5f73fe0599380479a37029de1d5647f33aae18c8,main.py,ModelSpeech,CreateModel,#ModelSpeech#,23

Before Change

		
		//_model = Sequential()
		//_model.add(Merge([m_lstm, aenc], mode="concat", concat_axis=-1))
		_model.add(Dense(1279, activation="softmax"))
		_model.compile(optimizer="adam", loss="categorical_crossentropy",metrics=["accuracy"])
		return _model

After Change

		输出层：全连接层，神经元数量为1279，使用softmax作为激活函数，使用CTC的loss作为损失函数
		"""
		// 每一帧使用13维mfcc特征及其13维一阶差分和13维二阶差分表示，最大信号序列长度为1500
		layer_input = Input((1500,39))
		
		layer_h1 = Conv1D(256, 5, use_bias=True, padding="valid")(layer_input) // 卷积层
		layer_h2 = MaxPooling1D(pool_size=2, strides=None, padding="valid")(layer_h1) // 池化层
		layer_h3 = Dropout(0.2)(layer_h2) // 随机中断部分神经网络连接，防止过拟合
		layer_h4 = LSTM(256, activation="relu", use_bias=True)(layer_h3) // LSTM层

Instances