代码之家 › 专栏 › 技术社区 › BigBadMe

在for循环中创建新的序列模型(使用Keras)

keras neural-network python

BigBadMe · 技术社区 · 8 年前

model = None
batch_generator = None

for sequence_length in all_sequence_length:
    for label_periods in all_label_periods:
        for num_layers in all_num_layers:
            for num_units in all_num_units:
                loadFiles()
                createmodel()
                trainmodel()

第一次迭代创建如下模型:

Layer (type)                 Output Shape              Param #
=================================================================
cu_dnnlstm_1 (CuDNNLSTM)     (None, 100, 75)           45300
_________________________________________________________________
dropout_1 (Dropout)          (None, 100, 75)           0
_________________________________________________________________
cu_dnnlstm_2 (CuDNNLSTM)     (None, 100, 75)           45600
_________________________________________________________________
dropout_2 (Dropout)          (None, 100, 75)           0
_________________________________________________________________
cu_dnnlstm_3 (CuDNNLSTM)     (None, 100, 75)           45600
_________________________________________________________________
dropout_3 (Dropout)          (None, 100, 75)           0
_________________________________________________________________
cu_dnnlstm_4 (CuDNNLSTM)     (None, 100, 75)           45600
_________________________________________________________________
dropout_4 (Dropout)          (None, 100, 75)           0
_________________________________________________________________
cu_dnnlstm_5 (CuDNNLSTM)     (None, 100, 75)           45600
_________________________________________________________________
dropout_5 (Dropout)          (None, 100, 75)           0
_________________________________________________________________
dense_1 (Dense)              (None, 3)                 228
=================================================================

model.fit_generator() 训练模型,执行良好。然后在下一个循环迭代中再次创建模型,摘要如下所示:

Layer (type)                 Output Shape              Param #
=================================================================
cu_dnnlstm_6 (CuDNNLSTM)     (None, 100, 75)           45300
_________________________________________________________________
dropout_6 (Dropout)          (None, 100, 75)           0
_________________________________________________________________
cu_dnnlstm_7 (CuDNNLSTM)     (None, 100, 75)           45600
_________________________________________________________________
dropout_7 (Dropout)          (None, 100, 75)           0
_________________________________________________________________
cu_dnnlstm_8 (CuDNNLSTM)     (None, 100, 75)           45600
_________________________________________________________________
dropout_8 (Dropout)          (None, 100, 75)           0
_________________________________________________________________
cu_dnnlstm_9 (CuDNNLSTM)     (None, 100, 75)           45600
_________________________________________________________________
dropout_9 (Dropout)          (None, 100, 75)           0
_________________________________________________________________
cu_dnnlstm_10 (CuDNNLSTM)     (None, 100, 75)           45600
_________________________________________________________________
dropout_10 (Dropout)          (None, 100, 75)           0
_________________________________________________________________
dense_2 (Dense)              (None, 3)                 228
=================================================================

当我为模型变量创建一个新的序列模型时,您将看到层id增加了,这让我很惊讶,因此我希望得到与第一个相同的摘要。

当我打电话的时候 型号.fit\u发电机() 我得到这个错误:

InvalidArgumentError(回溯见上文):必须输入一个值对于占位符张量'cu\u dnnlstm\u 1\u input',带有数据类型float和shape

cu_dnnlstm_1_input ,这是第一个迭代模型上的输入,而不是第二个模型上的cu\ U dnnlstm\ U 6。我创建模型的代码是在一个函数中完成的:

def createmodel():

    global model

    model = Sequential()
    model.add( CuDNNLSTM(units=num_units, return_sequences=True, input_shape=(sequence_length, features_size) ) )

    for _ in range(num_layers):
        model.add( Dropout(dropout_rate) )
        model.add( CuDNNLSTM(units=num_units, return_sequences=True) )

    model.add( Dropout(dropout_rate) )
    model.add( CuDNNLSTM(units=num_units, return_sequences=False) )

    model.add( Dropout(dropout_rate) )
    model.add( Dense(labels_size) )

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

    model.summary()

模型使用以下功能进行训练:

def trainmodel():

    global model

    model.fit_generator(generator=batch_generator,
        epochs=num_epochs,
        steps_per_epoch=num_steps_per_epoch,
        validation_data=validation_data_tuple,
        callbacks=callbacks)

1 回复 | 直到 8 年前

A cup of tea 8 年前

我想这是因为Keras试图在同一张张量流图上创建不同的模型。由于您的模型有不同的体系结构,它无法做到这一点。

尝试导入tensorflow:

import tensorflow as tf

并通过以下方式修改循环:

for sequence_length in all_sequence_length:
    for label_periods in all_label_periods:
        for num_layers in all_num_layers:
            for num_units in all_num_units:
                graph = tf.Graph()
                with tf.Session(graph=graph):
                    loadFiles()
                    createmodel()
                    trainmodel()