我们从Python开源项目中，提取了以下7个代码示例，用于说明如何使用keras.layers.core.SpatialDropout2D()。
def get_model(shape, dropout=0.5, path=None): print('building neural network') model=Sequential() model.add(Convolution2D(512, 3, 3, border_mode='same', input_shape=shape)) model.add(Activation('relu')) model.add(Convolution2D(512, 3, 3, border_mode='same')) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(SpatialDropout2D(dropout)) model.add(Flatten())#input_shape=shape)) # model.add(Dense(4096)) # model.add(Activation('relu')) # model.add(Dropout(0.5)) model.add(Dense(512)) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(1)) #model.add(Activation('linear')) return model
def test_tiny_conv_dropout_random(self): np.random.seed(1988) num_samples = 1 input_dim = 8 input_shape = (input_dim, input_dim, 3) num_kernels = 2 kernel_height = 5 kernel_width = 5 hidden_dim = 4 # Define a model model = Sequential() model.add(Conv2D(input_shape = input_shape, filters = num_kernels, kernel_size=(kernel_height, kernel_width))) model.add(SpatialDropout2D(0.5)) model.add(Flatten()) model.add(Dense(hidden_dim)) # Set some random weights model.set_weights([np.random.rand(*w.shape) for w in model.get_weights()]) # Get the coreml model self._test_keras_model(model)
def test_dropout(): layer_test(core.Dropout, kwargs={'p': 0.5}, input_shape=(3, 2)) layer_test(core.SpatialDropout1D, kwargs={'p': 0.5}, input_shape=(2, 3, 4)) layer_test(core.SpatialDropout2D, kwargs={'p': 0.5}, input_shape=(2, 3, 4, 5)) layer_test(core.SpatialDropout3D, kwargs={'p': 0.5}, input_shape=(2, 3, 4, 5, 6))
def test_dropout(): layer_test(core.Dropout, kwargs={'p': 0.5}, input_shape=(3, 2)) layer_test(core.Dropout, kwargs={'p': 0.5, 'noise_shape': [3, 1]}, input_shape=(3, 2)) layer_test(core.SpatialDropout1D, kwargs={'p': 0.5}, input_shape=(2, 3, 4)) layer_test(core.SpatialDropout2D, kwargs={'p': 0.5}, input_shape=(2, 3, 4, 5)) layer_test(core.SpatialDropout3D, kwargs={'p': 0.5}, input_shape=(2, 3, 4, 5, 6))
def bottleneck(inp, output, internal_scale=4, asymmetric=0, dilated=0, downsample=False, dropout_rate=0.1): # main branch internal = output // internal_scale encoder = inp # 1x1 input_stride = 2 if downsample else 1 # the 1st 1x1 projection is replaced with a 2x2 convolution when downsampling encoder = Conv2D(internal, (input_stride, input_stride), # padding='same', strides=(input_stride, input_stride), use_bias=False)(encoder) # Batch normalization + PReLU encoder = BatchNormalization(momentum=0.1)(encoder) # enet uses momentum of 0.1, keras default is 0.99 encoder = PReLU(shared_axes=[1, 2])(encoder) # conv if not asymmetric and not dilated: encoder = Conv2D(internal, (3, 3), padding='same')(encoder) elif asymmetric: encoder = Conv2D(internal, (1, asymmetric), padding='same', use_bias=False)(encoder) encoder = Conv2D(internal, (asymmetric, 1), padding='same')(encoder) elif dilated: encoder = Conv2D(internal, (3, 3), dilation_rate=(dilated, dilated), padding='same')(encoder) else: raise(Exception('You shouldn\'t be here')) encoder = BatchNormalization(momentum=0.1)(encoder) # enet uses momentum of 0.1, keras default is 0.99 encoder = PReLU(shared_axes=[1, 2])(encoder) # 1x1 encoder = Conv2D(output, (1, 1), use_bias=False)(encoder) encoder = BatchNormalization(momentum=0.1)(encoder) # enet uses momentum of 0.1, keras default is 0.99 encoder = SpatialDropout2D(dropout_rate)(encoder) other = inp # other branch if downsample: other = MaxPooling2D()(other) other = Permute((1, 3, 2))(other) pad_feature_maps = output - inp.get_shape().as_list()[3] tb_pad = (0, 0) lr_pad = (0, pad_feature_maps) other = ZeroPadding2D(padding=(tb_pad, lr_pad))(other) other = Permute((1, 3, 2))(other) encoder = add([encoder, other]) encoder = PReLU(shared_axes=[1, 2])(encoder) return encoder
def bottleneck(inp, output, internal_scale=4, asymmetric=0, dilated=0, downsample=False, dropout_rate=0.1): # main branch internal = output // internal_scale encoder = inp # 1x1 input_stride = 2 if downsample else 1 # the 1st 1x1 projection is replaced with a 2x2 convolution when downsampling encoder = Conv2D(internal, (input_stride, input_stride), # padding='same', strides=(input_stride, input_stride), use_bias=False)(encoder) # Batch normalization + PReLU encoder = BatchNormalization(momentum=0.1)(encoder) # enet_unpooling uses momentum of 0.1, keras default is 0.99 encoder = PReLU(shared_axes=[1, 2])(encoder) # conv if not asymmetric and not dilated: encoder = Conv2D(internal, (3, 3), padding='same')(encoder) elif asymmetric: encoder = Conv2D(internal, (1, asymmetric), padding='same', use_bias=False)(encoder) encoder = Conv2D(internal, (asymmetric, 1), padding='same')(encoder) elif dilated: encoder = Conv2D(internal, (3, 3), dilation_rate=(dilated, dilated), padding='same')(encoder) else: raise(Exception('You shouldn\'t be here')) encoder = BatchNormalization(momentum=0.1)(encoder) # enet_unpooling uses momentum of 0.1, keras default is 0.99 encoder = PReLU(shared_axes=[1, 2])(encoder) # 1x1 encoder = Conv2D(output, (1, 1), use_bias=False)(encoder) encoder = BatchNormalization(momentum=0.1)(encoder) # enet_unpooling uses momentum of 0.1, keras default is 0.99 encoder = SpatialDropout2D(dropout_rate)(encoder) other = inp # other branch if downsample: other, indices = MaxPoolingWithArgmax2D()(other) other = Permute((1, 3, 2))(other) pad_feature_maps = output - inp.get_shape().as_list()[3] tb_pad = (0, 0) lr_pad = (0, pad_feature_maps) other = ZeroPadding2D(padding=(tb_pad, lr_pad))(other) other = Permute((1, 3, 2))(other) encoder = add([encoder, other]) encoder = PReLU(shared_axes=[1, 2])(encoder) if downsample: return encoder, indices else: return encoder
