Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into my_unpool_max_2d
sweetsky0901
8 years ago
commit
822f28343b
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import paddle.v2.fluid.layers as layers
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from paddle.v2.fluid.framework import Program
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from paddle.v2.fluid.executor import Executor
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from paddle.v2.fluid.optimizer import MomentumOptimizer
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import paddle.v2.fluid.core as core
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import paddle.v2 as paddle
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import unittest
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import numpy as np
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class TestMNISTIfElseOp(unittest.TestCase):
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def test_raw_api(self):
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kwargs = {'startup_program': Program(), 'main_program': Program()}
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image = layers.data(
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name='x', shape=[784], data_type='float32', **kwargs)
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label = layers.data(name='y', shape=[1], data_type='int64', **kwargs)
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limit = layers.fill_constant_batch_size_like(
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input=label, dtype='int64', shape=[1], value=5.0, **kwargs)
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cond = layers.less_than(x=label, y=limit, **kwargs)
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true_image, false_image = layers.split_lod_tensor(
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input=image, mask=cond, **kwargs)
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true_out = layers.create_tensor(dtype='float32', **kwargs)
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true_cond = layers.ConditionalBlock([true_image], **kwargs)
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with true_cond.block():
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hidden = layers.fc(input=true_image, size=100, act='tanh', **kwargs)
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prob = layers.fc(input=hidden, size=10, act='softmax', **kwargs)
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layers.assign(input=prob, output=true_out, **kwargs)
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false_out = layers.create_tensor(dtype='float32', **kwargs)
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false_cond = layers.ConditionalBlock([false_image], **kwargs)
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with false_cond.block():
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hidden = layers.fc(input=false_image,
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size=200,
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act='tanh',
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**kwargs)
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prob = layers.fc(input=hidden, size=10, act='softmax', **kwargs)
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layers.assign(input=prob, output=false_out, **kwargs)
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prob = layers.merge_lod_tensor(
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in_true=true_out, in_false=false_out, mask=cond, x=image, **kwargs)
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loss = layers.cross_entropy(input=prob, label=label, **kwargs)
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avg_loss = layers.mean(x=loss, **kwargs)
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optimizer = MomentumOptimizer(learning_rate=0.001, momentum=0.9)
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optimizer.minimize(avg_loss, kwargs['startup_program'])
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train_reader = paddle.batch(
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paddle.reader.shuffle(
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paddle.dataset.mnist.train(), buf_size=8192),
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batch_size=200)
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place = core.CPUPlace()
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exe = Executor(place)
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exe.run(kwargs['startup_program'])
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PASS_NUM = 100
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for pass_id in range(PASS_NUM):
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for data in train_reader():
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x_data = np.array(map(lambda x: x[0], data)).astype("float32")
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y_data = np.array(map(lambda x: x[1], data)).astype("int64")
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y_data = np.expand_dims(y_data, axis=1)
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tensor_x = core.LoDTensor()
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tensor_x.set(x_data, place)
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tensor_y = core.LoDTensor()
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tensor_y.set(y_data, place)
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outs = map(np.array,
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exe.run(kwargs['main_program'],
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feed={'x': tensor_x,
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'y': tensor_y},
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fetch_list=[avg_loss]))
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print outs[0]
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if outs[0] < 1.0:
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return
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self.assertFalse(True)
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def test_ifelse(self):
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kwargs = {'startup_program': Program(), 'main_program': Program()}
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image = layers.data(
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name='x', shape=[784], data_type='float32', **kwargs)
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label = layers.data(name='y', shape=[1], data_type='int64', **kwargs)
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limit = layers.fill_constant_batch_size_like(
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input=label, dtype='int64', shape=[1], value=5.0, **kwargs)
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cond = layers.less_than(x=label, y=limit, **kwargs)
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ie = layers.IfElse(cond, **kwargs)
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with ie.true_block():
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true_image = ie.input(image)
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hidden = layers.fc(input=true_image, size=100, act='tanh', **kwargs)
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prob = layers.fc(input=hidden, size=10, act='softmax', **kwargs)
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ie.output(prob)
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with ie.false_block():
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false_image = ie.input(image)
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hidden = layers.fc(input=false_image,
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size=200,
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act='tanh',
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**kwargs)
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prob = layers.fc(input=hidden, size=10, act='softmax', **kwargs)
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ie.output(prob)
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prob = ie()
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loss = layers.cross_entropy(input=prob[0], label=label, **kwargs)
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avg_loss = layers.mean(x=loss, **kwargs)
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optimizer = MomentumOptimizer(learning_rate=0.001, momentum=0.9)
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optimizer.minimize(avg_loss, kwargs['startup_program'])
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train_reader = paddle.batch(
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paddle.reader.shuffle(
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paddle.dataset.mnist.train(), buf_size=8192),
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batch_size=200)
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place = core.CPUPlace()
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exe = Executor(place)
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exe.run(kwargs['startup_program'])
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PASS_NUM = 100
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for pass_id in range(PASS_NUM):
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for data in train_reader():
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x_data = np.array(map(lambda x: x[0], data)).astype("float32")
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y_data = np.array(map(lambda x: x[1], data)).astype("int64")
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y_data = np.expand_dims(y_data, axis=1)
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tensor_x = core.LoDTensor()
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tensor_x.set(x_data, place)
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tensor_y = core.LoDTensor()
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tensor_y.set(y_data, place)
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outs = map(np.array,
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exe.run(kwargs['main_program'],
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feed={'x': tensor_x,
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'y': tensor_y},
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fetch_list=[avg_loss]))
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print outs[0]
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if outs[0] < 1.0:
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return
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self.assertFalse(True)
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if __name__ == '__main__':
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unittest.main()
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