我们从Python开源项目中,提取了以下5个代码示例,用于说明如何使用keras.layers.core.SpatialDropout1D()。
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 build_model(self, loss, P=None): input = Input(shape=(self.maxlen,)) x = Embedding(self.max_features, self.embedding_dims)(input) x = SpatialDropout1D(0.8)(x) x = Activation('relu')(x) x = Flatten()(x) output = Dense(self.classes, kernel_initializer='he_normal')(x) if loss in yes_bound: output = BatchNormalization(axis=1)(output) if loss in yes_softmax: output = Activation('softmax')(output) model = Model(inputs=input, outputs=output) self.compile(model, loss, P)
def build_model(self, loss, P=None): input = Input(shape=(self.maxlen,)) x = Embedding(self.max_features, self.embedding_dims)(input) x = SpatialDropout1D(0.8)(x) x = LSTM(self.lstm_dim, kernel_initializer='uniform')(x) x = Dense(self.embedding_dims, kernel_initializer='he_normal')(x) x = Dropout(0.5)(x) x = Activation('relu')(x) output = Dense(self.classes, kernel_initializer='he_normal')(x) if loss in yes_bound: output = BatchNormalization(axis=1)(output) if loss in yes_softmax: output = Activation('softmax')(output) model = Model(inputs=input, outputs=output) self.compile(model, loss, P)