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87 lines
2.5 KiB
87 lines
2.5 KiB
"""
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CNN on mnist data using fluid api of paddlepaddle
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"""
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import paddle.v2 as paddle
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import paddle.v2.fluid as fluid
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def mnist_cnn_model(img):
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"""
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Mnist cnn model
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Args:
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img(Varaible): the input image to be recognized
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Returns:
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Variable: the label prediction
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"""
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conv_pool_1 = fluid.nets.simple_img_conv_pool(
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input=img,
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num_filters=20,
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filter_size=5,
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pool_size=2,
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pool_stride=2,
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act='relu')
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conv_pool_2 = fluid.nets.simple_img_conv_pool(
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input=conv_pool_1,
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num_filters=50,
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filter_size=5,
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pool_size=2,
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pool_stride=2,
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act='relu')
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logits = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax')
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return logits
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def main():
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"""
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Train the cnn model on mnist datasets
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"""
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img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
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label = fluid.layers.data(name='label', shape=[1], dtype='int64')
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logits = mnist_cnn_model(img)
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cost = fluid.layers.cross_entropy(input=logits, label=label)
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avg_cost = fluid.layers.mean(x=cost)
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optimizer = fluid.optimizer.Adam(learning_rate=0.01)
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optimizer.minimize(avg_cost)
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accuracy = fluid.evaluator.Accuracy(input=logits, label=label)
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BATCH_SIZE = 50
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PASS_NUM = 3
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ACC_THRESHOLD = 0.98
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LOSS_THRESHOLD = 10.0
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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=500),
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batch_size=BATCH_SIZE)
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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feeder = fluid.DataFeeder(feed_list=[img, label], place=place)
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exe.run(fluid.default_startup_program())
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for pass_id in range(PASS_NUM):
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accuracy.reset(exe)
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for data in train_reader():
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loss, acc = exe.run(fluid.default_main_program(),
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feed=feeder.feed(data),
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fetch_list=[avg_cost] + accuracy.metrics)
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pass_acc = accuracy.eval(exe)
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print("pass_id=" + str(pass_id) + " acc=" + str(acc) + " pass_acc="
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+ str(pass_acc))
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if loss < LOSS_THRESHOLD and pass_acc > ACC_THRESHOLD:
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break
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pass_acc = accuracy.eval(exe)
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print("pass_id=" + str(pass_id) + " pass_acc=" + str(pass_acc))
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fluid.io.save_params(
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exe, dirname='./mnist', main_program=fluid.default_main_program())
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print('train mnist done')
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if __name__ == '__main__':
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main()
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