fn(params):
batch_size = params["batch_size"]
data_dir = params["data_dir"]
ds = dataset.test(data_dir).apply(
tf.contrib.data.batch_and_drop_remainder(batch_size))
images, labels = ds.make_one_shot_iterator().get_next()
return images, labels
async def send_to_net(self, image, loop=None):
dataset = ModelDataset(image, self.augmentation)
model = ResnetGenerator()
model.load_state_dict(
torch.load('/opt/app/model_gen_BtoA2.pth',
map_location=torch.device('cpu')))
image1 = await loop.create_task(test(dataset, model, loop))
image1 = Image.fromarray((image1 * 255).astype('uint8'))
image1.save(self.imgByteArr, 'JPEG')
im = self.imgByteArr.getvalue()
return im
def train():
questions = list(questions_from_dataset(dataset.train()))
random.shuffle(questions)
test_questions = list(questions_from_dataset(dataset.test()))
random.shuffle(test_questions)
i = 0
for i, batch in enumerate(iterate_batches(questions, size=20)):
n.train(batch)
if i % 10 == 0:
n.save(i)
def run_mnist_eager():
"""Run MNIST training and eval loop in eager mode.
"""
data_dir = '/tmp/tensorflow/mnist/input_data' + str(ddl.rank())
model_dir = '/tmp/tensorflow/mnist/checkpoints/' + str(ddl.rank()) + '/'
# Delete model dir
if os.path.isdir(model_dir) and ddl.local_rank() == 0:
shutil.rmtree(model_dir)
data_format = 'channels_first'
# Load the datasets
train_ds, _ = mnist_dataset.train(data_dir, (1, 28, 28), label_int=True)
train_ds = train_ds.shard(ddl.size(),
ddl.rank()).shuffle(60000).batch(batch_size)
test_ds, _ = mnist_dataset.test(data_dir, (1, 28, 28), label_int=True)
test_ds = test_ds.batch(batch_size)
# Create the model and optimizer
model = create_model(data_format)
optimizer = tf.train.MomentumOptimizer(0.01, 0.5)
train_dir = None
test_dir = None
summary_writer = tf.contrib.summary.create_file_writer(train_dir,
flush_millis=10000)
test_summary_writer = tf.contrib.summary.create_file_writer(
test_dir, flush_millis=10000, name='test')
# Create and restore checkpoint (if one exists on the path)
checkpoint_prefix = os.path.join(model_dir, 'ckpt-r' + str(ddl.rank()))
step_counter = tf.train.get_or_create_global_step()
checkpoint = tf.train.Checkpoint(model=model,
optimizer=optimizer,
step_counter=step_counter)
# Restore variables on creation if a checkpoint exists.
checkpoint.restore(tf.train.latest_checkpoint(model_dir))
# Train and evaluate for a set number of epochs.
for _ in range(train_epochs):
start = time.time()
with summary_writer.as_default():
train(model, optimizer, train_ds, step_counter, 10)
end = time.time()
if ddl.rank() == 0:
print('\nTrain time for epoch #%d (%d total steps): %f' %
(checkpoint.save_counter.numpy() + 1, step_counter.numpy(),
end - start))
with test_summary_writer.as_default():
test(model, test_ds)
checkpoint.save(checkpoint_prefix)
def main(_):
tfe.enable_eager_execution()
(device, data_format) = ('/gpu:0', 'channels_first')
if FLAGS.no_gpu or tfe.num_gpus() <= 0:
(device, data_format) = ('/cpu:0', 'channels_last')
print('Using device %s, and data format %s.' % (device, data_format))
# Load the datasets
train_ds = dataset.train(FLAGS.data_dir).shuffle(60000).batch(
FLAGS.batch_size)
test_ds = dataset.test(FLAGS.data_dir).batch(FLAGS.batch_size)
# Create the model and optimizer
model = mnist.Model(data_format)
optimizer = tf.train.MomentumOptimizer(FLAGS.lr, FLAGS.momentum)
if FLAGS.output_dir:
# Create directories to which summaries will be written
# tensorboard --logdir=<output_dir>
# can then be used to see the recorded summaries.
train_dir = os.path.join(FLAGS.output_dir, 'train')
test_dir = os.path.join(FLAGS.output_dir, 'eval')
tf.gfile.MakeDirs(FLAGS.output_dir)
else:
train_dir = None
test_dir = None
summary_writer = tf.contrib.summary.create_file_writer(train_dir,
flush_millis=10000)
test_summary_writer = tf.contrib.summary.create_file_writer(
test_dir, flush_millis=10000, name='test')
checkpoint_prefix = os.path.join(FLAGS.checkpoint_dir, 'ckpt')
step_counter = tf.train.get_or_create_global_step()
checkpoint = tfe.Checkpoint(model=model,
optimizer=optimizer,
step_counter=step_counter)
# Restore variables on creation if a checkpoint exists.
checkpoint.restore(tf.train.latest_checkpoint(FLAGS.checkpoint_dir))
# Train and evaluate for 10 epochs.
with tf.device(device):
for _ in range(10):
start = time.time()
with summary_writer.as_default():
train(model, optimizer, train_ds, step_counter,
FLAGS.log_interval)
end = time.time()
print('\nTrain time for epoch #%d (%d total steps): %f' %
(checkpoint.save_counter.numpy() + 1, step_counter.numpy(),
end - start))
with test_summary_writer.as_default():
test(model, test_ds)
checkpoint.save(checkpoint_prefix)
def main(_):
tfe.enable_eager_execution()
(device, data_format) = ('/gpu:0', 'channels_first')
if FLAGS.no_gpu or tfe.num_gpus() <= 0:
(device, data_format) = ('/cpu:0', 'channels_last')
print('Using device %s, and data format %s.' % (device, data_format))
# Load the datasets
train_ds = dataset.train(FLAGS.data_dir).shuffle(60000).batch(
FLAGS.batch_size)
test_ds = dataset.test(FLAGS.data_dir).batch(FLAGS.batch_size)
# Create the model and optimizer
model = mnist.Model(data_format)
optimizer = tf.train.MomentumOptimizer(FLAGS.lr, FLAGS.momentum)
if FLAGS.output_dir:
# Create directories to which summaries will be written
# tensorboard --logdir=<output_dir>
# can then be used to see the recorded summaries.
train_dir = os.path.join(FLAGS.output_dir, 'train')
test_dir = os.path.join(FLAGS.output_dir, 'eval')
tf.gfile.MakeDirs(FLAGS.output_dir)
else:
train_dir = None
test_dir = None
summary_writer = tf.contrib.summary.create_file_writer(
train_dir, flush_millis=10000)
test_summary_writer = tf.contrib.summary.create_file_writer(
test_dir, flush_millis=10000, name='test')
checkpoint_prefix = os.path.join(FLAGS.checkpoint_dir, 'ckpt')
step_counter = tf.train.get_or_create_global_step()
checkpoint = tfe.Checkpoint(
model=model, optimizer=optimizer, step_counter=step_counter)
# Restore variables on creation if a checkpoint exists.
checkpoint.restore(tf.train.latest_checkpoint(FLAGS.checkpoint_dir))
# Train and evaluate for 10 epochs.
with tf.device(device):
for _ in range(10):
start = time.time()
with summary_writer.as_default():
train(model, optimizer, train_ds, step_counter, FLAGS.log_interval)
end = time.time()
print('\nTrain time for epoch #%d (%d total steps): %f' %
(checkpoint.save_counter.numpy() + 1,
step_counter.numpy(),
end - start))
with test_summary_writer.as_default():
test(model, test_ds)
checkpoint.save(checkpoint_prefix)
def run_create_concrete():
path = "./configs/pairs/"
config_files = [
join(path, f) for f in listdir(path) if isfile(join(path, f))
if ".py" not in f and "generic" not in f and "__" not in f
]
for config_file in config_files:
print("MMMMMMMMMMMMMMMMMMMMMMMMMM", config_file)
_config = process_config(config_file, create_folders=False)
data_root = join(_config.data_root, "valid")
ds = dataset.Dataset(data_root,
specific_folders=_config.specific_classes)
classes = ds.classes
## Find checkpoint
print(_config.exp_name)
checkpoint_file = glob(
f"./experiments/{_config.exp_name}*/checkpoints/killed*.pth"
)[0]
self.load_checkpoint(checkpoint_file)
print("CHECKPOINT!", checkpoint_file)
input_channels = int(_config.input_channels)
loader, test_ds = dataset.test(
"./input/quickdraw/test_simplified.csv",
200,
_config.image_size,
num_workers=8,
input_channels=input_channels)
self.validate_concrete("test", classes, loader, ds)
## valid
ds = self.valid_dataset
loader = self.valid_data_loader
self.validate_concrete("valid", classes, loader, ds)
def openfile():
filepath = filedialog.askopenfilename(initialdir="test",
title="Тест",
filetypes=(("png файлы", "*.png"),
("png файлы", "*.png")))
test_image = test(filepath, 1024)
imgt = ImageTk.PhotoImage(Image.open(filepath))
panel.config(image=imgt)
panel.image = imgt
number_of_answer = ""
max_weight = 0
print("Нейросеть думает...\n")
test_data = network.query(test_image)
for g in range(len(test_data)):
print(ALPHABET.get(g) + ") ", test_data[g])
if max_weight < test_data[g]:
max_weight = test_data[g]
number_of_answer = g
answer = ALPHABET.get(number_of_answer)
label2.config(text=answer)
print("\nНейросеть думает, что на картинке: ", answer, '\n')
import torch
import test_model
import dataset
import os
import loss
import tqdm
import cv2
import predict as pt
model = test_model.YoloModel().cuda()
model = torch.nn.DataParallel(model).cuda()
print('preparing data...')
train_data = dataset.train()
test_data = dataset.test()
print('done')
MODEL_PATH = 'yolo1.h5'
if os.path.exists(MODEL_PATH):
print('loading saved state...')
model.load_state_dict(torch.load(MODEL_PATH))
print('loading done')
print('start traing...')
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4, weight_decay=5e-4)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=1,
gamma=0.95)
loss_fun = loss.yololoss()
bestloss = 1e10
e = 0
# for e in range(1200):
while True:
def eval_input_fn():
ds = dataset.test(data_path)
ds = ds.batch(params['batch_size'])
return ds
def eval_input_fn():
return dataset.test(DATA_DIR).batch(
FLAGS.batch_size).make_one_shot_iterator().get_next()
def eval_input_fn(): return tf.compat.v1.data.make_one_shot_iterator(dataset.test(DATA_DIR).batch(FLAGS.batch_size)).get_next()
congress = dataset.fromFile('congress', 0)
r = congress.tree()
if __name__ == '__main__':
print(r)
res, pre, rec, har = r.stats()
print("\nresults: " + str(res))
print("precision: " + str(pre))
print("recall: " + str(rec))
print("harmonic mean " + str(har))
print("\nPRUNED:\n")
r.prune(0.33)
print(r)
res, pre, rec, har = r.stats()
print("\nresults: " + str(res))
print("precision: " + str(pre))
print("recall: " + str(rec))
print("harmonic mean " + str(har))
print("\nTESTING:\n")
r, stats = dataset.test(congress, 50)
print(r)
res, pre, rec, har = stats
print("\nresults: " + str(res))
print("precision: " + str(pre))
print("recall: " + str(rec))
print("harmonic mean " + str(har))
def eval_full_data(params):
batch_size = params['batch_size']
data_dir = params['data_dir']
data = dataset.test(data_dir)
data = data.batch(batch_size, drop_remainder=True)
return data
def main(params):
# basic parameters
parser = argparse.ArgumentParser()
parser.add_argument('--checkpoint_path',
type=str,
default=None,
required=True,
help='The path to the pretrained weights of model')
parser.add_argument(
'--crop_height',
type=int,
default=640,
help='Height of cropped/resized input image to network')
parser.add_argument('--crop_width',
type=int,
default=640,
help='Width of cropped/resized input image to network')
parser.add_argument('--data',
type=str,
default='/path/to/data',
help='Path of training data')
parser.add_argument('--batch_size',
type=int,
default=1,
help='Number of images in each batch')
parser.add_argument('--context_path',
type=str,
default="resnet101",
help='The context path model you are using.')
parser.add_argument('--cuda',
type=str,
default='0',
help='GPU ids used for training')
parser.add_argument('--use_gpu',
type=bool,
default=True,
help='Whether to user gpu for training')
parser.add_argument('--num_classes',
type=int,
default=2,
help='num of object classes (with void)')
args = parser.parse_args(params)
# create dataset and dataloader
dataloader = DataLoader(test(input_transform, target_transform),
num_workers=1,
batch_size=1,
shuffle=True)
# build model
os.environ['CUDA_VISIBLE_DEVICES'] = args.cuda
model = DANet(nclass=2, backbone='resnet50', aux=False, se_loss=False)
model = model.cuda()
# load pretrained model if exists
print('load model from %s ...' % args.checkpoint_path)
model.load_state_dict(torch.load(args.checkpoint_path))
print('Done!')
# test
eval(model, dataloader, args)
def run_mnist_eager(flags_obj):
"""
Run MNIST training and eval loop in eager mode.
Args:
flags_obj: An object containing parsed flag values.
"""
# Soft placement
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
tfe.enable_eager_execution(config=config)
model_helpers.apply_clean(flags.FLAGS)
# Automatically determine device and data_format
(device, data_format) = ('/gpu:0', 'channels_first')
if flags_obj.no_gpu or not tf.test.is_gpu_available():
(device, data_format) = ('/cpu:0', 'channels_last')
# If data_format is defined in FLAGS, overwrite automatically set value.
if flags_obj.data_format is not None:
data_format = flags_obj.data_format
print('Using device %s, and data format %s.' % (device, data_format))
# Load the datasets
train_ds = mnist_dataset.train(flags_obj.data_dir).shuffle(60000).batch(
flags_obj.batch_size)
test_ds = mnist_dataset.test(flags_obj.data_dir).batch(
flags_obj.batch_size)
# Create the model and optimizer
model = model_lib.create_model(data_format)
optimizer = tf.train.MomentumOptimizer(flags_obj.lr, flags_obj.momentum)
# Print model summary
print(model.summary())
# Create file writers for writing TensorBoard summaries.
if flags_obj.output_dir:
# Create directories to which summaries will be written
# tensorboard --logdir=<output_dir>
# can then be used to see the recorded summaries.
train_dir = os.path.join(flags_obj.output_dir, 'train')
test_dir = os.path.join(flags_obj.output_dir, 'eval')
tf.gfile.MakeDirs(flags_obj.output_dir)
else:
train_dir = None
test_dir = None
summary_writer = tf.contrib.summary.create_file_writer(train_dir,
flush_millis=10000)
test_summary_writer = tf.contrib.summary.create_file_writer(
test_dir, flush_millis=10000, name='test')
# Create and restore checkpoint (if one exists on the path)
checkpoint_prefix = os.path.join(flags_obj.model_dir, 'ckpt')
step_counter = tf.train.get_or_create_global_step()
checkpoint = tf.train.Checkpoint(model=model,
optimizer=optimizer,
step_counter=step_counter)
# Restore variables on creation if a checkpoint exists.
checkpoint.restore(tf.train.latest_checkpoint(flags_obj.model_dir))
# Train and evaluate for a set number of epochs.
with tf.device(device):
for _ in range(flags_obj.train_epochs):
start = time.time()
with summary_writer.as_default():
train(model, optimizer, train_ds, step_counter,
flags_obj.log_interval)
end = time.time()
# Note time taken
print('\nTrain time for epoch #%d (%d total steps): %f' %
(checkpoint.save_counter.numpy() + 1, step_counter.numpy(),
end - start))
with test_summary_writer.as_default():
test(model, test_ds)
checkpoint.save(checkpoint_prefix)
#from sklearn.model_selection import train_test_split
import numpy as np
import dataset
import os
batch_size = 64
num_classes = 62
epochs = 10
img_rows, img_cols = 28, 28
print('Start loading data.')
#Da modificar
folder_path = os.getcwd()
train_dataset = dataset.train(folder_path + '\emnist')
test_dataset = dataset.test(folder_path + '\emnist')
print('Data has been loaded.')
#Non so se le reshape dei tensor x e y contenenti train e test vadano fatte
if K.image_data_format() == 'channels_first':
#x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
#x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
#x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
#x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
"""print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')"""
def eval_input_fn():
return dataset.test('%s/validation' % FLAGS.data_dir).batch(
FLAGS.batch_size).make_one_shot_iterator().get_next()
def eval_input_fn(params):
batch_size = params["batch_size"]
data_dir = params["data_dir"]
ds = dataset.test(data_dir).batch(batch_size, drop_remainder=True)
return ds
def eval_input_fn():
return dataset.test(FLAGS.data_dir).batch(
FLAGS.batch_size).make_one_shot_iterator().get_next()
def eval_input_fn(): ds = dataset.test(data_dir) ds = ds.batch(batch_size) return ds
import tensorflow as tf
import dataset
sess = tf.InteractiveSession()
mnist_train = dataset.train("./mnist_data")
mnist_test = dataset.test("./mnist_data")
# https://www.tensorflow.org/guide/datasets
print(mnist_train.output_shapes, mnist_train.output_types)
print(mnist_test.output_shapes, mnist_test.output_types)
batched_train = mnist_train.batch(100)
iterator = batched_train.make_one_shot_iterator()
next_element = iterator.get_next()
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
# y is a matrix
y = tf.nn.softmax(tf.matmul(x, W) + b)
# TODO: try [None, 10]
y_ = tf.placeholder(tf.int32, [None])
# TODO: why this not work?
# y_ = tf.placeholder(tf.int32, [None, 1])
# y_ = tf.placeholder(tf.float32, [None, 10])
# cause y is a 2D matrix, note the meaning of reduce_sum
one_hot_y = tf.one_hot(y_, 10)
def eval_input_fn(data_dir, batch_size=100):
features = dataset.test(data_dir).batch(
batch_size=batch_size).make_one_shot_iterator().get_next()
return {'pixels': features[0]}, features[1]
def test(self):
self.model.eval()
test_data_loader, test_dataset = dataset.test(
self.config.testcsv,
self.config.batch_size,
self.config.image_size,
num_workers=8,
input_channels=self.input_channels)
self.load_checkpoint(self.config.checkpoint)
cls_to_idx = {
cls: idx
for idx, cls in enumerate(self.train_dataset.classes)
}
print(cls_to_idx)
idx_to_cls = {cls_to_idx[c]: c for c in cls_to_idx}
def row2string(r):
v = [r[-1], r[-2], r[-3]]
v = [v.item() for v in v]
v = map(lambda v: v if v < 340 else 0, v)
v = [idx_to_cls[v].replace(' ', '_') for v in v]
return ' '.join(v)
labels = []
key_ids = []
outputs = []
with torch.no_grad():
for idx, (data, target) in enumerate(test_data_loader):
data = data.to(self.device)
output = self.model(data)
n = output.detach().cpu().numpy()
outputs.append(n)
order = np.argsort(n, 1)[:, -3:]
# order = refine(order, "test", idx * self.config.batch_size)
predicted_y = [row2string(o) for o in order]
labels = labels + predicted_y
key_ids = key_ids + target.numpy().tolist()
if idx % 10 == 0:
print(f"{idx} of {len(test_data_loader)}")
import pickle
with open('labels', 'wb') as fp:
pickle.dump(key_ids, fp)
with open('idx_to_cls', 'wb') as fp:
pickle.dump(idx_to_cls, fp)
_all = np.concatenate(outputs)
np.save("output.npy", _all)
d = {'key_id': key_ids, 'word': labels}
df = pd.DataFrame.from_dict(d)
df.to_csv('submission.csv', index=False)
def main(argv):
parser = MNISTEagerArgParser()
flags = parser.parse_args(args=argv[1:])
# TF v1.7
tfe.enable_eager_execution()
# Automatically determine device and data_format
(device, data_format) = ('/gpu:0', 'channels_first')
if flags.no_gpu or tfe.num_gpus() <= 0:
(device, data_format) = ('/cpu:0', 'channels_last')
# If data_format is defined in FLAGS, overwrite automatically set value.
if flags.data_format is not None:
data_format = flags.data_format
# Log Info
print("-" * 64)
print("TEST INFO - EAGER")
print("-" * 64)
print("TF version:\t {}".format(tf.__version__))
print("Eager execution:\t {}".format(tf.executing_eagerly()))
print("Dataset:\t MNIST")
print("Model:\t CNN")
print('Device:\t {}'.format(device))
if data_format == 'channels_first':
print("Data format:\t NCHW (channel first)")
else:
print("Data format:\t NHWC (channel last)")
print("=" * 64)
# Load the datasets
train_ds = mnist_dataset.train(flags.data_dir).shuffle(60000).batch(
flags.batch_size)
test_ds = mnist_dataset.test(flags.data_dir).batch(flags.batch_size)
# Create the model and optimizer
# model = create_model(data_format)
model = MNISTModel(data_format)
optimizer = tf.train.MomentumOptimizer(flags.lr, flags.momentum)
# Create file writers for writing TensorBoard summaries.
if flags.output_dir:
# Create directories to which summaries will be written
# tensorboard --logdir=<output_dir>
# can then be used to see the recorded summaries.
train_dir = os.path.join(flags.output_dir, 'train')
test_dir = os.path.join(flags.output_dir, 'eval')
tf.gfile.MakeDirs(flags.output_dir)
else:
train_dir = None
test_dir = None
summary_writer = tf.contrib.summary.create_file_writer(train_dir,
flush_millis=10000)
test_summary_writer = tf.contrib.summary.create_file_writer(
test_dir, flush_millis=10000, name='test')
# Create and restore checkpoint (if one exists on the path)
checkpoint_prefix = os.path.join(flags.model_dir, 'ckpt')
step_counter = tf.train.get_or_create_global_step()
checkpoint = tfe.Checkpoint(model=model,
optimizer=optimizer,
step_counter=step_counter)
# Restore variables on creation if a checkpoint exists.
checkpoint.restore(tf.train.latest_checkpoint(flags.model_dir))
# Train and evaluate for a set number of epochs.
with tf.device(device):
for _ in range(flags.train_epochs):
start = time.time()
with summary_writer.as_default():
train(model, optimizer, train_ds, step_counter,
flags.log_interval)
end = time.time()
print('\nTrain time for epoch #%d (%d total steps): %f' %
(checkpoint.save_counter.numpy() + 1, step_counter.numpy(),
end - start))
with test_summary_writer.as_default():
test(model, test_ds)
checkpoint.save(checkpoint_prefix)
def eval_data():
data = dataset.test(FLAGS.data_dir)
data = data.cache()
data = data.batch(FLAGS.batch_size)
return data
def show_heatmap():
test_questions = list(questions_from_dataset(dataset.test()))
random.shuffle(test_questions)
para, question = test_questions[0]
show_html.show(generate_heatmap(n, para, question).encode('utf-8'))
def mnist(learning_rate, initializer_mode, num_conv_layers, num_fc_layers):
if num_conv_layers not in [1, 2]:
raise ValueError("num_conv_layers should be 1 or 2")
if num_fc_layers not in [1, 2]:
raise ValueError("num_fc_layers should be 1 or 2")
def make_hyperparameter_string(learning_rate, initializer_mode,
num_conv_layers, num_fc_layers):
hyperparameter = "lr_%.e_" % learning_rate
if initializer_mode == 0:
hyperparameter += "xavier_constant"
else:
hyperparameter += "truncated_normal_constant"
hyperparameter += "_%d_conv_%d_fc" % (num_conv_layers, num_fc_layers)
return hyperparameter
learning_rate = learning_rate
if initializer_mode == 0:
weights_initializer = tf.contrib.layers.xavier_initializer()
biases_initializer = tf.constant_initializer(0.1)
else:
weights_initializer = tf.truncated_normal_initializer(stddev=0.1)
biases_initializer = tf.constant_initializer(0.1)
logdir = "logs/mnist/" + make_hyperparameter_string(
learning_rate, initializer_mode, num_conv_layers, num_fc_layers)
if not tf.gfile.Exists(logdir):
tf.gfile.MakeDirs(logdir)
def mnist_net(x):
endpoints = {}
with slim.arg_scope([slim.conv2d, slim.fully_connected],
activation_fn=tf.nn.relu,
weights_initializer=weights_initializer,
biases_initializer=biases_initializer):
with slim.arg_scope([slim.conv2d, slim.max_pool2d],
stride=1,
padding="SAME"):
net = slim.conv2d(x, 32, [5, 5], scope="conv1")
net = slim.max_pool2d(net, [2, 2], stride=2, scope="pool1")
endpoints["block1"] = net
if num_conv_layers == 2:
net = slim.conv2d(net, 64, [5, 5], scope="conv2")
net = slim.max_pool2d(net, [2, 2], stride=2, scope="pool2")
endpoints["block2"] = net
net = tf.reshape(net, shape=[-1, 7 * 7 * 64])
elif num_conv_layers == 1:
net = tf.reshape(net, shape=[-1, 14 * 14 * 32])
if num_fc_layers == 1:
logits = slim.fully_connected(net, 10, scope="fc")
else:
logits = slim.stack(net,
slim.fully_connected, [1024, 10],
scope="fc")
endpoints["logits"] = logits
return logits, endpoints
# ValueError: Variable conv1/weights already exists, disallowed.
# Did you mean to set reuse=True or reuse=tf.AUTO_REUSE in VarScope? Originally defined at:
graph = tf.Graph()
with graph.as_default():
with tf.name_scope("input"):
images = tf.placeholder(tf.float32,
shape=[None, 784],
name="images")
images_3d = tf.reshape(images,
shape=[-1, 28, 28, 1],
name="images_3d")
labels = tf.placeholder(tf.uint8, shape=[None], name="labels")
onehot_labels = tf.one_hot(indices=labels,
depth=10,
name="onehot_labels")
logits, endpoints = mnist_net(images_3d)
with tf.name_scope("loss"):
# loss = slim.losses.softmax_cross_entropy(logits, onehot_labels)
loss = tf.losses.softmax_cross_entropy(onehot_labels=onehot_labels,
logits=logits)
# 要注释掉这一行，否则会在 tensorboard 中出现两次 softmax_cross_entropy_loss，应该是上面的一行已经加了一次了
# tf.losses.add_loss(loss) # Letting TF-Slim know about the additional loss.
total_loss = tf.losses.get_total_loss(
add_regularization_losses=False)
# tf.add_to_collection('EXTRA_LOSSES', total_loss)
with tf.name_scope("accuracy"):
correct_predictions = tf.equal(
labels, tf.cast(tf.argmax(logits, axis=1), tf.uint8))
accuracy = tf.reduce_mean(tf.cast(correct_predictions, tf.float32))
with tf.name_scope("optimize"):
optimizer = tf.train.AdamOptimizer(learning_rate)
# create_train_op ensures that each time we ask for the loss, the update_ops
# are run and the gradients being computed are applied too.
train_op = slim.learning.create_train_op(total_loss, optimizer)
# batch size 100 要比 30 好很多，也要稳很多
train_set = dataset.train("MNIST-data").cache().shuffle(
buffer_size=1000).batch(100).repeat(num_epoch)
test_set = dataset.test("MNIST-data").cache().batch(30).repeat()
iterator = train_set.make_one_shot_iterator()
one_element = iterator.get_next()
iterator_test = test_set.make_one_shot_iterator()
one_element_test = iterator_test.get_next()
init_op = tf.global_variables_initializer()
log_writer = tf.summary.FileWriter(logdir)
# summaries = set(tf.get_collection(tf.GraphKeys.SUMMARIES))
summaries = set()
for key in endpoints:
summaries.add(tf.summary.histogram("block/" + key, endpoints[key]))
for variable in slim.get_model_variables():
summaries.add(tf.summary.histogram(variable.op.name, variable))
for loss in tf.get_collection(tf.GraphKeys.LOSSES):
summaries.add(tf.summary.scalar(loss.op.name, loss))
# for loss in tf.get_collection('EXTRA_LOSSES'):
# summaries.add(tf.summary.scalar(loss.op.name, loss))
# summaries.add(tf.summary.scalar("accuracy", accuracy))
accuracy_train_summary_op = tf.summary.scalar("accuracy_train",
accuracy)
accuracy_test_summary_op = tf.summary.scalar("accuracy_test", accuracy)
summaries.add(tf.summary.image("image", images_3d, 4))
summary_op = tf.summary.merge(list(summaries), name='summary_op')
step = 0
with tf.Session() as sess:
log_writer.add_graph(sess.graph)
sess.run(init_op)
try:
while True:
images_, labels_ = sess.run(one_element)
sess.run(train_op,
feed_dict={
images: images_,
labels: labels_
})
if step % 10 == 0:
summary_, accuracy_train_summary = sess.run(
[summary_op, accuracy_train_summary_op],
feed_dict={
images: images_,
labels: labels_
})
images_, labels_ = sess.run(one_element_test)
accuracy_test_summary = sess.run(
accuracy_test_summary_op,
feed_dict={
images: images_,
labels: labels_
})
log_writer.add_summary(summary_, step)
log_writer.add_summary(accuracy_train_summary, step)
log_writer.add_summary(accuracy_test_summary, step)
step += 1
except tf.errors.OutOfRangeError:
print("Finished")
log_writer.close()
# data_dir
data_dir = "/tmp/mnist_convnet_model_data" + str(ddl.rank())
input_shape = ()
if K.image_data_format() == 'channels_first':
input_shape = (1, img_rows, img_cols)
else:
input_shape = (img_rows, img_cols, 1)
# the data, split between train and test sets
(train_set, num_of_train_imgs) = dataset.train(data_dir, input_shape)
train_set = train_set.shard(ddl.size(), ddl.rank())
train_set = train_set.cache().shuffle(
buffer_size=1000).batch(batch_size).repeat()
(eval_set, num_of_test_imgs) = dataset.test(data_dir, input_shape)
eval_full = eval_set
eval_set = eval_set.shard(ddl.size(), ddl.rank())
eval_set = eval_set.batch(batch_size).repeat()
num_of_all_test_imgs = num_of_test_imgs
num_of_train_imgs /= ddl.size()
num_of_test_imgs /= ddl.size()
model = Sequential()
model.add(
Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
def predict_input_fn(params):
batch_size = params["batch_size"]
data_dir = params["data_dir"]
# Take out top 10 samples from test data to make the predictions.
ds = dataset.test(data_dir).take(10).batch(batch_size)
return ds
def eval_input_fn():
return dataset.test(flags_obj.data_dir).batch(
flags_obj.batch_size).make_one_shot_iterator().get_next()
'''Data set of car conditions and whether a buyer would buy it
from http://archive.ics.uci.edu/ml/datasets/Car+Evaluation
'''
import dataset
car = dataset.fromFile('car')
r = car.tree()
if __name__ == '__main__':
r.prune(.1)
print(r)
res, pre, rec, har = r.stats()
print("\nresults: " + str(res))
print("precision: " + str(pre))
print("recall: " + str(rec))
print("harmonic mean " + str(har))
print("\nTESTING:\n")
r, stats = dataset.test(car, 50, .1)
print(r)
res, pre, rec, har = stats
print("\nresults: " + str(res))
print("precision: " + str(pre))
print("recall: " + str(rec))
print("harmonic mean " + str(har))
