Python keras.engine.topology 模块，Merge() 实例源码

def build_critic(self):
        """Build critic network

        recieve convereted tensor: raw_data, smooted_data, and downsampled_data
        """
        # lower layer
        lower_model = [self.build_network(self.model_config['critic_lower'], input_shape=(self.history_length, self.n_stock, 1)) 
                       for _ in range(1  + self.n_smooth + self.n_down)]
        merged = Merge(lower_model, mode='concat')
        # upper layer
        upper_model = self.build_network(self.model_config['critic_upper'],  model=merged)
        # action layer
        action = self.build_network(self.model_config['critic_action'], input_shape=(self.n_stock,), is_conv=False)
        # output layer
        merged = Merge([upper_model, action], mode='mul')
        model = Sequential()
        model.add(merged)
        model.add(Dense(1))
        return model

def _maybe_merge_inputs(inputs):
    if isinstance(inputs, list) and len(inputs) > 1:
        return Merge(mode='concat')(inputs)
    elif isinstance(inputs, list) and len(inputs) == 1:
        return inputs[0]
    else:
        return inputs

def __build_keras_model(self):
        models = []

        model_artist_id = Sequential()
        model_artist_id.add(Embedding(100, 10, input_length=1))
        model_artist_id.add(Reshape(target_shape=(10,)))
        models.append(model_artist_id)

        model_week = Sequential()
        model_week.add(Embedding(7, 2, input_length=1))
        model_week.add(Reshape(target_shape=(6,)))
        models.append(model_week)

#         model_gender = Sequential()
#         model_gender.add(Embedding(1, 3, input_length=1))
#         model_gender.add(Reshape(target_shape=(3,)))
#         models.append(model_gender)

        model_day = Sequential()
        model_day.add(Embedding(1, 10, input_length=1))
        model_day.add(Reshape(target_shape=(10,)))
        models.append(model_day)

#         model_language = Sequential()
#         model_language.add(Embedding(1, 3, input_length=1))
#         model_language.add(Reshape(target_shape=(3,)))
#         models.append(model_language)

        model_others = Sequential()
        model_others.add(Reshape((self.others_dim,), input_shape=(self.others_dim,)))
        models.append(model_others)

        self.model = Sequential()
        self.model.add(Merge(models, mode='concat'))
        self.model.add(Dense(100, init='uniform'))
        self.model.add(Activation('relu'))
        self.model.add(Dense(200, init='uniform'))
        self.model.add(Activation('relu'))
        self.model.add(Dense(1))

        self.model.compile(loss='mean_absolute_error', optimizer='adam')

def build_actor(self):
        """Build actor network

        recieve convereted tensor: raw_data, smooted_data, and downsampled_data
        """
        # lower layer
        lower_model = [self.build_network(self.model_config['actor_lower'], input_shape=(self.history_length, self.n_stock, 1)) 
                       for _ in range(1  + self.n_smooth + self.n_down)]
        merged = Merge(lower_model, mode='concat')
        # upper layer
        model = self.build_network(self.model_config['actor_upper'],  model=merged)
        return model

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)

def build_critic(self):
        """Build critic network

        recieve transformed tensor: raw_data, smooted_data, and downsampled_data
        """
        nf = self.n_feature
        # layer1
        # smoothed input
        sm_model = [Sequential() for _ in range(self.n_smooth)]
        for m in sm_model:
            m.add(Lambda(lambda x: x,  input_shape=(self.history_length, self.n_stock, 1)))
            m.add(Convolution2D(nb_filter=nf, nb_row=self.k_w, nb_col=1, border_mode='same'))
            m.add(BatchNormalization(mode=2, axis=-1))
            m.add(PReLU())
        # down sampled input
        dw_model = [Sequential() for _ in range(self.n_down)]
        for m in dw_model:
            m.add(Lambda(lambda x: x,  input_shape=(self.history_length, self.n_stock, 1)))
            m.add(Convolution2D(nb_filter=nf, nb_row=self.k_w, nb_col=1, border_mode='same'))
            m.add(BatchNormalization(mode=2, axis=-1))
            m.add(PReLU())
        # raw input
        state = Sequential()
        nf = self.n_feature
        state.add(Lambda(lambda x: x,  input_shape=(self.history_length, self.n_stock, 1)))
        state.add(Convolution2D(nb_filter=nf, nb_row=self.k_w, nb_col=1, border_mode='same'))
        state.add(BatchNormalization(mode=2, axis=-1))
        state.add(PReLU())
        merged = Merge([state,] + sm_model + dw_model, mode='concat', concat_axis=-1)
        # layer2
        nf = nf * 2
        model = Sequential()
        model.add(merged)
        model.add(Convolution2D(nb_filter=nf, nb_row=self.k_w, nb_col=1, border_mode='same'))
        model.add(BatchNormalization(mode=2, axis=-1))
        model.add(PReLU())
        model.add(Flatten())
        # layer3
        model.add(Dense(self.n_hidden))
        model.add(BatchNormalization(mode=1, axis=-1))
        model.add(PReLU())
        # layer4
        model.add(Dense(int(np.sqrt(self.n_hidden))))
        model.add(PReLU())
        # output
        model.add(Dense(2 * self.n_stock))
        model.add(Reshape((self.n_stock, 2)))
        return model