我的网络架构是 7 层 CNN 和 2 层 BiLSTM 的组合，当我训练我的模型时，它显示过拟合，解决这个问题的解决方案之一是架构中的 Dropout。我们如何在这个网络架构中添加 dropout。
# input with shape of height=42 and width=600
inputs = Input(shape=(42,600,1))
# convolution layer with kernel size (3,3)
conv_1 = Conv2D(64, (3,3), activation = 'relu', padding='same')(inputs)
# poolig layer with kernel size (2,2)
pool_1 = MaxPool2D(pool_size=(2, 2), strides=2)(conv_1)
conv_2 = Conv2D(128, (3,3), activation = 'relu', padding='same')(pool_1)
pool_2 = MaxPool2D(pool_size=(2, 2), strides=2)(conv_2)
conv_3 = Conv2D(256, (3,3), activation = 'relu', padding='same')(pool_2)
conv_4 = Conv2D(256, (3,3), activation = 'relu', padding='same')(conv_3)
# poolig layer with kernel size (2,1)
pool_4 = MaxPool2D(pool_size=(2, 1))(conv_4)
conv_5 = Conv2D(512, (3,3), activation = 'relu', padding='same')(pool_4)
# Batch normalization layer
batch_norm_5 = BatchNormalization()(conv_5)
conv_6 = Conv2D(512, (3,3), activation = 'relu', padding='same')(batch_norm_5)
batch_norm_6 = BatchNormalization()(conv_6)
pool_6 = MaxPool2D(pool_size=(2, 1))(batch_norm_6)
conv_7 = Conv2D(512, (2,2), activation = 'relu')(pool_6)
squeezed = Lambda(lambda x: K.squeeze(x, 1))(conv_7)
# bidirectional LSTM layers with units=128
blstm_1 = Bidirectional(LSTM(128, return_sequences=True, dropout = 0.5))(squeezed)
blstm_2 = Bidirectional(LSTM(128, return_sequences=True, dropout = 0.5))(blstm_1)
outputs = Dense(len(char_list)+1, activation = 'softmax')(blstm_2)
# model to be used at test time
act_model = Model(inputs, outputs)
训练模型的准确率和损失图为：
