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RNN层的输入输出要求为(batch, timesteps, num_classes),为了接入RNN经过以上操作,那么又引出一个Time Step的概念,所以timesteps的值也是 outputs_shape[1],而CTC Loss要求的输入为 [batch_size, frames, num_labels],若是 timesteps 小于标签数则无法计算损失,也就无法找损失函数中找到极小值,梯度何以下降。
原输出(batch_size, outputs_shape[1], outputs_shape[2], outputs_shape[3]),RNN层的输入输出要求为(batch, timesteps, num_classes),为了接入RNN经过以上操作,那么又引出一个Time Step的概念,所以timesteps的值也是 outputs_shape[1],而CTC Loss要求的输入为 [batch_size, frames, num_labels],若是 time...
原输出(batch_size, outputs_shape[1], outputs_shape[2], outputs_shape[3]),RNN层的输入输出要求为(batch, timesteps, num_classes),为了接入RNN经过以上操作,那么又引出一个Time Step的概念,所以timesteps的值也是 outputs_shape[1],而CTC Loss要求的输入为 [batch_size, frames, num_labels],若是 time...
原输出(batch_size, outputs_shape[1], outputs_shape[2], outputs_shape[3]),RNN层的输入输出要求为(batch, timesteps, num_classes),为了接入RNN经过以上操作,那么又引出一个Time Step的概念,所以timesteps的值也是 outputs_shape[1],而CTC Loss要求的输入为 [batch_size, frames, num_labels],若是 time...
原输出(batch_size, outputs_shape[1], outputs_shape[2], outputs_shape[3]),RNN层的输入输出要求为(batch, timesteps, num_classes),为了接入RNN经过以上操作,那么又引出一个Time Step的概念,所以timesteps的值也是 outputs_shape[1],而CTC Loss要求的输入为 [batch_size, frames, num_labels],若是 time...
原输出(batch_size, outputs_shape[1], outputs_shape[2], outputs_shape[3]),RNN层的输入输出要求为(batch, timesteps, num_classes),为了接入RNN经过以上操作,那么又引出一个Time Step的概念,所以timesteps的值也是 outputs_shape[1],而CTC Loss要求的输入为 [batch_size, frames, num_labels],若是 time...