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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7日 18:07
    # 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
    )
    
    WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU
    
    In [5]:
    m10.data
    
    Out[5]:
    <KerasTensor: shape=(None, 32) dtype=float32 (created by layer 'dense')>