本文整理汇总了Python中keras.layers.LocallyConnected1D方法的典型用法代码示例。如果您正苦于以下问题：Python layers.LocallyConnected1D方法的具体用法？Python layers.LocallyConnected1D怎么用？Python layers.LocallyConnected1D使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类keras.layers的用法示例。
在下文中一共展示了layers.LocallyConnected1D方法的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_keras_import
# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import LocallyConnected1D [as 别名]
def test_keras_import(self):
# Conv 1D
model = Sequential()
model.add(LocallyConnected1D(32, 3, kernel_regularizer=regularizers.l2(0.01),
bias_regularizer=regularizers.l2(0.01),
activity_regularizer=regularizers.l2(0.01), kernel_constraint='max_norm',
bias_constraint='max_norm', activation='relu', input_shape=(16, 10)))
model.build()
self.keras_param_test(model, 1, 12)
# Conv 2D
model = Sequential()
model.add(LocallyConnected2D(32, (3, 3), kernel_regularizer=regularizers.l2(0.01),
bias_regularizer=regularizers.l2(0.01),
activity_regularizer=regularizers.l2(0.01), kernel_constraint='max_norm',
bias_constraint='max_norm', activation='relu', input_shape=(16, 16, 10)))
model.build()
self.keras_param_test(model, 1, 14)
# ********** Recurrent Layers ********** 示例2: localconv1d
# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import LocallyConnected1D [as 别名]
def localconv1d(x, filters, kernel_size, strides=1, use_bias=True, name=None):
"""LocallyConnected1D possibly wrapped by a TimeDistributed layer."""
f = LocallyConnected1D(filters, kernel_size, strides=strides,
use_bias=use_bias, name=name)
return TimeDistributed(f, name=name)(x) if K.ndim(x) == 4 else f(x) 示例3: add_conv_layer
# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import LocallyConnected1D [as 别名]
def add_conv_layer(model, layer_params, input_dim=None, locally_connected=False):
if len(layer_params) == 3: # 1D convolution
filters = layer_params[0]
filter_len = layer_params[1]
stride = layer_params[2]
if locally_connected:
if input_dim:
model.add(LocallyConnected1D(filters, filter_len, strides=stride, input_shape=(input_dim, 1)))
else:
model.add(LocallyConnected1D(filters, filter_len, strides=stride))
else:
if input_dim:
model.add(Conv1D(filters, filter_len, strides=stride, input_shape=(input_dim, 1)))
else:
model.add(Conv1D(filters, filter_len, strides=stride))
elif len(layer_params) == 5: # 2D convolution
filters = layer_params[0]
filter_len = (layer_params[1], layer_params[2])
stride = (layer_params[3], layer_params[4])
if locally_connected:
if input_dim:
model.add(LocallyConnected2D(filters, filter_len, strides=stride, input_shape=(input_dim, 1)))
else:
model.add(LocallyConnected2D(filters, filter_len, strides=stride))
else:
if input_dim:
model.add(Conv2D(filters, filter_len, strides=stride, input_shape=(input_dim, 1)))
else:
model.add(Conv2D(filters, filter_len, strides=stride))
return model 示例4: locally_connected
# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import LocallyConnected1D [as 别名]
def locally_connected(layer, layer_in, layerId, tensor=True):
localMap = {
'1D': LocallyConnected1D,
'2D': LocallyConnected2D,
}
out = {}
kernel_initializer = layer['params']['kernel_initializer']
bias_initializer = layer['params']['bias_initializer']
filters = layer['params']['filters']
kernel_regularizer = regularizerMap[layer['params']['kernel_regularizer']]
bias_regularizer = regularizerMap[layer['params']['bias_regularizer']]
activity_regularizer = regularizerMap[layer['params']
['activity_regularizer']]
kernel_constraint = constraintMap[layer['params']['kernel_constraint']]
bias_constraint = constraintMap[layer['params']['bias_constraint']]
use_bias = layer['params']['use_bias']
layer_type = layer['params']['layer_type']
if (layer_type == '1D'):
strides = layer['params']['stride_w']
kernel = layer['params']['kernel_w']
else:
strides = (layer['params']['stride_h'], layer['params']['stride_w'])
kernel = (layer['params']['kernel_h'], layer['params']['kernel_w'])
out[layerId] = localMap[layer_type](filters, kernel, strides=strides, padding='valid',
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer, use_bias=use_bias,
bias_constraint=bias_constraint,
kernel_constraint=kernel_constraint)
if tensor:
out[layerId] = out[layerId](*layer_in)
return out
# ********** Recurrent Layers ********** 示例5: test_keras_export
# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import LocallyConnected1D [as 别名]
def test_keras_export(self):
tests = open(os.path.join(settings.BASE_DIR, 'tests', 'unit', 'keras_app',
'keras_export_test.json'), 'r')
response = json.load(tests)
tests.close()
net = yaml.safe_load(json.dumps(response['net']))
net = {'l0': net['Input'], 'l1': net['Input2'], 'l3': net['LocallyConnected']}
# LocallyConnected 1D
net['l1']['connection']['output'].append('l3')
net['l3']['connection']['input'] = ['l1']
net['l3']['params']['layer_type'] = '1D'
inp = data(net['l1'], '', 'l1')['l1']
temp = locally_connected(net['l3'], [inp], 'l3')
model = Model(inp, temp['l3'])
self.assertEqual(model.layers[1].__class__.__name__, 'LocallyConnected1D')
# LocallyConnected 2D
net['l0']['connection']['output'].append('l0')
net['l0']['shape']['output'] = [3, 10, 10]
net['l3']['connection']['input'] = ['l0']
net['l3']['params']['layer_type'] = '2D'
inp = data(net['l0'], '', 'l0')['l0']
temp = locally_connected(net['l3'], [inp], 'l3')
model = Model(inp, temp['l3'])
self.assertEqual(model.layers[1].__class__.__name__, 'LocallyConnected2D')
# ********** Recurrent Layers Test **********