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# 本代码由可视化策略环境自动生成 2018年2月5日 17:42
# 本代码单元只能在可视化模式下编辑。您也可以拷贝代码，粘贴到新建的代码单元或者策略，然后修改。


m1 = M.dl_layer_input.v1(
    shape='784,',
    batch_shape='',
    dtype='float32',
    sparse=False,
    name=''
)

m2 = M.dl_layer_dense.v1(
    inputs=m1.data,
    units=512,
    activation='relu',
    use_bias=True,
    kernel_initializer='glorot_uniform',
    bias_initializer='Zeros',
    kernel_regularizer='None',
    kernel_regularizer_l1=0,
    kernel_regularizer_l2=0,
    bias_regularizer='None',
    bias_regularizer_l1=0,
    bias_regularizer_l2=0,
    activity_regularizer='None',
    activity_regularizer_l1=0,
    activity_regularizer_l2=0,
    kernel_constraint='None',
    bias_constraint='None',
    name=''
)

m3 = M.dl_layer_dense.v1(
    inputs=m2.data,
    units=10,
    activation='softmax',
    use_bias=True,
    kernel_initializer='glorot_uniform',
    bias_initializer='Zeros',
    kernel_regularizer='None',
    kernel_regularizer_l1=0,
    kernel_regularizer_l2=0,
    bias_regularizer='None',
    bias_regularizer_l1=0,
    bias_regularizer_l2=0,
    activity_regularizer='None',
    activity_regularizer_l1=0,
    activity_regularizer_l2=0,
    kernel_constraint='None',
    bias_constraint='None',
    name=''
)

m4 = M.dl_model_init.v1(
    inputs=m1.data,
    outputs=m3.data
)

# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端
def m6_run_bigquant_run(input_1, input_2, input_3):
    
    from keras.datasets import mnist
    from keras.utils import to_categorical
    
    (train_images, train_labels),(test_images, test_labels) = mnist.load_data()
    
    train_x = train_images.reshape((60000,784)).astype("float32")/255
    test_x = test_images.reshape((10000,784)).astype("float32")/255

    train_y = to_categorical(train_labels)
    test_y = to_categorical(test_labels)

    train = {"x":train_x,"y":train_y}
    test = {"x":test_x,"y":test_y}
    
    print(train_y.shape)
    print(train_x.shape)
    data_1 = DataSource.write_pickle(train)
    data_2 = DataSource.write_pickle(test)
    return Outputs(data_1=data_1, data_2=data_2, data_3=None)

m6 = M.cached.v3(
    run=m6_run_bigquant_run
)

m5 = M.dl_model_train.v1(
    input_model=m4.data,
    training_data=m6.data_1,
    validation_data=m6.data_2,
    optimizer='RMSprop',
    loss='categorical_crossentropy',
    metrics='accuracy',
    batch_size=128,
    epochs=5,
    n_gpus=0,
    verbose='2:每个epoch输出一行记录'
)

[2018-02-05 17:39:42.820319] INFO: bigquant: cached.v3 开始运行..
[2018-02-05 17:39:42.825028] INFO: bigquant: 命中缓存
[2018-02-05 17:39:42.826304] INFO: bigquant: cached.v3 运行完成[0.006059s].
[2018-02-05 17:39:42.838917] INFO: bigquant: cached.v3 开始运行..
[2018-02-05 17:39:42.843257] INFO: bigquant: 命中缓存
[2018-02-05 17:39:42.844596] INFO: bigquant: cached.v3 运行完成[0.005698s].
[2018-02-05 17:39:42.853189] INFO: bigquant: dl_model_train.v1 开始运行..
[2018-02-05 17:39:43.140365] INFO: device_manager: 没有gpu资源，将使用cpu计算
[2018-02-05 17:39:43.146045] INFO: device_manager: 本次操作不使用GPU
[2018-02-05 17:39:47.210659] INFO: dl_model_train: 准备训练，训练样本个数：60000，迭代次数：5
Train on 60000 samples, validate on 10000 samples
Epoch 1/5
14s - loss: 0.2552 - acc: 0.9261 - val_loss: 0.1389 - val_acc: 0.9579
Epoch 2/5
17s - loss: 0.1043 - acc: 0.9697 - val_loss: 0.0905 - val_acc: 0.9724
Epoch 3/5
15s - loss: 0.0691 - acc: 0.9792 - val_loss: 0.0788 - val_acc: 0.9749
Epoch 4/5
15s - loss: 0.0509 - acc: 0.9844 - val_loss: 0.0701 - val_acc: 0.9801
Epoch 5/5
21s - loss: 0.0373 - acc: 0.9889 - val_loss: 0.0693 - val_acc: 0.9789
[2018-02-05 17:41:12.150540] INFO: dl_model_train: 训练结束，耗时：84.93s
[2018-02-05 17:41:12.312077] INFO: bigquant: dl_model_train.v1 运行完成[89.458718s].