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克隆策略

In [1]:

# 本代码由可视化策略环境自动生成 2021年12月10日 19:33
# 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。


m3 = M.dl_layer_input.v1(
    shape='50,5',
    batch_shape='',
    dtype='float32',
    sparse=False,
    name=''
)

m13 = M.dl_layer_reshape.v1(
    inputs=m3.data,
    target_shape='50,5,1',
    name=''
)

m14 = M.dl_layer_conv2d.v1(
    inputs=m13.data,
    filters=32,
    kernel_size='3,5',
    strides='1,1',
    padding='valid',
    data_format='channels_last',
    dilation_rate='1,1',
    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=''
)

m15 = M.dl_layer_reshape.v1(
    inputs=m14.data,
    target_shape='48,32',
    name=''
)

m4 = M.dl_layer_lstm.v1(
    inputs=m15.data,
    units=32,
    activation='tanh',
    recurrent_activation='hard_sigmoid',
    use_bias=True,
    kernel_initializer='glorot_uniform',
    recurrent_initializer='Orthogonal',
    bias_initializer='Ones',
    unit_forget_bias=True,
    kernel_regularizer='None',
    kernel_regularizer_l1=0,
    kernel_regularizer_l2=0,
    recurrent_regularizer='None',
    recurrent_regularizer_l1=0,
    recurrent_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',
    recurrent_constraint='None',
    bias_constraint='None',
    dropout=0,
    recurrent_dropout=0,
    return_sequences=False,
    implementation='1',
    name=''
)

m11 = M.dl_layer_dropout.v1(
    inputs=m4.data,
    rate=0.2,
    noise_shape='',
    name=''
)

m10 = M.dl_layer_dense.v1(
    inputs=m11.data,
    units=32,
    activation='tanh',
    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=''
)

m12 = M.dl_layer_dropout.v1(
    inputs=m10.data,
    rate=0.2,
    noise_shape='',
    name=''
)

m9 = M.dl_layer_dense.v1(
    inputs=m12.data,
    units=1,
    activation='sigmoid',
    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=''
)

m5 = M.dl_model_init.v1(
    inputs=m3.data,
    outputs=m9.data
)

[2021-12-10 19:31:02.570629] INFO: moduleinvoker: dl_layer_input.v1 运行完成[7.751992s].

[2021-12-10 19:31:02.694458] INFO: moduleinvoker: dl_layer_reshape.v1 运行完成[0.09403s].

[2021-12-10 19:31:02.775532] INFO: moduleinvoker: dl_layer_conv2d.v1 运行完成[0.058768s].

[2021-12-10 19:31:02.808467] INFO: moduleinvoker: dl_layer_reshape.v1 运行完成[0.018641s].

[2021-12-10 19:31:03.261631] INFO: moduleinvoker: dl_layer_lstm.v1 运行完成[0.435337s].

[2021-12-10 19:31:03.280897] INFO: moduleinvoker: dl_layer_dropout.v1 运行完成[0.004834s].

[2021-12-10 19:31:03.319262] INFO: moduleinvoker: dl_layer_dense.v1 运行完成[0.019046s].

[2021-12-10 19:31:03.353464] INFO: moduleinvoker: dl_layer_dropout.v1 运行完成[0.006665s].

[2021-12-10 19:31:03.383543] INFO: moduleinvoker: dl_layer_dense.v1 运行完成[0.020288s].

[2021-12-10 19:31:03.464626] INFO: moduleinvoker: cached.v3 开始运行..

[2021-12-10 19:31:03.495511] INFO: moduleinvoker: 命中缓存

[2021-12-10 19:31:03.498350] INFO: moduleinvoker: cached.v3 运行完成[0.033732s].

[2021-12-10 19:31:03.505372] INFO: moduleinvoker: dl_model_init.v1 运行完成[0.105421s].

In [3]:

m5.data.read()

Out[3]:

'backend: tensorflow\nclass_name: Functional\nconfig:\n  input_layers:\n  - - L0\n    - 0\n    - 0\n  layers:\n  - class_name: InputLayer\n    config:\n      batch_input_shape: !!python/tuple\n      - null\n      - 50\n      - 5\n      dtype: float32\n      name: L0\n      ragged: false\n      sparse: false\n    inbound_nodes: []\n    name: L0\n  - class_name: Reshape\n    config:\n      dtype: float32\n      name: reshape\n      target_shape: !!python/tuple\n      - 50\n      - 5\n      - 1\n      trainable: true\n    inbound_nodes:\n    - - - L0\n        - 0\n        - 0\n        - {}\n    name: reshape\n  - class_name: Conv2D\n    config:\n      activation: relu\n      activity_regularizer: null\n      bias_constraint: null\n      bias_initializer:\n        class_name: Zeros\n        config: {}\n      bias_regularizer: null\n      data_format: channels_last\n      dilation_rate: !!python/tuple\n      - 1\n      - 1\n      dtype: float32\n      filters: 32\n      groups: 1\n      kernel_constraint: null\n      kernel_initializer:\n        class_name: GlorotUniform\n        config:\n          seed: null\n      kernel_regularizer: null\n      kernel_size: !!python/tuple\n      - 3\n      - 5\n      name: conv2d\n      padding: valid\n      strides: !!python/tuple\n      - 1\n      - 1\n      trainable: true\n      use_bias: true\n    inbound_nodes:\n    - - - reshape\n        - 0\n        - 0\n        - {}\n    name: conv2d\n  - class_name: Reshape\n    config:\n      dtype: float32\n      name: reshape_1\n      target_shape: !!python/tuple\n      - 48\n      - 32\n      trainable: true\n    inbound_nodes:\n    - - - conv2d\n        - 0\n        - 0\n        - {}\n    name: reshape_1\n  - class_name: LSTM\n    config:\n      activation: tanh\n      activity_regularizer: null\n      bias_constraint: null\n      bias_initializer:\n        class_name: Ones\n        config: {}\n      bias_regularizer: null\n      dropout: 0.0\n      dtype: float32\n      go_backwards: false\n      implementation: 1\n      kernel_constraint: null\n      kernel_initializer:\n        class_name: GlorotUniform\n        config:\n          seed: null\n      kernel_regularizer: null\n      name: lstm\n      recurrent_activation: hard_sigmoid\n      recurrent_constraint: null\n      recurrent_dropout: 0.0\n      recurrent_initializer:\n        class_name: Orthogonal\n        config:\n          gain: 1.0\n          seed: null\n      recurrent_regularizer: null\n      return_sequences: false\n      return_state: false\n      stateful: false\n      time_major: false\n      trainable: true\n      unit_forget_bias: true\n      units: 32\n      unroll: false\n      use_bias: true\n    inbound_nodes:\n    - - - reshape_1\n        - 0\n        - 0\n        - {}\n    name: lstm\n  - class_name: Dropout\n    config:\n      dtype: float32\n      name: dropout\n      noise_shape: null\n      rate: 0.2\n      seed: null\n      trainable: true\n    inbound_nodes:\n    - - - lstm\n        - 0\n        - 0\n        - {}\n    name: dropout\n  - class_name: Dense\n    config:\n      activation: tanh\n      activity_regularizer: null\n      bias_constraint: null\n      bias_initializer:\n        class_name: Zeros\n        config: {}\n      bias_regularizer: null\n      dtype: float32\n      kernel_constraint: null\n      kernel_initializer:\n        class_name: GlorotUniform\n        config:\n          seed: null\n      kernel_regularizer: null\n      name: dense\n      trainable: true\n      units: 32\n      use_bias: true\n    inbound_nodes:\n    - - - dropout\n        - 0\n        - 0\n        - {}\n    name: dense\n  - class_name: Dropout\n    config:\n      dtype: float32\n      name: dropout_1\n      noise_shape: null\n      rate: 0.2\n      seed: null\n      trainable: true\n    inbound_nodes:\n    - - - dense\n        - 0\n        - 0\n        - {}\n    name: dropout_1\n  - class_name: Dense\n    config:\n      activation: sigmoid\n      activity_regularizer: null\n      bias_constraint: null\n      bias_initializer:\n        class_name: Zeros\n        config: {}\n      bias_regularizer: null\n      dtype: float32\n      kernel_constraint: null\n      kernel_initializer:\n        class_name: GlorotUniform\n        config:\n          seed: null\n      kernel_regularizer: null\n      name: dense_1\n      trainable: true\n      units: 1\n      use_bias: true\n    inbound_nodes:\n    - - - dropout_1\n        - 0\n        - 0\n        - {}\n    name: dense_1\n  name: BigQuantDL\n  output_layers:\n  - - dense_1\n    - 0\n    - 0\nkeras_version: 2.4.0\n'