本文整理匯總了Python中keras.layers.Convolution3D方法的典型用法代碼示例。如果您正苦於以下問題：Python layers.Convolution3D方法的具體用法？Python layers.Convolution3D怎麽用？Python layers.Convolution3D使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類keras.layers的用法示例。
在下文中一共展示了layers.Convolution3D方法的13個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: conv_block
# 需要導入模塊: from keras import layers [as 別名]
# 或者: from keras.layers import Convolution3D [as 別名]
def conv_block(x, nb_filter, nb0, nb1, nb2, border_mode='same', subsample=(1, 1, 1), bias=True, batch_norm=False):
if K.image_dim_ordering() == "th":
channel_axis = 1
else:
channel_axis = -1
x = Convolution3D(nb_filter, nb0, nb1, nb2, subsample=subsample, border_mode=border_mode, bias=bias)(x)
if batch_norm:
assert not bias
x = BatchNormalization(axis=channel_axis)(x)
else:
assert bias
x = Activation('relu')(x)
return x 示例2: dsrff3D
# 需要導入模塊: from keras import layers [as 別名]
# 或者: from keras.layers import Convolution3D [as 別名]
def dsrff3D(image_size, num_labels):
num_channels=1
inputs = Input(shape = (image_size, image_size, image_size, num_channels))
# modified VGG19 architecture
bn_axis = 3
m = Convolution3D(32, 3, 3, 3, activation='relu', border_mode='same')(inputs)
m = Convolution3D(32, 3, 3, 3, activation='relu', border_mode='same')(m)
m = MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2))(m)
m = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same')(m)
m = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same')(m)
m = MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2))(m)
m = Flatten(name='flatten')(m)
m = Dense(512, activation='relu', name='fc1')(m)
m = Dense(512, activation='relu', name='fc2')(m)
m = Dense(num_labels, activation='softmax')(m)
mod = KM.Model(inputs=inputs, outputs=m)
return mod 示例3: conv_block
# 需要導入模塊: from keras import layers [as 別名]
# 或者: from keras.layers import Convolution3D [as 別名]
def conv_block(x, nb_filter, nb0, nb1, nb2, border_mode='same', subsample=(1, 1, 1), bias=True, batch_norm=False):
from keras.layers import Input, Dense, Convolution3D, MaxPooling3D, UpSampling3D, Reshape, Flatten, Activation
from keras.layers.normalization import BatchNormalization
from keras import backend as K
if K.image_dim_ordering() == "th":
channel_axis = 1
else:
channel_axis = -1
x = Convolution3D(nb_filter, nb0, nb1, nb2, subsample=subsample, border_mode=border_mode, bias=bias)(x)
if batch_norm:
assert not bias
x = BatchNormalization(axis=channel_axis)(x)
else:
assert bias
x = Activation('relu')(x)
return x 示例4: res_block
# 需要導入模塊: from keras import layers [as 別名]
# 或者: from keras.layers import Convolution3D [as 別名]
def res_block(input_tensor, nb_filters=16, block=0, subsample_factor=1):
subsample = (subsample_factor, subsample_factor, subsample_factor)
x = BatchNormalization(axis=4)(input_tensor)
x = Activation('relu')(x)
x = Convolution3D(nb_filters, 3, 3, 3, subsample=subsample, border_mode='same')(x)
x = BatchNormalization(axis=4)(x)
x = Activation('relu')(x)
x = Convolution3D(nb_filters, 3, 3, 3, subsample=(1, 1, 1), border_mode='same')(x)
if subsample_factor > 1:
shortcut = Convolution3D(nb_filters, 1, 1, 1, subsample=subsample, border_mode='same')(input_tensor)
else:
shortcut = input_tensor
x = merge([x, shortcut], mode='sum')
return x 示例5: inception3D
# 需要導入模塊: from keras import layers [as 別名]
# 或者: from keras.layers import Convolution3D [as 別名]
def inception3D(image_size, num_labels):
num_channels=1
inputs = Input(shape = (image_size, image_size, image_size, num_channels))
m = Convolution3D(32, 5, 5, 5, subsample=(1, 1, 1), activation='relu', border_mode='valid', input_shape=())(inputs)
m = MaxPooling3D(pool_size=(2, 2, 2), strides=None, border_mode='same')(m)
# inception module 0
branch1x1 = Convolution3D(32, 1, 1, 1, subsample=(1, 1, 1), activation='relu', border_mode='same')(m)
branch3x3_reduce = Convolution3D(32, 1, 1, 1, subsample=(1, 1, 1), activation='relu', border_mode='same')(m)
branch3x3 = Convolution3D(64, 3, 3, 3, subsample=(1, 1, 1), activation='relu', border_mode='same')(branch3x3_reduce)
branch5x5_reduce = Convolution3D(16, 1, 1, 1, subsample=(1, 1, 1), activation='relu', border_mode='same')(m)
branch5x5 = Convolution3D(32, 5, 5, 5, subsample=(1, 1, 1), activation='relu', border_mode='same')(branch5x5_reduce)
branch_pool = MaxPooling3D(pool_size=(2, 2, 2), strides=(1, 1, 1), border_mode='same')(m)
branch_pool_proj = Convolution3D(32, 1, 1, 1, subsample=(1, 1, 1), activation='relu', border_mode='same')(branch_pool)
#m = merge([branch1x1, branch3x3, branch5x5, branch_pool_proj], mode='concat', concat_axis=-1)
from keras.layers import concatenate
m = concatenate([branch1x1, branch3x3, branch5x5, branch_pool_proj],axis=-1)
m = AveragePooling3D(pool_size=(2, 2, 2), strides=(1, 1, 1), border_mode='valid')(m)
m = Flatten()(m)
m = Dropout(0.7)(m)
# expliciately seperate Dense and Activation layers in order for projecting to structural feature space
m = Dense(num_labels, activation='linear')(m)
m = Activation('softmax')(m)
mod = KM.Model(input=inputs, output=m)
return mod 示例6: define_model
# 需要導入模塊: from keras import layers [as 別名]
# 或者: from keras.layers import Convolution3D [as 別名]
def define_model(image_shape):
img_input = Input(shape=image_shape)
x = Convolution3D(16, 3, 3, 3, subsample=(1, 1, 1), border_mode='same')(img_input)
x = res_block(x, nb_filters=16, block=0, subsample_factor=1)
x = res_block(x, nb_filters=16, block=0, subsample_factor=1)
x = res_block(x, nb_filters=16, block=0, subsample_factor=1)
x = res_block(x, nb_filters=32, block=1, subsample_factor=2)
x = res_block(x, nb_filters=32, block=1, subsample_factor=1)
x = res_block(x, nb_filters=32, block=1, subsample_factor=1)
x = res_block(x, nb_filters=64, block=2, subsample_factor=2)
x = res_block(x, nb_filters=64, block=2, subsample_factor=1)
x = res_block(x, nb_filters=64, block=2, subsample_factor=1)
x = res_block(x, nb_filters=64, block=2, subsample_factor=1)
x = res_block(x, nb_filters=128, block=3, subsample_factor=2)
x = res_block(x, nb_filters=128, block=3, subsample_factor=1)
x = res_block(x, nb_filters=128, block=3, subsample_factor=1)
x = res_block(x, nb_filters=128, block=3, subsample_factor=1)
x = res_block(x, nb_filters=256, block=4, subsample_factor=2)
x = res_block(x, nb_filters=256, block=4, subsample_factor=1)
x = res_block(x, nb_filters=256, block=4, subsample_factor=1)
x = res_block(x, nb_filters=256, block=4, subsample_factor=1)
x = BatchNormalization(axis=4)(x)
x = Activation('relu')(x)
x = AveragePooling3D(pool_size=(3, 3, 3), strides=(2, 2, 2), border_mode='valid')(x)
x = Flatten()(x)
x = Dense(1, activation='sigmoid', name='predictions')(x)
model = Model(img_input, x)
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy', 'precision', 'recall', 'fmeasure'])
model.summary()
return model 示例7: define_model
# 需要導入模塊: from keras import layers [as 別名]
# 或者: from keras.layers import Convolution3D [as 別名]
def define_model(image_shape):
img_input = Input(shape=image_shape)
x = Convolution3D(16, 5, 5, 5, subsample=(1, 1, 1), border_mode='same')(img_input)
x = res_block(x, nb_filters=16, block=0, subsample_factor=1)
x = res_block(x, nb_filters=16, block=0, subsample_factor=1)
x = res_block(x, nb_filters=16, block=0, subsample_factor=1)
x = res_block(x, nb_filters=32, block=1, subsample_factor=2)
x = res_block(x, nb_filters=32, block=1, subsample_factor=1)
x = res_block(x, nb_filters=32, block=1, subsample_factor=1)
x = res_block(x, nb_filters=64, block=2, subsample_factor=2)
x = res_block(x, nb_filters=64, block=2, subsample_factor=1)
x = res_block(x, nb_filters=64, block=2, subsample_factor=1)
x = res_block(x, nb_filters=128, block=3, subsample_factor=2)
x = res_block(x, nb_filters=128, block=3, subsample_factor=1)
x = res_block(x, nb_filters=128, block=3, subsample_factor=1)
x = BatchNormalization(axis=4)(x)
x = Activation('relu')(x)
x = AveragePooling3D(pool_size=(4, 4, 8))(x)
x = Flatten()(x)
x = Dense(1, activation='sigmoid', name='predictions')(x)
model = Model(img_input, x)
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy', 'precision', 'recall', 'fmeasure'])
model.summary()
return model 示例8: define_model
# 需要導入模塊: from keras import layers [as 別名]
# 或者: from keras.layers import Convolution3D [as 別名]
def define_model():
img_input = Input(shape=(32, 32, 64, 1))
x = Convolution3D(16, 5, 5, 5, subsample=(1, 1, 1), border_mode='same')(img_input)
x = res_block(x, nb_filters=16, block=0, subsample_factor=1)
x = res_block(x, nb_filters=16, block=0, subsample_factor=1)
x = res_block(x, nb_filters=16, block=0, subsample_factor=1)
x = res_block(x, nb_filters=32, block=1, subsample_factor=2)
x = res_block(x, nb_filters=32, block=1, subsample_factor=1)
x = res_block(x, nb_filters=32, block=1, subsample_factor=1)
x = res_block(x, nb_filters=64, block=2, subsample_factor=2)
x = res_block(x, nb_filters=64, block=2, subsample_factor=1)
x = res_block(x, nb_filters=64, block=2, subsample_factor=1)
x = res_block(x, nb_filters=128, block=3, subsample_factor=2)
x = res_block(x, nb_filters=128, block=3, subsample_factor=1)
x = res_block(x, nb_filters=128, block=3, subsample_factor=1)
x = BatchNormalization(axis=4)(x)
x = Activation('relu')(x)
x = AveragePooling3D(pool_size=(4, 4, 8))(x)
x = Flatten()(x)
x = Dense(1, activation='sigmoid', name='predictions')(x)
model = Model(img_input, x)
model.compile(optimizer='adam', loss='binary_crossentropy')
return model 示例9: build
# 需要導入模塊: from keras import layers [as 別名]
# 或者: from keras.layers import Convolution3D [as 別名]
def build():
model = Sequential()
# Conv layer 1
model.add(Convolution3D(
input_shape = (14,32,32,32),
filters=64,
kernel_size=5,
padding='valid', # Padding method
data_format='channels_first',
))
model.add(LeakyReLU(alpha = 0.1))
# Dropout 1
model.add(Dropout(0.2))
# Conv layer 2
model.add(Convolution3D(
filters=64,
kernel_size=3,
padding='valid', # Padding method
data_format='channels_first',
))
model.add(LeakyReLU(alpha = 0.1))
# Maxpooling 1
model.add(MaxPooling3D(
pool_size=(2,2,2),
strides=None,
padding='valid', # Padding method
data_format='channels_first'
))
# Dropout 2
model.add(Dropout(0.4))
# FC 1
model.add(Flatten())
model.add(Dense(128)) # TODO changed to 64 for the CAM
model.add(LeakyReLU(alpha = 0.1))
# Dropout 3
model.add(Dropout(0.4))
# Fully connected layer 2 to shape (2) for 2 classes
model.add(Dense(2))
model.add(Activation('softmax'))
return model 示例10: srcnn
# 需要導入模塊: from keras import layers [as 別名]
# 或者: from keras.layers import Convolution3D [as 別名]
def srcnn(input_shape=(33,33,110,1)):
#for ROSIS sensor
model = Sequential()
model.add(Convolution3D(64, 9, 9, 7, input_shape=input_shape, activation='relu'))
model.add(Convolution3D(32, 1, 1, 1, activation='relu'))
model.add(Convolution3D(9, 1, 1, 1, activation='relu'))
model.add(Convolution3D(1, 5, 5, 3))
model.compile(Adam(lr=0.00005), 'mse')
return model 開發者ID:MeiShaohui，項目名稱:Hyperspectral-Image-Spatial-Super-Resolution-via-3D-Full-Convolutional-Neural-Network，代碼行數:11，代碼來源:network3d.py
示例11: auto_classifier_model
# 需要導入模塊: from keras import layers [as 別名]
# 或者: from keras.layers import Convolution3D [as 別名]
def auto_classifier_model(img_shape, encoding_dim=128, NUM_CHANNELS=1, num_of_class=2):
input_shape = (None, img_shape[0], img_shape[1], img_shape[2], NUM_CHANNELS)
mask_shape = (None, num_of_class)
# use relu activation for hidden layer to guarantee non-negative outputs are passed to the max pooling layer. In such case, as long as the output layer is linear activation, the network can still accomodate negative image intendities, just matter of shift back using the bias term
input_img = Input(shape=input_shape[1:])
mask = Input(shape=mask_shape[1:])
x = input_img
x = conv_block(x, 32, 3, 3, 3)
x = MaxPooling3D((2, 2, 2), padding ='same')(x)
x = conv_block(x, 32, 3, 3, 3)
x = MaxPooling3D((2, 2, 2), padding ='same')(x)
encoder_conv_shape = [_.value for _ in x.get_shape()] # x.get_shape() returns a list of tensorflow.python.framework.tensor_shape.Dimension objects
x = Flatten()(x)
encoded = Dense(encoding_dim, activation='relu', activity_regularizer=regularizers.l1(10e-5))(x)
encoder = Model(inputs=input_img, outputs=encoded)
x = BatchNormalization()(x)
x = Dense(encoding_dim, activation='relu', activity_regularizer=regularizers.l1(10e-5))(x)
x = Dense(128, activation = 'relu')(x)
x = Dense(num_of_class, activation = 'softmax')(x)
prob = x
# classifier output
classifier = Model(inputs=input_img, outputs=prob)
input_img_decoder = Input(shape=encoder.output_shape[1:])
x = input_img_decoder
x = Dense(np.prod(encoder_conv_shape[1:]), activation='relu')(x)
x = Reshape(encoder_conv_shape[1:])(x)
x = UpSampling3D((2, 2, 2))(x)
x = conv_block(x, 32, 3, 3, 3)
x = UpSampling3D((2, 2, 2))(x)
x = conv_block(x, 32, 3, 3, 3)
x = Convolution3D(1, (3, 3, 3), activation='linear', padding ='same')(x)
decoded = x
# autoencoder output
decoder = Model(inputs=input_img_decoder, outputs=decoded)
autoencoder = Sequential()
for l in encoder.layers:
autoencoder.add(l)
last = None
for l in decoder.layers:
last = l
autoencoder.add(l)
decoded = autoencoder(input_img)
auto_classifier = Model(inputs=input_img, outputs=[decoded, prob])
auto_classifier.summary()
return auto_classifier 示例12: get_net
# 需要導入模塊: from keras import layers [as 別名]
# 或者: from keras.layers import Convolution3D [as 別名]
def get_net(input_shape=(CUBE_SIZE, CUBE_SIZE, CUBE_SIZE, 1), load_weight_path=None, features=False, mal=False) -> Model:
inputs = Input(shape=input_shape, name="input_1")
x = inputs
x = AveragePooling3D(pool_size=(2, 1, 1), strides=(2, 1, 1), border_mode="same")(x)
x = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same', name='conv1', subsample=(1, 1, 1))(x)
x = MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2), border_mode='valid', name='pool1')(x)
# 2nd layer group
x = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same', name='conv2', subsample=(1, 1, 1))(x)
x = MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), border_mode='valid', name='pool2')(x)
if USE_DROPOUT:
x = Dropout(p=0.3)(x)
# 3rd layer group
x = Convolution3D(256, 3, 3, 3, activation='relu', border_mode='same', name='conv3a', subsample=(1, 1, 1))(x)
x = Convolution3D(256, 3, 3, 3, activation='relu', border_mode='same', name='conv3b', subsample=(1, 1, 1))(x)
x = MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), border_mode='valid', name='pool3')(x)
if USE_DROPOUT:
x = Dropout(p=0.4)(x)
# 4th layer group
x = Convolution3D(512, 3, 3, 3, activation='relu', border_mode='same', name='conv4a', subsample=(1, 1, 1))(x)
x = Convolution3D(512, 3, 3, 3, activation='relu', border_mode='same', name='conv4b', subsample=(1, 1, 1),)(x)
x = MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), border_mode='valid', name='pool4')(x)
if USE_DROPOUT:
x = Dropout(p=0.5)(x)
last64 = Convolution3D(64, 2, 2, 2, activation="relu", name="last_64")(x)
out_class = Convolution3D(1, 1, 1, 1, activation="sigmoid", name="out_class_last")(last64)
out_class = Flatten(name="out_class")(out_class)
out_malignancy = Convolution3D(1, 1, 1, 1, activation=None, name="out_malignancy_last")(last64)
out_malignancy = Flatten(name="out_malignancy")(out_malignancy)
model = Model(input=inputs, output=[out_class, out_malignancy])
if load_weight_path is not None:
model.load_weights(load_weight_path, by_name=False)
model.compile(optimizer=SGD(lr=LEARN_RATE, momentum=0.9, nesterov=True), loss={"out_class": "binary_crossentropy", "out_malignancy": mean_absolute_error}, metrics={"out_class": [binary_accuracy, binary_crossentropy], "out_malignancy": mean_absolute_error})
if features:
model = Model(input=inputs, output=[last64])
model.summary(line_length=140)
return model 示例13: build
# 需要導入模塊: from keras import layers [as 別名]
# 或者: from keras.layers import Convolution3D [as 別名]
def build(video_shape, audio_spectrogram_size):
model = Sequential()
model.add(ZeroPadding3D(padding=(1, 2, 2), name='zero1', input_shape=video_shape))
model.add(Convolution3D(32, (3, 5, 5), strides=(1, 2, 2), kernel_initializer='he_normal', name='conv1'))
model.add(BatchNormalization())
model.add(LeakyReLU())
model.add(MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2), name='max1'))
model.add(Dropout(0.25))
model.add(ZeroPadding3D(padding=(1, 2, 2), name='zero2'))
model.add(Convolution3D(64, (3, 5, 5), strides=(1, 1, 1), kernel_initializer='he_normal', name='conv2'))
model.add(BatchNormalization())
model.add(LeakyReLU())
model.add(MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2), name='max2'))
model.add(Dropout(0.25))
model.add(ZeroPadding3D(padding=(1, 1, 1), name='zero3'))
model.add(Convolution3D(128, (3, 3, 3), strides=(1, 1, 1), kernel_initializer='he_normal', name='conv3'))
model.add(BatchNormalization())
model.add(LeakyReLU())
model.add(MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2), name='max3'))
model.add(Dropout(0.25))
model.add(TimeDistributed(Flatten(), name='time'))
model.add(Dense(1024, kernel_initializer='he_normal', name='dense1'))
model.add(BatchNormalization())
model.add(LeakyReLU())
model.add(Dropout(0.25))
model.add(Dense(1024, kernel_initializer='he_normal', name='dense2'))
model.add(BatchNormalization())
model.add(LeakyReLU())
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(2048, kernel_initializer='he_normal', name='dense3'))
model.add(BatchNormalization())
model.add(LeakyReLU())
model.add(Dropout(0.25))
model.add(Dense(2048, kernel_initializer='he_normal', name='dense4'))
model.add(BatchNormalization())
model.add(LeakyReLU())
model.add(Dropout(0.25))
model.add(Dense(audio_spectrogram_size, name='output'))
model.summary()
return VideoToSpeechNet(model)