Blame: example/module/python_loss.py - apache/mxnet

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Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

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Add license header (#7379) * add * add .py and ci * fix pylint * update 2017-08-08 16:36:23 -07:00			`# Licensed to the Apache Software Foundation (ASF) under one`
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			`#`
			`# http://www.apache.org/licenses/LICENSE-2.0`
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			`# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY`
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fix lint errors 2016-04-15 16:12:18 -04:00			`# pylint: skip-file`
add an example to define loss function in Python 2016-04-15 16:08:19 -04:00			`import numpy as np`
			`import mxnet as mx`
			`import numba`
			`import logging`

test python loss on gpu 2016-04-15 16:17:09 -04:00			`# We use numba.jit to implement the loss gradient.`
add an example to define loss function in Python 2016-04-15 16:08:19 -04:00			`@numba.jit`
			`def mc_hinge_grad(scores, labels):`
fix lint error for dangerous default value (+3 squashed commits) Squashed commits: [960414a] simplify the user interface for defining python loss module [dbb797e] test python loss on gpu [f5d94a8] fix lint errors (+1 squashed commit) Squashed commits: [5d7c7ea] add an example to define loss function in Python 2016-04-16 00:13:30 -04:00			`scores = scores.asnumpy()`
Fix example/module folder and remove duplicate examples (#8964) * Fix example/module folder and remove duplicate examples * Add readme * remove relative path insert 2018-01-05 12:00:57 -08:00			`labels = labels.asnumpy().astype(int)`
fix lint error for dangerous default value (+3 squashed commits) Squashed commits: [960414a] simplify the user interface for defining python loss module [dbb797e] test python loss on gpu [f5d94a8] fix lint errors (+1 squashed commit) Squashed commits: [5d7c7ea] add an example to define loss function in Python 2016-04-16 00:13:30 -04:00
add an example to define loss function in Python 2016-04-15 16:08:19 -04:00			`n, _ = scores.shape`
			`grad = np.zeros_like(scores)`

			`for i in range(n):`
			`score = 1 + scores[i] - scores[i, labels[i]]`
			`score[labels[i]] = 0`
			`ind_pred = score.argmax()`
			`grad[i, labels[i]] -= 1`
			`grad[i, ind_pred] += 1`

			`return grad`

			`if __name__ == '__main__':`
test python loss on gpu 2016-04-15 16:17:09 -04:00			`n_epoch = 10`
add an example to define loss function in Python 2016-04-15 16:08:19 -04:00			`batch_size = 100`
test python loss on gpu 2016-04-15 16:17:09 -04:00			`num_gpu = 2`
			`contexts = mx.context.cpu() if num_gpu < 1 else [mx.context.gpu(i) for i in range(num_gpu)]`
add an example to define loss function in Python 2016-04-15 16:08:19 -04:00
			`# build a MLP module`
			`data = mx.symbol.Variable('data')`
			`fc1 = mx.symbol.FullyConnected(data, name='fc1', num_hidden=128)`
			`act1 = mx.symbol.Activation(fc1, name='relu1', act_type="relu")`
			`fc2 = mx.symbol.FullyConnected(act1, name = 'fc2', num_hidden = 64)`
			`act2 = mx.symbol.Activation(fc2, name='relu2', act_type="relu")`
			`fc3 = mx.symbol.FullyConnected(act2, name='fc3', num_hidden=10)`

			`mlp = mx.mod.Module(fc3, context=contexts)`
fix lint error for dangerous default value (+3 squashed commits) Squashed commits: [960414a] simplify the user interface for defining python loss module [dbb797e] test python loss on gpu [f5d94a8] fix lint errors (+1 squashed commit) Squashed commits: [5d7c7ea] add an example to define loss function in Python 2016-04-16 00:13:30 -04:00			`loss = mx.mod.PythonLossModule(grad_func=mc_hinge_grad)`
add an example to define loss function in Python 2016-04-15 16:08:19 -04:00
fix lint error for dangerous default value (+3 squashed commits) Squashed commits: [960414a] simplify the user interface for defining python loss module [dbb797e] test python loss on gpu [f5d94a8] fix lint errors (+1 squashed commit) Squashed commits: [5d7c7ea] add an example to define loss function in Python 2016-04-16 00:13:30 -04:00			`mod = mx.mod.SequentialModule() \`
			`.add(mlp) \`
			`.add(loss, take_labels=True, auto_wiring=True)`
add an example to define loss function in Python 2016-04-15 16:08:19 -04:00
			`train_dataiter = mx.io.MNISTIter(`
			`image="data/train-images-idx3-ubyte",`
			`label="data/train-labels-idx1-ubyte",`
			`data_shape=(784,),`
			`batch_size=batch_size, shuffle=True, flat=True, silent=False, seed=10)`
			`val_dataiter = mx.io.MNISTIter(`
			`image="data/t10k-images-idx3-ubyte",`
			`label="data/t10k-labels-idx1-ubyte",`
			`data_shape=(784,),`
			`batch_size=batch_size, shuffle=True, flat=True, silent=False)`

			`logging.basicConfig(level=logging.DEBUG)`
			`mod.fit(train_dataiter, eval_data=val_dataiter,`
			`optimizer_params={'learning_rate':0.01, 'momentum': 0.9},`
			`num_epoch=n_epoch)`