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{"description":"实验创建于2017/11/15","graph":{"edges":[{"to_node_id":"-316:inputs","from_node_id":"-210:data"},{"to_node_id":"-403:inputs","from_node_id":"-210:data"},{"to_node_id":"-14834:inputs","from_node_id":"-218:data"},{"to_node_id":"-316:outputs","from_node_id":"-259:data"},{"to_node_id":"-14841:inputs","from_node_id":"-14806:data"},{"to_node_id":"-14806:inputs","from_node_id":"-14834:data"},{"to_node_id":"-259:inputs","from_node_id":"-14841:data"},{"to_node_id":"-408:inputs","from_node_id":"-403:data"},{"to_node_id":"-446:inputs","from_node_id":"-408:data"},{"to_node_id":"-218:inputs","from_node_id":"-446:data"}],"nodes":[{"node_id":"-210","module_id":"BigQuantSpace.dl_layer_input.dl_layer_input-v1","parameters":[{"name":"shape","value":"50,5","type":"Literal","bound_global_parameter":null},{"name":"batch_shape","value":"","type":"Literal","bound_global_parameter":null},{"name":"dtype","value":"float32","type":"Literal","bound_global_parameter":null},{"name":"sparse","value":"False","type":"Literal","bound_global_parameter":null},{"name":"name","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"inputs","node_id":"-210"}],"output_ports":[{"name":"data","node_id":"-210"}],"cacheable":false,"seq_num":3,"comment":"","comment_collapsed":true},{"node_id":"-218","module_id":"BigQuantSpace.dl_layer_lstm.dl_layer_lstm-v1","parameters":[{"name":"units","value":"32","type":"Literal","bound_global_parameter":null},{"name":"activation","value":"tanh","type":"Literal","bound_global_parameter":null},{"name":"user_activation","value":"","type":"Literal","bound_global_parameter":null},{"name":"recurrent_activation","value":"hard_sigmoid","type":"Literal","bound_global_parameter":null},{"name":"user_recurrent_activation","value":"","type":"Literal","bound_global_parameter":null},{"name":"use_bias","value":"True","type":"Literal","bound_global_parameter":null},{"name":"kernel_initializer","value":"glorot_uniform","type":"Literal","bound_global_parameter":null},{"name":"user_kernel_initializer","value":"","type":"Literal","bound_global_parameter":null},{"name":"recurrent_initializer","value":"Orthogonal","type":"Literal","bound_global_parameter":null},{"name":"user_recurrent_initializer","value":"","type":"Literal","bound_global_parameter":null},{"name":"bias_initializer","value":"Ones","type":"Literal","bound_global_parameter":null},{"name":"user_bias_initializer","value":"","type":"Literal","bound_global_parameter":null},{"name":"unit_forget_bias","value":"True","type":"Literal","bound_global_parameter":null},{"name":"kernel_regularizer","value":"None","type":"Literal","bound_global_parameter":null},{"name":"kernel_regularizer_l1","value":0,"type":"Literal","bound_global_parameter":null},{"name":"kernel_regularizer_l2","value":0,"type":"Literal","bound_global_parameter":null},{"name":"user_kernel_regularizer","value":"","type":"Literal","bound_global_parameter":null},{"name":"recurrent_regularizer","value":"None","type":"Literal","bound_global_parameter":null},{"name":"recurrent_regularizer_l1","value":0,"type":"Literal","bound_global_parameter":null},{"name":"recurrent_regularizer_l2","value":0,"type":"Literal","bound_global_parameter":null},{"name":"user_recurrent_regularizer","value":"","type":"Literal","bound_global_parameter":null},{"name":"bias_regularizer","value":"None","type":"Literal","bound_global_parameter":null},{"name":"bias_regularizer_l1","value":0,"type":"Literal","bound_global_parameter":null},{"name":"bias_regularizer_l2","value":0,"type":"Literal","bound_global_parameter":null},{"name":"user_bias_regularizer","value":"","type":"Literal","bound_global_parameter":null},{"name":"activity_regularizer","value":"None","type":"Literal","bound_global_parameter":null},{"name":"activity_regularizer_l1","value":0,"type":"Literal","bound_global_parameter":null},{"name":"activity_regularizer_l2","value":0,"type":"Literal","bound_global_parameter":null},{"name":"user_activity_regularizer","value":"","type":"Literal","bound_global_parameter":null},{"name":"kernel_constraint","value":"None","type":"Literal","bound_global_parameter":null},{"name":"user_kernel_constraint","value":"","type":"Literal","bound_global_parameter":null},{"name":"recurrent_constraint","value":"None","type":"Literal","bound_global_parameter":null},{"name":"user_recurrent_constraint","value":"","type":"Literal","bound_global_parameter":null},{"name":"bias_constraint","value":"None","type":"Literal","bound_global_parameter":null},{"name":"user_bias_constraint","value":"","type":"Literal","bound_global_parameter":null},{"name":"dropout","value":"0","type":"Literal","bound_global_parameter":null},{"name":"recurrent_dropout","value":0,"type":"Literal","bound_global_parameter":null},{"name":"return_sequences","value":"False","type":"Literal","bound_global_parameter":null},{"name":"implementation","value":"1","type":"Literal","bound_global_parameter":null},{"name":"name","value":"","type":"Literal","bound_global_parame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    In [1]:
    # 本代码由可视化策略环境自动生成 2021年12月10日 15:46
# 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
)
    In [2]:
    m11.data
    Out[2]:
    <KerasTensor: shape=(None, 32) dtype=float32 (created by layer 'dropout')>