def train_mnist_model():
with tf.Session(config=session_config) as sess:
mnist = create_inputs.read_data_sets(dataset_path, one_hot=True)
# 计算有多少批次
n_batch = mnist.train.num_examples // batch_size
# 构建网络
x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, class_num])
prediction, _ = model(x, input_shape, class_num)
# 求loss
loss = dk.cross_entropy_loss(prediction, y)
# 设置优化器
global_step, train_step = dk.set_optimizer(
lr_range, num_batches_per_epoch=n_batch, loss=loss)
# 求acc
accuracy = dk.get_acc(prediction, y)
# 初始化变量
coord, threads = dk.init_variables_and_start_thread(sess)
# 恢复model
saver, start_epoch = dk.restore_model(sess, ckpt, restore_model=False)
# 设置训练日志
summary_dict = {'loss': loss, 'accuracy': accuracy}
summary_writer, summary_op = dk.set_summary(sess, logdir, summary_dict)
# epoch=50
for epoch_th in range(epoch):
for bat in range(n_batch):
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
# 训练一个step
_, loss_value, acc_value, summary_str, step = sess.run(
[train_step, loss, accuracy, summary_op, global_step],
feed_dict={
x: batch_xs,
y: batch_ys
})
# 显示结果
dk.print_message(epoch_th, bat, n_batch, step, loss_value,
acc_value)
# 保存summary
if (step + 1) % 20 == 0:
summary_writer.add_summary(summary_str, step)
# 保存model
if (((epoch_th + 1) % 50)) == 0:
print('saving movdel.......')
saver.save(sess,
os.path.join(ckpt,
'model_{}.ckpt'.format(epoch_th)),
global_step=global_step)
dk.stop_threads(coord, threads)
def evaluate_result():
with tf.Session(config=session_config) as sess:
test_x, test_y = create_inputs(is_train)
# test_y = tf.one_hot(test_y, depth=class_num, axis=1, dtype=tf.int32)
# 构建网络
x = tf.placeholder(tf.float32,
shape=[
input_shape[0], input_shape[1], input_shape[2],
input_shape[3]
])
y = tf.placeholder(tf.float32, shape=[input_shape[0], class_num])
prediction, endpoint = model(x, input_shape, class_num)
# 打印模型结构
dk.print_model_struct(endpoint)
# 求acc
accuracy = dk.get_acc(prediction, y)
# 初始化变量
coord, threads = dk.init_variables_and_start_thread(sess)
# 恢复model
saver = dk.restore_model(sess, ckpt, restore_model=restore_model)
# 显示参数量
dk.show_parament_numbers()
start_epoch = 0
acc_list = []
if is_train:
print('train_number:', train_number)
n_batch_train = int(train_number // batch_size)
n_batch_total = n_batch_train
else:
print('test_number:', test_number)
n_batch_test = int(test_number // batch_size)
n_batch_total = n_batch_test
for epoch_n in range(start_epoch, epoch):
for n_batch in range(n_batch_total):
batch_x, batch_y = sess.run([test_x, test_y])
# 训练一个step
acc_value = sess.run(accuracy,
feed_dict={
x: batch_x,
y: batch_y
})
# 显示结果batch_size
print('epoch_n:', epoch_n, ' n_batch:', n_batch, ' acc_value:',
acc_value)
acc_list.append(acc_value)
result = np.mean(acc_list)
print('final result: ', result)
dk.stop_threads(coord, threads)
def train_cifar10_model():
with tf.Session(config=session_config) as sess:
#入口
train_x, train_y = create_inputs(is_train)
x = tf.placeholder(tf.float32, shape=input_shape)
y = tf.placeholder(tf.float32, shape=label_shape)
# 构建网络
prediction, _ = model(x, input_shape, class_num)
# 求loss
loss = dk.cross_entropy_loss(prediction, y)
# 设置优化器
global_step, train_step = dk.set_optimizer(
lr_range, num_batches_per_epoch=n_batch_train, loss=loss)
# 求acc
accuracy = dk.get_acc(prediction, y)
# 初始化变量
coord, threads = dk.init_variables_and_start_thread(sess)
# 设置训练日志
summary_dict = {'loss': loss, 'accuracy': accuracy}
summary_writer, summary_op = dk.set_summary(sess, logdir, summary_dict)
# 恢复model
saver, start_epoch = dk.restore_model(sess, ckpt, restore_model=False)
# 显示参数量
dk.show_parament_numbers()
start_epoch = 0
if restore_model:
step = sess.run(global_step)
start_epoch = int(
step / n_batch_train / save_epoch_n) * save_epoch_n
# 训练loop
total_step = n_batch_train * epoch
for epoch_n in range(epoch):
pre_index = 0
since = time.time()
acc_value_list = []
for n_batch in range(n_batch_train):
if pre_index + batch_size < 50000:
batch_x = train_x[pre_index:pre_index + batch_size]
batch_y = train_y[pre_index:pre_index + batch_size]
else:
batch_x = train_x[pre_index:]
batch_y = train_y[pre_index:]
# 训练一个step
_, loss_value, acc_value, summary_str, step = sess.run(
[train_step, loss, accuracy, summary_op, global_step],
feed_dict={
x: batch_x,
y: batch_y
})
# 显示结果
dk.print_message(epoch_n, n_batch, n_batch_train, step,
loss_value, acc_value)
# 保存summary
if (step + 1) % 20 == 0:
summary_writer.add_summary(summary_str, step)
pre_index += batch_size
# 保存结果
acc_value_list.append(acc_value)
# 显示进度、耗时、最小最大平均值
seconds_mean = (time.time() - since) / n_batch_train
dk.print_progress_and_time_massge(seconds_mean, step, total_step,
acc_value_list)
# 保存model
if (((epoch_n + 1) % save_epoch_n)) == 0:
print('epoch_n :{} saving movdel.......'.format(epoch_n))
saver.save(sess,
os.path.join(ckpt, 'model_{}.ckpt'.format(epoch_n)),
global_step=global_step)
dk.stop_threads(coord, threads)
n_batch_train = int(train_number // batch_size)
n_batch_test = int(test_number // batch_size)
session_config = dk.set_gpu()
with tf.Session(config=session_config) as sess:
test_x, test_y = create_inputs(is_train)
test_y = tf.one_hot(test_y, depth=class_num, axis=1, dtype=tf.float32)
# 构建网络
x = tf.placeholder(
tf.float32,
shape=[input_shape[0], input_shape[1], input_shape[2], input_shape[3]])
y = tf.placeholder(tf.float32, shape=[input_shape[0], class_num])
prediction = cnn_L4(x, input_shape, class_num, keep_prob=1)
# 求acc
accuracy = dk.get_acc(prediction, y)
# 初始化变量
coord, threads = dk.init_variables_and_start_thread(sess)
# 恢复model
saver = dk.restore_model(sess, ckpt, restore_model=restore_model)
# 显示参数量
dk.show_parament_numbers()
start_epoch = 0
acc_list = []
print('train_number:', train_number)
print('test_number:', test_number)
if is_train:
n_batch_total = n_batch_train
else:
n_batch_total = n_batch_test
for epoch_n in range(start_epoch, epoch):
