提交 8278d97e 编写于 作者: Q qijun

add book02.recognize_digits mlp train test

上级 37bfd03f
...@@ -21,7 +21,7 @@ class TestCrossEntropyOp1(OpTest): ...@@ -21,7 +21,7 @@ class TestCrossEntropyOp1(OpTest):
self.inputs = {"X": X, "Label": label} self.inputs = {"X": X, "Label": label}
self.outputs = {"Y": cross_entropy} self.outputs = {"Y": cross_entropy}
self.attrs = {"softLabel": False} self.attrs = {"soft_label": False}
def test_check_output(self): def test_check_output(self):
self.check_output() self.check_output()
......
import paddle.v2 as paddle
import paddle.v2.framework.layers as layers
import paddle.v2.framework.core as core
import paddle.v2.framework.optimizer as optimizer
from paddle.v2.framework.framework import Program, g_program
from paddle.v2.framework.executor import Executor
import numpy as np
init_program = Program()
program = Program()
image = layers.data(
name='x',
shape=[784],
data_type='float32',
program=program,
init_program=init_program)
hidden1 = layers.fc(input=image,
size=128,
act='relu',
program=program,
init_program=init_program)
hidden2 = layers.fc(input=hidden1,
size=64,
act='relu',
program=program,
init_program=init_program)
predict = layers.fc(input=hidden2,
size=10,
act='softmax',
program=program,
init_program=init_program)
label = layers.data(
name='y',
shape=[1],
data_type='int32',
program=program,
init_program=init_program)
cost = layers.cross_entropy(
input=predict, label=label, program=program, init_program=init_program)
avg_cost = layers.mean(x=cost, program=program, init_program=init_program)
sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.001)
opts = sgd_optimizer.minimize(avg_cost)
BATCH_SIZE = 128
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=8192),
batch_size=BATCH_SIZE)
place = core.CPUPlace()
exe = Executor(place)
exe.run(init_program, feed={}, fetch_list=[])
PASS_NUM = 100
for pass_id in range(PASS_NUM):
for data in train_reader():
x_data = np.array(map(lambda x: x[0], data)).astype("float32")
y_data = np.array(map(lambda x: x[1], data)).astype("int32")
y_data = np.expand_dims(y_data, axis=1)
tensor_x = core.LoDTensor()
tensor_x.set(x_data, place)
tensor_y = core.LoDTensor()
tensor_y.set(y_data, place)
outs = exe.run(program,
feed={'x': tensor_x,
'y': tensor_y},
fetch_list=[avg_cost])
out = np.array(outs[0])
if out[0] < 5.0:
exit(0) # if avg cost less than 5.0, we think our code is good.
exit(1)
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