def train1():
print("train1........................................")
print("\t训练代码demo")
with fluid.dygraph.guard():
epoch_num = 5
BATCH_SIZE = 64
train_reader = paddle.batch(paddle.dataset.mnist.train(),
batch_size=32,
drop_last=True)
mnist = MNIST()
adam = fluid.optimizer.AdamOptimizer(learning_rate=0.001,
parameter_list=mnist.parameters())
for epoch in range(epoch_num):
for batch_id, data in enumerate(train_reader()):
dy_x_data = np.array([x[0].reshape(1, 28, 28)
for x in data]).astype('float32')
y_data = np.array([x[1] for x in data
]).astype('int64').reshape(-1, 1)
img = fluid.dygraph.to_variable(dy_x_data)
label = fluid.dygraph.to_variable(y_data)
cost = mnist(img)
loss = fluid.layers.cross_entropy(cost, label)
avg_loss = fluid.layers.mean(loss)
if batch_id % 100 == 0 and batch_id is not 0:
print("epoch: {}, batch_id: {}, loss is: {}".format(
epoch, batch_id, avg_loss.numpy()))
avg_loss.backward()
adam.minimize(avg_loss)
mnist.clear_gradients()
def train4(use_cudnn, model_file):
with fluid.dygraph.guard():
epoch_num = 5
BATCH_SIZE = 64
mnist = MNIST(use_cudnn=use_cudnn)
adam = fluid.optimizer.Adam(learning_rate=0.001,
parameter_list=mnist.parameters())
train_reader = paddle.batch(paddle.dataset.mnist.train(),
batch_size=BATCH_SIZE,
drop_last=True)
test_reader = paddle.batch(paddle.dataset.mnist.test(),
batch_size=BATCH_SIZE,
drop_last=True)
np.set_printoptions(precision=3, suppress=True)
dy_param_init_value = {}
start_time = time.time()
for epoch in range(epoch_num):
for batch_id, data in enumerate(train_reader()):
# step 1 : 处理输入
dy_x_data = np.array([x[0].reshape(1, 28, 28)
for x in data]).astype('float32')
y_data = np.array([x[1] for x in data
]).astype('int64').reshape(BATCH_SIZE, 1)
img = fluid.dygraph.to_variable(dy_x_data)
label = fluid.dygraph.to_variable(y_data)
label.stop_gradient = True
# step 2 : 前向传播&&损失函数
cost = mnist(img)
loss = fluid.layers.cross_entropy(cost, label)
avg_loss = fluid.layers.mean(loss)
dy_out = avg_loss.numpy()
# step 3 : 反向传播&&最优化
avg_loss.backward()
adam.minimize(avg_loss)
mnist.clear_gradients()
# step 4 : 测试模型
if batch_id % 100 == 0 and batch_id is not 0:
mnist.eval()
test_cost, test_acc = test(test_reader, mnist, BATCH_SIZE)
mnist.train()
print(
"epoch {}, batch_id {}, train loss is {}, test cost is {}, test acc is {}"
.format(epoch, batch_id, avg_loss.numpy(), test_cost,
test_acc))
fluid.dygraph.save_dygraph(mnist.state_dict(), model_file)
end_time = time.time()
print("training model has finished! time=%.2fs" %
(end_time - start_time))
def train3(use_cudnn, model_file):
print("train3........................................")
print("\t多卡训练（paddle有bug，没有调试通）")
place = fluid.CUDAPlace(fluid.dygraph.parallel.Env().dev_id)
with fluid.dygraph.guard(place):
strategy = fluid.dygraph.parallel.prepare_context()
epoch_num = 5
BATCH_SIZE = 64
mnist = MNIST(use_cudnn=use_cudnn)
adam = fluid.optimizer.AdamOptimizer(learning_rate=0.001,
parameter_list=mnist.parameters())
mnist = fluid.dygraph.parallel.DataParallel(mnist, strategy)
train_reader = paddle.batch(paddle.dataset.mnist.train(),
batch_size=BATCH_SIZE,
drop_last=True)
train_reader = fluid.contrib.reader.distributed_batch_reader(
train_reader)
start_time = time.time()
for epoch in range(epoch_num):
for batch_id, data in enumerate(train_reader()):
dy_x_data = np.array([x[0].reshape(1, 28, 28)
for x in data]).astype('float32')
y_data = np.array([x[1] for x in data
]).astype('int64').reshape(-1, 1)
img = fluid.dygraph.to_variable(dy_x_data)
label = fluid.dygraph.to_variable(y_data)
label.stop_gradient = True
cost, acc = mnist(img, label)
loss = fluid.layers.cross_entropy(cost, label)
avg_loss = fluid.layers.mean(loss)
avg_loss = mnist.scale_loss(avg_loss)
avg_loss.backward()
mnist.apply_collective_grads()
adam.minimize(avg_loss)
mnist.clear_gradients()
if batch_id % 100 == 0 and batch_id is not 0:
print("epoch: {}, batch_id: {}, loss is: {}".format(
epoch, batch_id, avg_loss.numpy()))
fluid.dygraph.save_dygraph(mnist.state_dict(), model_file)
end_time = time.time()
print("training model has finished! time=%.2fs" %
(end_time - start_time))
def train2():
print("train2........................................")
print("\t获取神经网络中的参数")
with fluid.dygraph.guard():
epoch_num = 5
BATCH_SIZE = 64
mnist = MNIST()
adam = fluid.optimizer.AdamOptimizer(learning_rate=0.0001,
parameter_list=mnist.parameters())
train_reader = paddle.batch(paddle.dataset.mnist.train(),
batch_size=BATCH_SIZE,
drop_last=True)
np.set_printoptions(precision=3, suppress=True)
for epoch in range(epoch_num):
for batch_id, data in enumerate(train_reader()):
dy_x_data = np.array([x[0].reshape(1, 28, 28)
for x in data]).astype("float32")
y_data = np.array([x[1] for x in data
]).astype("int64").reshape(BATCH_SIZE, 1)
img = fluid.dygraph.to_variable(dy_x_data)
label = fluid.dygraph.to_variable(y_data)
label.stop_gradient = True
cost = mnist(img)
loss = fluid.layers.cross_entropy(cost, label)
avg_loss = fluid.layers.mean(loss)
dy_out = avg_loss.numpy()
avg_loss.backward()
adam.minimize(avg_loss)
mnist.clear_gradients()
dy_param_value = {}
for param in mnist.parameters():
print("%s = %s" % (param.name, str(param.numpy())))
dy_param_value[param.name] = param.numpy()
if batch_id % 20 == 0:
print("loss at step {}: {}".format(batch_id,
avg_loss.numpy()))
break
break
print("Final loss : {}".format(avg_loss.numpy()))
