def main(_):
fig_path = '.'
# 加载数据
(x_train, y_train), (_, _) = load_mnist(
path=os.path.join(config.PATH, config.DATA_PATH))
x_train = x_train.astype('float32') / 255.
x_train = tf.reshape(x_train, shape=(-1, 28, 28, 1))
y_train_ohe = tf.one_hot(y_train, depth=FLAGS.num_classes)
buffer_size = x_train.numpy().shape[0]
train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train_ohe))
train_dataset = train_dataset.shuffle(1000)
train_dataset = train_dataset.batch(FLAGS.batch_size, drop_remainder=False)
# 初始化模型
model = CNN(num_classes=FLAGS.num_classes)
lr_finder = LrFinder(model=model,
data_size=buffer_size,
batch_szie=FLAGS.batch_size,
fig_path=fig_path)
optimizer = tf.train.AdamOptimizer(learning_rate=lr_finder.learning_rate)
lr_finder.find(trainDatsset=train_dataset,
start_lr=0.00001,
end_lr=10,
optimizer=optimizer,
epochs=1)
def main(_):
log_path = os.path.join(config.PATH, config.LOG_PATH)
checkpoint_dir = os.path.join(config.PATH, config.CHECKPOINT_PATH)
# 加载数据
(x_train, y_train), (x_test, y_test) = load_mnist(
path=os.path.join(config.PATH, config.DATA_PATH))
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = tf.reshape(x_train, shape=(-1, 28, 28, 1))
x_test = tf.reshape(x_test, shape=(-1, 28, 28, 1))
y_train_ohe = tf.one_hot(y_train, depth=FLAGS.num_classes)
y_test_ohe = tf.one_hot(y_test, depth=FLAGS.num_classes)
buffer_size = x_train.numpy().shape[0]
n_batch = buffer_size // FLAGS.batch_size
resiud = buffer_size % FLAGS.batch_size
train_dataset = tf.data.Dataset.from_tensor_slices(
(x_train, y_train_ohe)).shuffle(1000)
train_dataset = train_dataset.batch(FLAGS.batch_size, drop_remainder=False)
#优化器
optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate)
# 初始化模型
model = InceptionNet(num_layers=2, num_classes=FLAGS.num_classes)
save_kwargs = {
'model': model,
'optimizer': optimizer,
'global_step': tf.train.get_or_create_global_step()
}
# 定义callbacks
write_summary = WriteSummary(log_path=log_path)
model_checkpoint = ModelCheckpoint(checkpoint_dir=checkpoint_dir,
mode='min',
monitor='val_loss',
save_best_only=False,
epoch_per=3,
**save_kwargs)
early_stop = EarlyStopping(mode='min',
patience=FLAGS.early_stopping_rounds)
progressbar = ProgressBar(data_size=buffer_size,
n_batch=n_batch,
batch_size=FLAGS.batch_size,
resiud=resiud,
eval_name='acc',
loss_name='loss')
# 开始训练模型
model.fit(trainDataset=train_dataset,
valDataset=[x_test, y_test_ohe],
epochs=FLAGS.epochs,
optimizer=optimizer,
ModelCheckPoint=model_checkpoint,
progressbar=progressbar,
write_summary=write_summary,
early_stopping=early_stop)
def main(_):
log_path = os.path.join(config.PATH, config.LOG_PATH)
checkpoint_dir = os.path.join(config.PATH, config.CHECKPOINT_PATH)
# 加载数据
(x_train, y_train), (x_test, y_test) = load_mnist(
path=os.path.join(config.PATH, config.DATA_PATH))
# cpu下好像不能使用tf.float64
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = tf.reshape(x_train, shape=(-1, 28, 28, 1))
x_test = tf.reshape(x_test, shape=(-1, 28, 28, 1))
y_train_ohe = tf.one_hot(y_train, depth=FLAGS.num_classes)
y_test_ohe = tf.one_hot(y_test, depth=FLAGS.num_classes)
buffer_size = x_train.numpy().shape[0]
n_batch = buffer_size // FLAGS.batch_size
resiud = buffer_size % FLAGS.batch_size
train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train_ohe))
if FLAGS.data_aug:
'''
好像dataset里面不太好实用numpy形式（调用cv2）进行数据曾强处理
'''
c_i_dataset = train_dataset.map(
lambda img, label: [central_scale_images(img, scale=0.65), label])
r_b_dataset = train_dataset.map(
lambda img, label: [random_brightness_image(img), label])
train_dataset = train_dataset.concatenate(r_b_dataset)
train_dataset = train_dataset.concatenate(c_i_dataset)
buffer_size = sum([1 for _ in train_dataset])
train_dataset = train_dataset.shuffle(1000)
train_dataset = train_dataset.batch(FLAGS.batch_size, drop_remainder=False)
#优化器
optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate)
# 初始化模型
model = CNN(num_classes=FLAGS.num_classes)
save_kwargs = {
'model': model,
'optimizer': optimizer,
'global_step': tf.train.get_or_create_global_step()
}
# 定义callbacks
write_summary = WriteSummary(log_path=log_path)
model_checkpoint = ModelCheckpoint(checkpoint_dir=checkpoint_dir,
mode='min',
monitor='val_loss',
save_best_only=True,
epoch_per=3,
**save_kwargs)
early_stop = EarlyStopping(mode='min',
patience=FLAGS.early_stopping_rounds)
progressbar = ProgressBar(data_size=buffer_size,
n_batch=n_batch,
batch_size=FLAGS.batch_size,
resiud=resiud,
eval_name='acc',
loss_name='loss')
# 开始训练模型
model.fit(trainDataset=train_dataset,
valDataset=[x_test, y_test_ohe],
epochs=FLAGS.epochs,
optimizer=optimizer,
ModelCheckPoint=model_checkpoint,
progressbar=progressbar,
write_summary=write_summary,
early_stopping=early_stop)