def test_conv_lstm_2d_config(self):
config_dict = {
'filters': 20,
'kernel_size': 3,
'strides': [1, 1],
'padding': 'valid',
'data_format': None,
'dilation_rate': [1, 1],
'activation': 'tanh',
'recurrent_activation': 'hard_sigmoid',
'use_bias': True,
'kernel_initializer': GlorotNormalInitializerConfig().to_schema(),
'recurrent_initializer': OrthogonalInitializerConfig().to_schema(),
'bias_initializer': ZerosInitializerConfig().to_schema(),
'unit_forget_bias': True,
'kernel_regularizer': None,
'recurrent_regularizer': None,
'bias_regularizer': None,
'activity_regularizer': None,
'kernel_constraint': None,
'recurrent_constraint': None,
'bias_constraint': None,
'return_sequences': False,
'go_backwards': False,
'stateful': False,
'dropout': 0.,
'recurrent_dropout': 0.
}
config = ConvLSTM2DConfig.from_dict(config_dict)
assert_equal_layers(config, config_dict)
def __init__(self,
units,
activation='tanh',
recurrent_activation='hard_sigmoid',
use_bias=True,
kernel_initializer=GlorotUniformInitializerConfig(),
recurrent_initializer=OrthogonalInitializerConfig(),
bias_initializer=ZerosInitializerConfig(),
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
dropout=0.,
recurrent_dropout=0.,
**kwargs):
super(GRUConfig, self).__init__(**kwargs)
self.units = units
self.activation = activation
self.recurrent_activation = recurrent_activation
self.use_bias = use_bias
self.kernel_initializer = kernel_initializer
self.recurrent_initializer = recurrent_initializer
self.bias_initializer = bias_initializer
self.kernel_regularizer = kernel_regularizer
self.recurrent_regularizer = recurrent_regularizer
self.bias_regularizer = bias_regularizer
self.activity_regularizer = activity_regularizer
self.kernel_constraint = kernel_constraint
self.recurrent_constraint = recurrent_constraint
self.bias_constraint = bias_constraint
self.dropout = dropout
self.recurrent_dropout = recurrent_dropout
def test_simple_rnn_config(self):
config_dict = {
'units': 3,
'activation': 'tanh',
'use_bias': True,
'kernel_initializer': GlorotUniformInitializerConfig().to_schema(),
'recurrent_initializer': OrthogonalInitializerConfig().to_schema(),
'bias_initializer': ZerosInitializerConfig().to_schema(),
'kernel_regularizer': None,
'recurrent_regularizer': None,
'bias_regularizer': None,
'activity_regularizer': None,
'kernel_constraint': None,
'recurrent_constraint': None,
'bias_constraint': None,
'dropout': 0.,
'recurrent_dropout': 0.,
}
config = SimpleRNNConfig.from_dict(config_dict)
assert_equal_layers(config, config_dict)