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70 lines
2.5 KiB
70 lines
2.5 KiB
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from .framework import Program, program_guard, unique_name, default_startup_program
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from .param_attr import ParamAttr
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from .initializer import Constant
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from . import layers
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from . import backward
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from .dygraph import Layer, nn
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from . import executor
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from . import core
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import numpy as np
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__all__ = ['run_check']
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class SimpleLayer(Layer):
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def __init__(self, name_scope):
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super(SimpleLayer, self).__init__(name_scope)
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self._fc1 = nn.FC(self.full_name(),
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3,
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ParamAttr(initializer=Constant(value=0.1)))
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def forward(self, inputs):
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x = self._fc1(inputs)
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x = layers.reduce_sum(x)
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return x
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def run_check():
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''' intall check to verify if install is success
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This func should not be called only if you need to verify installation
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'''
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print("Running Verify Fluid Program ... ")
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prog = Program()
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startup_prog = Program()
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scope = core.Scope()
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with executor.scope_guard(scope):
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with program_guard(prog, startup_prog):
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with unique_name.guard():
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np_inp = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
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inp = layers.data(
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name="inp", shape=[2, 2], append_batch_size=False)
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simple_layer = SimpleLayer("simple_layer")
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out = simple_layer(inp)
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param_grads = backward.append_backward(
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out, parameter_list=[simple_layer._fc1._w.name])[0]
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exe = executor.Executor(core.CPUPlace(
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) if not core.is_compiled_with_cuda() else core.CUDAPlace(0))
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exe.run(default_startup_program())
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exe.run(feed={inp.name: np_inp},
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fetch_list=[out.name, param_grads[1].name])
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print(
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"Your Paddle Fluid is installed successfully! Let's start deep Learning with Paddle Fluid now"
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)
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