19行关键代码，带你轻松入门PaddlePaddle单机训练

pip install paddlepaddle

import paddle as paddleimport paddle.fluid as fluid

image = fluid.layers.data(name='image', shape=[1, 28, 28], dtype='float32') label = fluid.layers.data(name='label', shape=[1], dtype='int64') hidden = fluid.layers.fc(input=image, size=100, act='relu')prediction = fluid.layers.fc(input=hidden, size=10, act='softmax')

cost = fluid.layers.cross_entropy(input=prediction, label=label)avgcost = fluid.layers.mean(cost)acc = fluid.layers.accuracy(input=prediction, label=label)

optimizer = fluid.optimizer.AdamOptimizer(learning_rate=0.001)  opts = optimizer.minimize(avg_cost)

exe = fluid.Executor(fluid.CPUPlace())exe.run(fluid.default_startup_program())

train_reader = paddle.batch(paddle.dataset.mnist.train(), batch_size=128)feeder = fluid.DataFeeder(place=fluid.CPUPlace(), feed_list=[image, label])

for batch_id, data in enumerate(train_reader()):train_cost,train_acc = exe.run(program=fluid.default_main_program(), feed=feeder.feed(data), fetch_list=[avg_cost, acc]) 

Batch:0, Cost:3.17834, Accuracy:0.04688Batch:50, Cost:0.53703, Accuracy:0.82031Batch:100, Cost:0.47778, Accuracy:0.83594Batch:150, Cost:0.34092, Accuracy:0.90625Batch:200, Cost:0.25703, Accuracy:0.92188Batch:250, Cost:0.37539, Accuracy:0.86719Batch:300, Cost:0.24384, Accuracy:0.94531Batch:350, Cost:0.51838, Accuracy:0.84375Batch:400, Cost:0.40187, Accuracy:0.85938Batch:450, Cost:0.36615, Accuracy:0.89062

fluid.io.save_inference_model("model", ['image'], [prediction], exe)

import numpy as npimport pickle

load_file=open("save.bin","rb")img=pickle.load(load_file)act_label=pickle.load(load_file)load_file.close()

with fluid.scope_guard(fluid.core.Scope()):    [infer_prog,feed_names,targets]=                fluid.io.load_inference_model("model", exe)    results = exe.run(program=infer_prog, feed={feed_names[0]: img}, fetch_list=targets)

print("实际数字为: %d" % act_label)print("预测结果为: %d" % np.argsort(results)[0][0][-1])