Admin: 새 문서: ===Linear Regression=== [http://mxnet.io/tutorials/python/linear-regression.html#linear-regression 원문]
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import mxnet as mx import numpy as np  #Training data...

2017-07-04T09:34:01Z

새 문서: ===Linear Regression=== [http://mxnet.io/tutorials/python/linear-regression.html#linear-regression 원문] <br>전체 소스 <pre>import mxnet as mx import numpy as np #Training data...

새 문서

===Linear Regression===
[http://mxnet.io/tutorials/python/linear-regression.html#linear-regression 원문]
<br>전체 소스
<pre>import mxnet as mx
import numpy as np

#Training data
train_data = np.random.uniform(0, 1, [100, 2])
train_label = np.array([train_data[i][0] + 2 * train_data[i][1] for i in range(100)])
batch_size = 1

#Evaluation Data
eval_data = np.array([[7,2],[6,10],[12,2]])
eval_label = np.array([11,26,16])

train_iter = mx.io.NDArrayIter(train_data,train_label, batch_size, shuffle=True,label_name='lin_reg_label') #(1)
eval_iter = mx.io.NDArrayIter(eval_data, eval_label, batch_size, shuffle=False)

X = mx.sym.Variable('data')
Y = mx.symbol.Variable('lin_reg_label') #(2)
fully_connected_layer = mx.sym.FullyConnected(data=X, name='fc1', num_hidden = 1)
lro = mx.sym.LinearRegressionOutput(data=fully_connected_layer, label=Y, name="lro") #(3)

model = mx.mod.Module(
symbol = lro ,
data_names=['data'],
label_names = ['lin_reg_label']# network structure (4)
)

mx.viz.plot_network(symbol=lro)
# (5)
model.fit(train_iter, eval_iter,
optimizer_params={'learning_rate':0.005, 'momentum': 0.9},
num_epoch=1000,
batch_end_callback = mx.callback.Speedometer(batch_size, 2))

model.predict(eval_iter).asnumpy()

metric = mx.metric.MSE()
model.score(eval_iter, metric)

eval_data = np.array([[7,2],[6,10],[12,2]])
eval_label = np.array([11.1,26.1,16.1]) #Adding 0.1 to each of the values
eval_iter = mx.io.NDArrayIter(eval_data, eval_label, batch_size, shuffle=False)
model.score(eval_iter, metric)</pre>

(3) {{c| mx.sym.LinearRegressionOutput}}은 l2 loss계산함. <br>
<i>(1),(2)에서 {{c|lin_reg_label}}이라고 준 것과 {{c|NDArrayIter}}에서 이름이 일치해야 한다.</i> <code>train_iter = mx.io.NDArrayIter(..., label_name='<span style="color:red">lin_reg_label</span>' ) </code> <i> 다시말해, 입력단의 이름이 일치해야 한다는 얘기. 결국 (4)에 나오는 이름까지 일치해야 해서 같은 이름이 세번 나온다</i>

(5)의 {{c|model.fit}}에서 {{c|batch_end_callback}}으로 <code>[http://mxnet.io/api/python/callback.html?highlight=ck.speedometer#mxnet.callback.Speedometer mx.callback.Speedometer]</code>줄 수 있다.
*<code>Speedometer(batch_size, frequent=50, auto_reset=True)</code> : 배치 50번마다 로깅하고, 로깅 후 reset할것. 아래를 미리 해줘야 stdout에 보인다.
<pre>import logging
logging.getLogger().setLevel(logging.DEBUG)</pre>
실행해보면,
<pre>>>> # Print training speed and evaluation metrics every ten batches. Batch size is one.
>>> module.fit(iterator, num_epoch=n_epoch,
... batch_end_callback=mx.callback.Speedometer(1, 10))
Epoch[0] Batch [10] Speed: 1910.41 samples/sec Train-accuracy=0.200000
Epoch[0] Batch [20] Speed: 1764.83 samples/sec Train-accuracy=0.400000
Epoch[0] Batch [30] Speed: 1740.59 samples/sec Train-accuracy=0.500000</pre>

===Handwritten Digit Recognition===
[http://mxnet.io/tutorials/python/mnist.html#handwritten-digit-recognition 원문]
<div class="toccolours mw-collapsible mw-collapsed">
====full src====
<div class=mw-collapsible-content>
<pre>
import mxnet as mx
mnist = mx.test_utils.get_mnist()

batch_size = 100
train_iter = mx.io.NDArrayIter(mnist['train_data'], mnist['train_label'], batch_size, shuffle=True)
val_iter = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], batch_size)

data = mx.sym.var('data')
# Flatten the data from 4-D shape into 2-D (batch_size, num_channel*width*height)
data = mx.sym.flatten(data=data)

# The first fully-connected layer and the corresponding activation function
fc1 = mx.sym.FullyConnected(data=data, num_hidden=128)
act1 = mx.sym.Activation(data=fc1, act_type="relu")

# The second fully-connected layer and the corresponding activation function
fc2 = mx.sym.FullyConnected(data=act1, num_hidden = 64)
act2 = mx.sym.Activation(data=fc2, act_type="relu")

# MNIST has 10 classes
fc3 = mx.sym.FullyConnected(data=act2, num_hidden=10)
# Softmax with cross entropy loss
mlp = mx.sym.SoftmaxOutput(data=fc3, name='softmax')

import logging
logging.getLogger().setLevel(logging.DEBUG) # logging to stdout
# create a trainable module on CPU
mlp_model = mx.mod.Module(symbol=mlp, context=mx.cpu())
mlp_model.fit(train_iter, # train data
eval_data=val_iter, # validation data
optimizer='sgd', # use SGD to train
optimizer_params={'learning_rate':0.1}, # use fixed learning rate
eval_metric='acc', # report accuracy during training
batch_end_callback = mx.callback.Speedometer(batch_size, 100), # output progress for each 100 data batches
num_epoch=10) # train for at most 10 dataset passes

test_iter = mx.io.NDArrayIter(mnist['test_data'], None, batch_size)
prob = mlp_model.predict(test_iter)
assert prob.shape == (10000, 10)

test_iter = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], batch_size)
# predict accuracy of mlp
acc = mx.metric.Accuracy()
mlp_model.score(test_iter, acc)
print(acc)
assert acc.get()[1] > 0.96

data = mx.sym.var('data')
# first conv layer
conv1 = mx.sym.Convolution(data=data, kernel=(5,5), num_filter=20)
tanh1 = mx.sym.Activation(data=conv1, act_type="tanh")
pool1 = mx.sym.Pooling(data=tanh1, pool_type="max", kernel=(2,2), stride=(2,2))
# second conv layer
conv2 = mx.sym.Convolution(data=pool1, kernel=(5,5), num_filter=50)
tanh2 = mx.sym.Activation(data=conv2, act_type="tanh")
pool2 = mx.sym.Pooling(data=tanh2, pool_type="max", kernel=(2,2), stride=(2,2))
# first fullc layer
flatten = mx.sym.flatten(data=pool2)
fc1 = mx.symbol.FullyConnected(data=flatten, num_hidden=500)
tanh3 = mx.sym.Activation(data=fc1, act_type="tanh")
# second fullc
fc2 = mx.sym.FullyConnected(data=tanh3, num_hidden=10)
# softmax loss
lenet = mx.sym.SoftmaxOutput(data=fc2, name='softmax')

# create a trainable module on GPU 0
lenet_model = mx.mod.Module(symbol=lenet, context=mx.cpu())
# train with the same
lenet_model.fit(train_iter,
eval_data=val_iter,
optimizer='sgd',
optimizer_params={'learning_rate':0.1},
eval_metric='acc',
batch_end_callback = mx.callback.Speedometer(batch_size, 100),
num_epoch=10)

test_iter = mx.io.NDArrayIter(mnist['test_data'], None, batch_size)
prob = lenet_model.predict(test_iter)
test_iter = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], batch_size)
# predict accuracy for lenet
acc = mx.metric.Accuracy()
lenet_model.score(test_iter, acc)
print(acc)
assert acc.get()[1] > 0.98
</pre></div></div>

====Loading data====
<pre>
mx.test_utils.get_mnist()
</pre>
이 뒤로는 trivial.

===Predict with pre-trained models===

===Large Scale Image Classification===

Mxnet/Training and Inference - 편집 역사

Admin: 새 문서: ===Linear Regression=== [http://mxnet.io/tutorials/python/linear-regression.html#linear-regression 원문]
전체 소스
import mxnet as mx import numpy as np #Training data...

Mxnet/Training and Inference - 편집 역사

Admin: 새 문서: ===Linear Regression=== [http://mxnet.io/tutorials/python/linear-regression.html#linear-regression 원문] 전체 소스 import mxnet as mx import numpy as np #Training data...

Admin: 새 문서: ===Linear Regression=== [http://mxnet.io/tutorials/python/linear-regression.html#linear-regression 원문]
전체 소스
import mxnet as mx import numpy as np #Training data...