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| kwargs['initial_state'] = initial_state | ||
| return super(Recurrent, self).__call__(inputs, **kwargs) | ||
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| def call(self, inputs, mask=None, training=None, initial_state=None): |
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The function is too long
call( self , inputs , mask = None , training = None , initial_state = None ) now spans 58 lines.
Corresponding modifications started here.
Keep your functions' length within 50 lines to improve readability.
This comment was generated with the following checker: long_method
| kwargs['initial_state'] = initial_state | ||
| return super(Recurrent, self).__call__(inputs, **kwargs) | ||
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| def call(self, inputs, mask=None, training=None, initial_state=None): |
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The function is too complicated
call( self , inputs , mask = None , training = None , initial_state = None ) now has cyclomatic complexity of 13.
Corresponding modifications started here.
Split your routines to keep cyclomatic complexity below 10 to improve their maintainability.
This comment was generated with the following checker: high_cyclomatic_complexity
| self.input_spec = [InputSpec(ndim=5)] | ||
| self.state_spec = None | ||
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| def compute_output_shape(self, input_shape): |
There was a problem hiding this comment.
The function is too complicated
compute_output_shape( self , input_shape ) now has cyclomatic complexity of 12.
Corresponding modifications started here.
Split your routines to keep cyclomatic complexity below 10 to improve their maintainability.
This comment was generated with the following checker: high_cyclomatic_complexity
| - [Maxout Networks](http://arxiv.org/abs/1302.4389) | ||
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| def __init__(self, output_dim, |
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The function has too many parameters
__init__( self , output_dim , nb_feature = 4 , init = 'glorot_uniform' , weights = None , W_regularizer = None , b_regularizer = None , activity_regularizer = None , W_constraint = None , b_constraint = None , bias = True , input_dim = None , ** kwargs ) now has 13 parameters.
Functions with parameter list length above 5 are mostly hard to understand and use.
To reduce the number of arguments, you can isolate arguments used together into an object, or split the function
This comment was generated with the following checker: long_parameter_list
| - [Highway Networks](http://arxiv.org/abs/1505.00387v2) | ||
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| def __init__(self, |
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The function has too many parameters
__init__( self , init = 'glorot_uniform' , activation = None , weights = None , W_regularizer = None , b_regularizer = None , activity_regularizer = None , W_constraint = None , b_constraint = None , bias = True , input_dim = None , ** kwargs ) now has 12 parameters.
Functions with parameter list length above 5 are mostly hard to understand and use.
To reduce the number of arguments, you can isolate arguments used together into an object, or split the function
This comment was generated with the following checker: long_parameter_list
| the initial state of the RNN layer. | ||
| """ | ||
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| def __init__(self, return_sequences=False, |
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The function has too many parameters
__init__( self , return_sequences = False , return_state = False , go_backwards = False , stateful = False , unroll = False , implementation = 0 , ** kwargs ) now has 8 parameters.
Functions with parameter list length above 5 are mostly hard to understand and use.
To reduce the number of arguments, you can isolate arguments used together into an object, or split the function
This comment was generated with the following checker: long_parameter_list
ghost
added
This pull request is a copy of /keras/legacy/layers.py from keras-team/keras