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| 2번째 줄: |
2번째 줄: |
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| | 뭘 이렇게들 만들어 대는지. [https://www.tensorflow.org tf]가 맘에 안들기는 하지만. | | 뭘 이렇게들 만들어 대는지. [https://www.tensorflow.org tf]가 맘에 안들기는 하지만. |
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| − | ==Training and Inference==
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| − |
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| − | ===Linear Regression===
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| − | [http://mxnet.io/tutorials/python/linear-regression.html#linear-regression 원문]
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| − | <br>전체 소스
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| − | <pre>import mxnet as mx
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| − | import numpy as np
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| − |
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| − | #Training data
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| − | train_data = np.random.uniform(0, 1, [100, 2])
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| − | train_label = np.array([train_data[i][0] + 2 * train_data[i][1] for i in range(100)])
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| − | batch_size = 1
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| − |
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| − | #Evaluation Data
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| − | eval_data = np.array([[7,2],[6,10],[12,2]])
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| − | eval_label = np.array([11,26,16])
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| − |
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| − | train_iter = mx.io.NDArrayIter(train_data,train_label, batch_size, shuffle=True,label_name='lin_reg_label') #(1)
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| − | eval_iter = mx.io.NDArrayIter(eval_data, eval_label, batch_size, shuffle=False)
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| − |
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| − | X = mx.sym.Variable('data')
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| − | Y = mx.symbol.Variable('lin_reg_label') #(2)
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| − | fully_connected_layer = mx.sym.FullyConnected(data=X, name='fc1', num_hidden = 1)
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| − | lro = mx.sym.LinearRegressionOutput(data=fully_connected_layer, label=Y, name="lro") #(3)
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| − |
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| − | model = mx.mod.Module(
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| − | symbol = lro ,
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| − | data_names=['data'],
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| − | label_names = ['lin_reg_label']# network structure (4)
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| − | )
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| − |
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| − | mx.viz.plot_network(symbol=lro)
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| − | # (5)
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| − | model.fit(train_iter, eval_iter,
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| − | optimizer_params={'learning_rate':0.005, 'momentum': 0.9},
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| − | num_epoch=1000,
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| − | batch_end_callback = mx.callback.Speedometer(batch_size, 2))
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| − |
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| − | model.predict(eval_iter).asnumpy()
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| − |
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| − | metric = mx.metric.MSE()
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| − | model.score(eval_iter, metric)
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| − |
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| − | eval_data = np.array([[7,2],[6,10],[12,2]])
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| − | eval_label = np.array([11.1,26.1,16.1]) #Adding 0.1 to each of the values
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| − | eval_iter = mx.io.NDArrayIter(eval_data, eval_label, batch_size, shuffle=False)
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| − | model.score(eval_iter, metric)</pre>
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| − |
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| − | (3) {{c| mx.sym.LinearRegressionOutput}}은 l2 loss계산함. <br>
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| − | <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>
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| − | (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.
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| − | *<code>Speedometer(batch_size, frequent=50, auto_reset=True)</code> : 배치 50번마다 로깅하고, 로깅 후 reset할것. 아래를 미리 해줘야 stdout에 보인다.
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| − | <pre>import logging
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| − | logging.getLogger().setLevel(logging.DEBUG)</pre>
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| − | 실행해보면,
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| − | <pre>>>> # Print training speed and evaluation metrics every ten batches. Batch size is one.
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| − | >>> module.fit(iterator, num_epoch=n_epoch,
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| − | ... batch_end_callback=mx.callback.Speedometer(1, 10))
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| − | Epoch[0] Batch [10] Speed: 1910.41 samples/sec Train-accuracy=0.200000
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| − | Epoch[0] Batch [20] Speed: 1764.83 samples/sec Train-accuracy=0.400000
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| − | Epoch[0] Batch [30] Speed: 1740.59 samples/sec Train-accuracy=0.500000</pre>
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| − |
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| − | ===Handwritten Digit Recognition===
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| − | [http://mxnet.io/tutorials/python/mnist.html#handwritten-digit-recognition 원문]
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| − | <div class="toccolours mw-collapsible mw-collapsed">
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| − | ====full src====
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| − | <div class=mw-collapsible-content>
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| − | <pre>
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| − | import mxnet as mx
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| − | mnist = mx.test_utils.get_mnist()
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| − |
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| − | batch_size = 100
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| − | train_iter = mx.io.NDArrayIter(mnist['train_data'], mnist['train_label'], batch_size, shuffle=True)
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| − | val_iter = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], batch_size)
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| − |
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| − | data = mx.sym.var('data')
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| − | # Flatten the data from 4-D shape into 2-D (batch_size, num_channel*width*height)
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| − | data = mx.sym.flatten(data=data)
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| − |
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| − | # The first fully-connected layer and the corresponding activation function
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| − | fc1 = mx.sym.FullyConnected(data=data, num_hidden=128)
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| − | act1 = mx.sym.Activation(data=fc1, act_type="relu")
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| − |
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| − | # The second fully-connected layer and the corresponding activation function
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| − | fc2 = mx.sym.FullyConnected(data=act1, num_hidden = 64)
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| − | act2 = mx.sym.Activation(data=fc2, act_type="relu")
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| − |
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| − | # MNIST has 10 classes
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| − | fc3 = mx.sym.FullyConnected(data=act2, num_hidden=10)
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| − | # Softmax with cross entropy loss
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| − | mlp = mx.sym.SoftmaxOutput(data=fc3, name='softmax')
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| − |
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| − | import logging
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| − | logging.getLogger().setLevel(logging.DEBUG) # logging to stdout
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| − | # create a trainable module on CPU
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| − | mlp_model = mx.mod.Module(symbol=mlp, context=mx.cpu())
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| − | mlp_model.fit(train_iter, # train data
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| − | eval_data=val_iter, # validation data
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| − | optimizer='sgd', # use SGD to train
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| − | optimizer_params={'learning_rate':0.1}, # use fixed learning rate
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| − | eval_metric='acc', # report accuracy during training
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| − | batch_end_callback = mx.callback.Speedometer(batch_size, 100), # output progress for each 100 data batches
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| − | num_epoch=10) # train for at most 10 dataset passes
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| − |
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| − | test_iter = mx.io.NDArrayIter(mnist['test_data'], None, batch_size)
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| − | prob = mlp_model.predict(test_iter)
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| − | assert prob.shape == (10000, 10)
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| − |
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| − | test_iter = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], batch_size)
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| − | # predict accuracy of mlp
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| − | acc = mx.metric.Accuracy()
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| − | mlp_model.score(test_iter, acc)
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| − | print(acc)
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| − | assert acc.get()[1] > 0.96
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| − |
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| − | data = mx.sym.var('data')
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| − | # first conv layer
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| − | conv1 = mx.sym.Convolution(data=data, kernel=(5,5), num_filter=20)
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| − | tanh1 = mx.sym.Activation(data=conv1, act_type="tanh")
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| − | pool1 = mx.sym.Pooling(data=tanh1, pool_type="max", kernel=(2,2), stride=(2,2))
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| − | # second conv layer
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| − | conv2 = mx.sym.Convolution(data=pool1, kernel=(5,5), num_filter=50)
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| − | tanh2 = mx.sym.Activation(data=conv2, act_type="tanh")
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| − | pool2 = mx.sym.Pooling(data=tanh2, pool_type="max", kernel=(2,2), stride=(2,2))
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| − | # first fullc layer
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| − | flatten = mx.sym.flatten(data=pool2)
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| − | fc1 = mx.symbol.FullyConnected(data=flatten, num_hidden=500)
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| − | tanh3 = mx.sym.Activation(data=fc1, act_type="tanh")
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| − | # second fullc
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| − | fc2 = mx.sym.FullyConnected(data=tanh3, num_hidden=10)
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| − | # softmax loss
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| − | lenet = mx.sym.SoftmaxOutput(data=fc2, name='softmax')
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| − |
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| − | # create a trainable module on GPU 0
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| − | lenet_model = mx.mod.Module(symbol=lenet, context=mx.cpu())
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| − | # train with the same
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| − | lenet_model.fit(train_iter,
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| − | eval_data=val_iter,
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| − | optimizer='sgd',
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| − | optimizer_params={'learning_rate':0.1},
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| − | eval_metric='acc',
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| − | batch_end_callback = mx.callback.Speedometer(batch_size, 100),
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| − | num_epoch=10)
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| − |
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| − | test_iter = mx.io.NDArrayIter(mnist['test_data'], None, batch_size)
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| − | prob = lenet_model.predict(test_iter)
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| − | test_iter = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], batch_size)
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| − | # predict accuracy for lenet
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| − | acc = mx.metric.Accuracy()
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| − | lenet_model.score(test_iter, acc)
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| − | print(acc)
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| − | assert acc.get()[1] > 0.98
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| − | </pre></div></div>
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| − |
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| − | ====Loading data====
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| − | <pre>
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| − | mx.test_utils.get_mnist()
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| − | </pre>
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| − | 이 뒤로는 trivial.
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| − |
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| − | ===Predict with pre-trained models===
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| − |
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| − | ===Large Scale Image Classification===
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