我们从Python开源项目中，提取了以下13个代码示例，用于说明如何使用keras.layers.core.SpatialDropout3D()。
def create_model_2(): inputs = Input((32, 32, 32, 1)) #noise = GaussianNoise(sigma=0.1)(x) conv1 = Convolution3D(32, 3, 3, 3, activation='relu', border_mode='same')(inputs) conv1 = SpatialDropout3D(0.1)(conv1) conv1 = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same')(conv1) pool1 = MaxPooling3D(pool_size=(2,2, 2))(conv1) x = Flatten()(pool1) x = Dense(64, init='normal')(x) x = Dropout(0.5)(x) predictions = Dense(1, init='normal', activation='sigmoid')(x) model = Model(input=inputs, output=predictions) model.summary() optimizer = Adam(lr=1e-5) model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['binary_accuracy','precision','recall','mean_squared_error','accuracy']) return model
def create_model_1(): inputs = Input((32, 32, 32, 1)) #noise = GaussianNoise(sigma=0.1)(x) conv1 = Convolution3D(32, 3, 3, 3, activation='relu', border_mode='same')(inputs) conv1 = SpatialDropout3D(0.1)(conv1) conv1 = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same')(conv1) pool1 = MaxPooling3D(pool_size=(2,2, 2))(conv1) x = Flatten()(pool1) x = Dense(64, init='normal')(x) predictions = Dense(1, init='normal', activation='sigmoid')(x) model = Model(input=inputs, output=predictions) model.summary() optimizer = Adam(lr=1e-5) model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['binary_accuracy','precision','recall','mean_squared_error','accuracy']) return 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 create_model_3_noise2(): inputs = Input((32, 32, 32, 1)) noise = GaussianNoise(sigma=0.02)(inputs) conv1 = Convolution3D(32, 3, 3, 3, activation='relu', border_mode='same')(noise) conv1 = SpatialDropout3D(0.4)(conv1) conv1 = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same')(conv1) pool1 = MaxPooling3D(pool_size=(2,2, 2))(conv1) conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(pool1) conv2 = SpatialDropout3D(0.4)(conv2) conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(conv2) pool2 = MaxPooling3D(pool_size=(2,2, 2))(conv2) x = Flatten()(pool2) x = Dense(128, init='normal')(x) x = Dropout(0.5)(x) x = Dense(64, init='normal')(x) predictions = Dense(1, init='normal', activation='sigmoid')(x) model = Model(input=inputs, output=predictions) model.summary() optimizer = Adam(lr=0.00001) model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['binary_accuracy','precision','recall','mean_squared_error','accuracy']) return model
def create_model_3_noise(): inputs = Input((32, 32, 32, 1)) noise = GaussianNoise(sigma=0.05)(inputs) conv1 = Convolution3D(32, 3, 3, 3, activation='relu', border_mode='same')(noise) conv1 = SpatialDropout3D(0.1)(conv1) conv1 = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same')(conv1) pool1 = MaxPooling3D(pool_size=(2,2, 2))(conv1) conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(pool1) conv2 = SpatialDropout3D(0.1)(conv2) conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(conv2) pool2 = MaxPooling3D(pool_size=(2,2, 2))(conv2) x = Flatten()(pool2) x = Dense(64, init='normal')(x) x = Dropout(0.5)(x) predictions = Dense(1, init='normal', activation='sigmoid')(x) model = Model(input=inputs, output=predictions) model.summary() optimizer = Adam(lr=0.000001) model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['binary_accuracy','precision','recall','mean_squared_error','accuracy']) return model
def create_model_8(): inputs = Input((32, 32, 32, 1)) #noise = GaussianNoise(sigma=0.1)(x) conv1 = Convolution3D(32, 3, 3, 3, activation='relu', border_mode='same')(inputs) conv1 = SpatialDropout3D(0.2)(conv1) conv1 = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same')(conv1) conv1 = SpatialDropout3D(0.2)(conv1) conv1 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(conv1) pool1 = MaxPooling3D(pool_size=(2,2, 2))(conv1) conv2 = Convolution3D(256, 3, 3, 3, activation='relu', border_mode='same')(pool1) conv2 = SpatialDropout3D(0.2)(conv2) conv2 = Convolution3D(512, 3, 3, 3, activation='relu', border_mode='same')(conv2) pool2 = MaxPooling3D(pool_size=(2,2, 2))(conv2) x = Flatten()(pool2) x = Dense(64, init='normal')(x) x = Dropout(0.5)(x) predictions = Dense(1, init='normal', activation='sigmoid')(x) model = Model(input=inputs, output=predictions) model.summary() optimizer = Adam(lr=0.00001) model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['binary_accuracy','precision','recall','mean_squared_error','accuracy']) return model
def create_model_7(): inputs = Input((32, 32, 32, 1)) #noise = GaussianNoise(sigma=0.1)(x) conv1 = Convolution3D(32, 5, 5, 5, activation='relu', border_mode='same')(inputs) conv1 = SpatialDropout3D(0.1)(conv1) conv1 = Convolution3D(64, 5, 5, 5, activation='relu', border_mode='same')(conv1) pool1 = MaxPooling3D(pool_size=(2,2, 2))(conv1) conv2 = Convolution3D(128, 5, 5, 5, activation='relu', border_mode='same')(pool1) conv2 = SpatialDropout3D(0.1)(conv2) conv2 = Convolution3D(128, 5, 5, 5, activation='relu', border_mode='same')(conv2) pool2 = MaxPooling3D(pool_size=(2,2, 2))(conv2) x = Flatten()(pool2) x = Dense(64, init='normal')(x) x = Dropout(0.5)(x) predictions = Dense(1, init='normal', activation='sigmoid')(x) model = Model(input=inputs, output=predictions) model.summary() optimizer = Adam(lr=0.00001) model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['binary_accuracy','precision','recall','mean_squared_error','accuracy']) return model
def create_model_6(): inputs = Input((32, 32, 32, 1)) #noise = GaussianNoise(sigma=0.1)(x) conv1 = Convolution3D(32, 3, 3, 3, activation='relu', border_mode='same')(inputs) conv1 = SpatialDropout3D(0.1)(conv1) conv1 = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same')(conv1) conv1 = SpatialDropout3D(0.1)(conv1) conv1 = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same')(conv1) pool1 = MaxPooling3D(pool_size=(2,2, 2))(conv1) conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(pool1) conv2 = SpatialDropout3D(0.1)(conv2) conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(conv2) conv2 = SpatialDropout3D(0.1)(conv2) conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(conv2) pool2 = MaxPooling3D(pool_size=(2,2, 2))(conv2) x = Flatten()(pool2) x = Dense(64, init='normal')(x) x = Dropout(0.5)(x) predictions = Dense(1, init='normal', activation='sigmoid')(x) model = Model(input=inputs, output=predictions) model.summary() optimizer = Adam(lr=0.00001) model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['binary_accuracy','precision','recall','mean_squared_error','accuracy']) return model
def create_model_4(): inputs1 = Input((32, 32, 32, 1)) inputs2 = Input((6,)) #noise = GaussianNoise(sigma=0.1)(x) conv1 = Convolution3D(32, 3, 3, 3, activation='relu', border_mode='same')(inputs1) conv1 = SpatialDropout3D(0.1)(conv1) conv1 = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same')(conv1) pool1 = MaxPooling3D(pool_size=(2,2, 2))(conv1) conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(pool1) conv2 = SpatialDropout3D(0.1)(conv2) conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(conv2) pool2 = MaxPooling3D(pool_size=(2,2, 2))(conv2) x = Flatten()(pool2) x = merge([x, inputs2], mode='concat') x = Dense(64, init='normal')(x) x = Dropout(0.5)(x) predictions = Dense(1, init='normal', activation='sigmoid')(x) model = Model(input=[inputs1,inputs2], output=predictions) model.summary() optimizer = Adam() model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['binary_accuracy','precision','recall','mean_squared_error','accuracy']) return model
def create_model_3(): inputs = Input((32, 32, 32, 1)) #noise = GaussianNoise(sigma=0.1)(x) conv1 = Convolution3D(32, 3, 3, 3, activation='relu', border_mode='same')(inputs) conv1 = SpatialDropout3D(0.1)(conv1) conv1 = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same')(conv1) pool1 = MaxPooling3D(pool_size=(2,2, 2))(conv1) conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(pool1) conv2 = SpatialDropout3D(0.1)(conv2) conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(conv2) pool2 = MaxPooling3D(pool_size=(2,2, 2))(conv2) x = Flatten()(pool2) x = Dense(64, init='normal')(x) x = Dropout(0.5)(x) predictions = Dense(1, init='normal', activation='sigmoid')(x) model = Model(input=inputs, output=predictions) model.summary() optimizer = Adam(lr=0.00001) model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['binary_accuracy','precision','recall','mean_squared_error','accuracy']) return model
def unet_model(): inputs = Input(shape=(1, max_slices, img_size, img_size)) conv1 = Convolution3D(width, 3, 3, 3, activation = 'relu', border_mode='same')(inputs) conv1 = BatchNormalization(axis = 1)(conv1) conv1 = Convolution3D(width*2, 3, 3, 3, activation = 'relu', border_mode='same')(conv1) conv1 = BatchNormalization(axis = 1)(conv1) pool1 = MaxPooling3D(pool_size=(2, 2, 2), strides = (2, 2, 2), border_mode='same')(conv1) conv2 = Convolution3D(width*2, 3, 3, 3, activation = 'relu', border_mode='same')(pool1) conv2 = BatchNormalization(axis = 1)(conv2) conv2 = Convolution3D(width*4, 3, 3, 3, activation = 'relu', border_mode='same')(conv2) conv2 = BatchNormalization(axis = 1)(conv2) pool2 = MaxPooling3D(pool_size=(2, 2, 2), strides = (2, 2, 2), border_mode='same')(conv2) conv3 = Convolution3D(width*4, 3, 3, 3, activation = 'relu', border_mode='same')(pool2) conv3 = BatchNormalization(axis = 1)(conv3) conv3 = Convolution3D(width*8, 3, 3, 3, activation = 'relu', border_mode='same')(conv3) conv3 = BatchNormalization(axis = 1)(conv3) pool3 = MaxPooling3D(pool_size=(2, 2, 2), strides = (2, 2, 2), border_mode='same')(conv3) conv4 = Convolution3D(width*8, 3, 3, 3, activation = 'relu', border_mode='same')(pool3) conv4 = BatchNormalization(axis = 1)(conv4) conv4 = Convolution3D(width*8, 3, 3, 3, activation = 'relu', border_mode='same')(conv4) conv4 = BatchNormalization(axis = 1)(conv4) conv4 = Convolution3D(width*16, 3, 3, 3, activation = 'relu', border_mode='same')(conv4) conv4 = BatchNormalization(axis = 1)(conv4) up5 = merge([UpSampling3D(size=(2, 2, 2))(conv4), conv3], mode='concat', concat_axis=1) conv5 = SpatialDropout3D(dropout_rate)(up5) conv5 = Convolution3D(width*8, 3, 3, 3, activation = 'relu', border_mode='same')(conv5) conv5 = Convolution3D(width*8, 3, 3, 3, activation = 'relu', border_mode='same')(conv5) up6 = merge([UpSampling3D(size=(2, 2, 2))(conv5), conv2], mode='concat', concat_axis=1) conv6 = SpatialDropout3D(dropout_rate)(up6) conv6 = Convolution3D(width*4, 3, 3, 3, activation = 'relu', border_mode='same')(conv6) conv6 = Convolution3D(width*4, 3, 3, 3, activation = 'relu', border_mode='same')(conv6) up7 = merge([UpSampling3D(size=(2, 2, 2))(conv6), conv1], mode='concat', concat_axis=1) conv7 = SpatialDropout3D(dropout_rate)(up7) conv7 = Convolution3D(width*2, 3, 3, 3, activation = 'relu', border_mode='same')(conv7) conv7 = Convolution3D(width*2, 3, 3, 3, activation = 'relu', border_mode='same')(conv7) conv8 = Convolution3D(1, 1, 1, 1, activation='sigmoid')(conv7) model = Model(input=inputs, output=conv8) model.compile(optimizer=Adam(lr=1e-5), loss=dice_coef_loss, metrics=[dice_coef]) return model
