""" CNN on mnist data using fluid api of paddlepaddle """ import paddle.v2 as paddle import paddle.v2.fluid as fluid def mnist_cnn_model(img): """ Mnist cnn model Args: img(Varaible): the input image to be recognized Returns: Variable: the label prediction """ #conv1 = fluid.nets.conv2d() conv_pool_1 = fluid.nets.simple_img_conv_pool( input=img, num_filters=20, filter_size=5, pool_size=2, pool_stride=2, act='relu') conv_pool_2 = fluid.nets.simple_img_conv_pool( input=conv_pool_1, num_filters=50, filter_size=5, pool_size=2, pool_stride=2, act='relu') logits = fluid.layers.fc( input=conv_pool_2, size=10, act='softmax') return logits def main(): """ Train the cnn model on mnist datasets """ img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32') label = fluid.layers.data(name='label', shape=[1], dtype='int64') logits = mnist_cnn_model(img) cost = fluid.layers.cross_entropy(input=logits, label=label) avg_cost = fluid.layers.mean(x=cost) optimizer = fluid.optimizer.Adam(learning_rate=0.01) optimizer.minimize(avg_cost) accuracy = fluid.evaluator.Accuracy(input=logits, label=label) BATCH_SIZE = 50 PASS_NUM = 3 ACC_THRESHOLD = 0.98 LOSS_THRESHOLD = 10.0 train_reader = paddle.batch( paddle.reader.shuffle( paddle.dataset.mnist.train(), buf_size=500), batch_size=BATCH_SIZE) place = fluid.CPUPlace() exe = fluid.Executor(place) feeder = fluid.DataFeeder(feed_list=[img, label], place=place) exe.run(fluid.default_startup_program()) for pass_id in range(PASS_NUM): accuracy.reset(exe) for data in train_reader(): loss, acc = exe.run(fluid.default_main_program(), feed=feeder.feed(data), fetch_list=[avg_cost] + accuracy.metrics) pass_acc = accuracy.eval(exe) print("pass_id=" + str(pass_id) + " acc=" + str(acc) + " pass_acc=" + str(pass_acc)) # print loss, acc if loss < LOSS_THRESHOLD and pass_acc > ACC_THRESHOLD: # if avg cost less than 10.0 and accuracy is larger than 0.9, we think our code is good. break # exit(0) pass_acc = accuracy.eval(exe) print("pass_id=" + str(pass_id) + " pass_acc=" + str(pass_acc)) fluid.io.save_params(exe, dirname='./mnist', main_program=fluid.default_main_program()) print('train mnist done') exit(1) if __name__ == '__main__': main()