本文整理汇总了Python中keras.layers.core.SpatialDropout2D方法的典型用法代码示例。如果您正苦于以下问题:Python core.SpatialDropout2D方法的具体用法?Python core.SpatialDropout2D怎么用?Python core.SpatialDropout2D使用的例子?那么, 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.core
的用法示例。
在下文中一共展示了core.SpatialDropout2D方法的10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: double_conv_layer
# 需要导入模块: from keras.layers import core [as 别名]
# 或者: from keras.layers.core import SpatialDropout2D [as 别名]
def double_conv_layer(x, size, dropout=0.0, batch_norm=True):
if K.image_dim_ordering() == 'th':
axis = 1
else:
axis = 3
conv = Conv2D(size, (3, 3), padding='same')(x)
if batch_norm is True:
conv = BatchNormalization(axis=axis)(conv)
conv = Activation('relu')(conv)
conv = Conv2D(size, (3, 3), padding='same')(conv)
if batch_norm is True:
conv = BatchNormalization(axis=axis)(conv)
conv = Activation('relu')(conv)
if dropout > 0:
conv = SpatialDropout2D(dropout)(conv)
return conv
示例2: get_unet_resnet
# 需要导入模块: from keras.layers import core [as 别名]
# 或者: from keras.layers.core import SpatialDropout2D [as 别名]
def get_unet_resnet(input_shape):
resnet_base = ResNet50(input_shape=input_shape, include_top=False)
if args.show_summary:
resnet_base.summary()
for l in resnet_base.layers:
l.trainable = True
conv1 = resnet_base.get_layer("activation_1").output
conv2 = resnet_base.get_layer("activation_10").output
conv3 = resnet_base.get_layer("activation_22").output
conv4 = resnet_base.get_layer("activation_40").output
conv5 = resnet_base.get_layer("activation_49").output
up6 = concatenate([UpSampling2D()(conv5), conv4], axis=-1)
conv6 = conv_block_simple(up6, 256, "conv6_1")
conv6 = conv_block_simple(conv6, 256, "conv6_2")
up7 = concatenate([UpSampling2D()(conv6), conv3], axis=-1)
conv7 = conv_block_simple(up7, 192, "conv7_1")
conv7 = conv_block_simple(conv7, 192, "conv7_2")
up8 = concatenate([UpSampling2D()(conv7), conv2], axis=-1)
conv8 = conv_block_simple(up8, 128, "conv8_1")
conv8 = conv_block_simple(conv8, 128, "conv8_2")
up9 = concatenate([UpSampling2D()(conv8), conv1], axis=-1)
conv9 = conv_block_simple(up9, 64, "conv9_1")
conv9 = conv_block_simple(conv9, 64, "conv9_2")
vgg = VGG16(input_shape=input_shape, input_tensor=resnet_base.input, include_top=False)
for l in vgg.layers:
l.trainable = False
vgg_first_conv = vgg.get_layer("block1_conv2").output
up10 = concatenate([UpSampling2D()(conv9), resnet_base.input, vgg_first_conv], axis=-1)
conv10 = conv_block_simple(up10, 32, "conv10_1")
conv10 = conv_block_simple(conv10, 32, "conv10_2")
conv10 = SpatialDropout2D(0.2)(conv10)
x = Conv2D(1, (1, 1), activation="sigmoid", name="prediction")(conv10)
model = Model(resnet_base.input, x)
return model
示例3: get_simple_unet
# 需要导入模块: from keras.layers import core [as 别名]
# 或者: from keras.layers.core import SpatialDropout2D [as 别名]
def get_simple_unet(input_shape):
img_input = Input(input_shape)
conv1 = conv_block_simple(img_input, 32, "conv1_1")
conv1 = conv_block_simple(conv1, 32, "conv1_2")
pool1 = MaxPooling2D((2, 2), strides=(2, 2), padding="same", name="pool1")(conv1)
conv2 = conv_block_simple(pool1, 64, "conv2_1")
conv2 = conv_block_simple(conv2, 64, "conv2_2")
pool2 = MaxPooling2D((2, 2), strides=(2, 2), padding="same", name="pool2")(conv2)
conv3 = conv_block_simple(pool2, 128, "conv3_1")
conv3 = conv_block_simple(conv3, 128, "conv3_2")
pool3 = MaxPooling2D((2, 2), strides=(2, 2), padding="same", name="pool3")(conv3)
conv4 = conv_block_simple(pool3, 256, "conv4_1")
conv4 = conv_block_simple(conv4, 256, "conv4_2")
conv4 = conv_block_simple(conv4, 256, "conv4_3")
up5 = concatenate([UpSampling2D()(conv4), conv3], axis=-1)
conv5 = conv_block_simple(up5, 128, "conv5_1")
conv5 = conv_block_simple(conv5, 128, "conv5_2")
up6 = concatenate([UpSampling2D()(conv5), conv2], axis=-1)
conv6 = conv_block_simple(up6, 64, "conv6_1")
conv6 = conv_block_simple(conv6, 64, "conv6_2")
up7 = concatenate([UpSampling2D()(conv6), conv1], axis=-1)
conv7 = conv_block_simple(up7, 32, "conv7_1")
conv7 = conv_block_simple(conv7, 32, "conv7_2")
conv7 = SpatialDropout2D(0.2)(conv7)
prediction = Conv2D(1, (1, 1), activation="sigmoid", name="prediction")(conv7)
model = Model(img_input, prediction)
return model
示例4: get_unet_mobilenet
# 需要导入模块: from keras.layers import core [as 别名]
# 或者: from keras.layers.core import SpatialDropout2D [as 别名]
def get_unet_mobilenet(input_shape):
base_model = MobileNet(include_top=False, input_shape=input_shape)
conv1 = base_model.get_layer('conv_pw_1_relu').output
conv2 = base_model.get_layer('conv_pw_3_relu').output
conv3 = base_model.get_layer('conv_pw_5_relu').output
conv4 = base_model.get_layer('conv_pw_11_relu').output
conv5 = base_model.get_layer('conv_pw_13_relu').output
up6 = concatenate([UpSampling2D()(conv5), conv4], axis=-1)
conv6 = conv_block_simple(up6, 256, "conv6_1")
conv6 = conv_block_simple(conv6, 256, "conv6_2")
up7 = concatenate([UpSampling2D()(conv6), conv3], axis=-1)
conv7 = conv_block_simple(up7, 256, "conv7_1")
conv7 = conv_block_simple(conv7, 256, "conv7_2")
up8 = concatenate([UpSampling2D()(conv7), conv2], axis=-1)
conv8 = conv_block_simple(up8, 192, "conv8_1")
conv8 = conv_block_simple(conv8, 128, "conv8_2")
up9 = concatenate([UpSampling2D()(conv8), conv1], axis=-1)
conv9 = conv_block_simple(up9, 96, "conv9_1")
conv9 = conv_block_simple(conv9, 64, "conv9_2")
up10 = concatenate([UpSampling2D()(conv9), base_model.input], axis=-1)
conv10 = conv_block_simple(up10, 48, "conv10_1")
conv10 = conv_block_simple(conv10, 32, "conv10_2")
conv10 = SpatialDropout2D(0.2)(conv10)
x = Conv2D(1, (1, 1), activation="sigmoid", name="prediction")(conv10)
model = Model(base_model.input, x)
return model
示例5: get_unet_inception_resnet_v2
# 需要导入模块: from keras.layers import core [as 别名]
# 或者: from keras.layers.core import SpatialDropout2D [as 别名]
def get_unet_inception_resnet_v2(input_shape):
base_model = InceptionResNetV2(include_top=False, input_shape=input_shape)
conv1 = base_model.get_layer('activation_3').output
conv2 = base_model.get_layer('activation_5').output
conv3 = base_model.get_layer('block35_10_ac').output
conv4 = base_model.get_layer('block17_20_ac').output
conv5 = base_model.get_layer('conv_7b_ac').output
up6 = concatenate([UpSampling2D()(conv5), conv4], axis=-1)
conv6 = conv_block_simple(up6, 256, "conv6_1")
conv6 = conv_block_simple(conv6, 256, "conv6_2")
up7 = concatenate([UpSampling2D()(conv6), conv3], axis=-1)
conv7 = conv_block_simple(up7, 256, "conv7_1")
conv7 = conv_block_simple(conv7, 256, "conv7_2")
up8 = concatenate([UpSampling2D()(conv7), conv2], axis=-1)
conv8 = conv_block_simple(up8, 128, "conv8_1")
conv8 = conv_block_simple(conv8, 128, "conv8_2")
up9 = concatenate([UpSampling2D()(conv8), conv1], axis=-1)
conv9 = conv_block_simple(up9, 64, "conv9_1")
conv9 = conv_block_simple(conv9, 64, "conv9_2")
up10 = concatenate([UpSampling2D()(conv9), base_model.input], axis=-1)
conv10 = conv_block_simple(up10, 48, "conv10_1")
conv10 = conv_block_simple(conv10, 32, "conv10_2")
conv10 = SpatialDropout2D(0.4)(conv10)
x = Conv2D(1, (1, 1), activation="sigmoid", name="prediction")(conv10)
model = Model(base_model.input, x)
return model
示例6: test_tiny_conv_dropout_random
# 需要导入模块: from keras.layers import core [as 别名]
# 或者: from keras.layers.core import SpatialDropout2D [as 别名]
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_model(model)
示例7: multi_conv_layer
# 需要导入模块: from keras.layers import core [as 别名]
# 或者: from keras.layers.core import SpatialDropout2D [as 别名]
def multi_conv_layer(x, layers, size, dropout, batch_norm):
from keras.layers import Conv2D
from keras.layers.normalization import BatchNormalization
from keras.layers.core import SpatialDropout2D, Activation
for i in range(layers):
x = Conv2D(size, (3, 3), padding='same')(x)
if batch_norm is True:
x = BatchNormalization(axis=1)(x)
x = Activation('relu')(x)
if dropout > 0:
x = SpatialDropout2D(dropout)(x)
return x
示例8: double_conv_layer
# 需要导入模块: from keras.layers import core [as 别名]
# 或者: from keras.layers.core import SpatialDropout2D [as 别名]
def double_conv_layer(inputs, filter):
conv = Conv2D(filter, (3, 3), padding='same', kernel_initializer='he_normal')(inputs)
conv = BatchNormalization(axis=3)(conv)
conv = Activation('relu')(conv)
conv = Conv2D(filter, (3, 3), padding='same', kernel_initializer='he_normal')(conv)
conv = BatchNormalization(axis=3)(conv)
conv = Activation('relu')(conv)
conv = SpatialDropout2D(0.1)(conv)
return conv
示例9: bottleneck
# 需要导入模块: from keras.layers import core [as 别名]
# 或者: from keras.layers.core import SpatialDropout2D [as 别名]
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
示例10: bottleneck
# 需要导入模块: from keras.layers import core [as 别名]
# 或者: from keras.layers.core import SpatialDropout2D [as 别名]
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