我们从Python开源项目中，提取了以下6个代码示例，用于说明如何使用keras.layers.core.MaxoutDense()。
def test_maxout_dense(): from keras import regularizers from keras import constraints layer_test(core.MaxoutDense, kwargs={'output_dim': 3}, input_shape=(3, 2)) layer_test(core.MaxoutDense, kwargs={'output_dim': 3, 'W_regularizer': regularizers.l2(0.01), 'b_regularizer': regularizers.l1(0.01), 'activity_regularizer': regularizers.activity_l2(0.01), 'W_constraint': constraints.MaxNorm(1), 'b_constraint': constraints.MaxNorm(1)}, input_shape=(3, 2))
def get_maxout( size, loss='categorical_crossentropy', optimizer=Adam, optimizer_kwargs={}): # MaxOut network model = Sequential() model.add(MaxoutDense(256, input_shape=(size,), nb_feature=5, init='he_uniform')) model.add(MaxoutDense(128, nb_feature=5)) model.add(Dense(64)) model.add(Activation('relu')) model.add(Dense(25)) model.add(Activation('relu')) model.add(Dense(2)) model.add(Activation('sigmoid')) optimizer = optimizer(**optimizer_kwargs) model.compile(loss=loss, optimizer=optimizer) return model
def test_maxout_dense(self): layer = core.MaxoutDense(10, 10) self._runner(layer)
def build(self): enc_size = self.size_of_env_observation() argument_size = IntegerArguments.size_of_arguments input_enc = InputLayer(batch_input_shape=(self.batch_size, enc_size), name='input_enc') input_arg = InputLayer(batch_input_shape=(self.batch_size, argument_size), name='input_arg') input_prg = Embedding(input_dim=PROGRAM_VEC_SIZE, output_dim=PROGRAM_KEY_VEC_SIZE, input_length=1, batch_input_shape=(self.batch_size, 1)) f_enc = Sequential(name='f_enc') f_enc.add(Merge([input_enc, input_arg], mode='concat')) f_enc.add(MaxoutDense(128, nb_feature=4)) self.f_enc = f_enc program_embedding = Sequential(name='program_embedding') program_embedding.add(input_prg) f_enc_convert = Sequential(name='f_enc_convert') f_enc_convert.add(f_enc) f_enc_convert.add(RepeatVector(1)) f_lstm = Sequential(name='f_lstm') f_lstm.add(Merge([f_enc_convert, program_embedding], mode='concat')) f_lstm.add(LSTM(256, return_sequences=False, stateful=True, W_regularizer=l2(0.0000001))) f_lstm.add(Activation('relu', name='relu_lstm_1')) f_lstm.add(RepeatVector(1)) f_lstm.add(LSTM(256, return_sequences=False, stateful=True, W_regularizer=l2(0.0000001))) f_lstm.add(Activation('relu', name='relu_lstm_2')) # plot(f_lstm, to_file='f_lstm.png', show_shapes=True) f_end = Sequential(name='f_end') f_end.add(f_lstm) f_end.add(Dense(1, W_regularizer=l2(0.001))) f_end.add(Activation('sigmoid', name='sigmoid_end')) f_prog = Sequential(name='f_prog') f_prog.add(f_lstm) f_prog.add(Dense(PROGRAM_KEY_VEC_SIZE, activation="relu")) f_prog.add(Dense(PROGRAM_VEC_SIZE, W_regularizer=l2(0.0001))) f_prog.add(Activation('softmax', name='softmax_prog')) # plot(f_prog, to_file='f_prog.png', show_shapes=True) f_args = [] for ai in range(1, IntegerArguments.max_arg_num+1): f_arg = Sequential(name='f_arg%s' % ai) f_arg.add(f_lstm) f_arg.add(Dense(IntegerArguments.depth, W_regularizer=l2(0.0001))) f_arg.add(Activation('softmax', name='softmax_arg%s' % ai)) f_args.append(f_arg) # plot(f_arg, to_file='f_arg.png', show_shapes=True) self.model = Model([input_enc.input, input_arg.input, input_prg.input], [f_end.output, f_prog.output] + [fa.output for fa in f_args], name="npi") self.compile_model() plot(self.model, to_file='model.png', show_shapes=True)
