Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions paper ‣ A flow-based neural network model from the WaveGlow: A Flow-based Generative Network for Speech Synthesis paper. This model script Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions paper ‣ A flow-based neural network model from the WaveGlow: A Flow-based Generative Network for Speech Synthesis paper. This model script Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions paper ‣ A flow-based neural network model from the WaveGlow: A Flow-based Generative Network for Speech Synthesis paper. This model script

. . . . . . . . . . . . . . . . . . . . 129 6.3.2.3 flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 6.3.2.4 flow_from_directory . . . . . . . . . . . . . . . . . . . 是单个张量（对于单输出 Sequential 模型）。 模型 42 • __**kwargs__: 当使用 Theano/CNTK 后端时，这些参数被传入 K.function。当使用 Tensor- Flow 后端时，这些参数被传递到 tf.Session.run。 异常 • ValueError: 如果 optimizer, loss, metrics 或 sample_weight_mode 这些参数不合法。 是单个张量（单输出模型），张量列表，或一个映射输出名称到目标张量的字典。 • __**kwargs__: 当使用 Theano/CNTK 后端时，这些参数被传入 K.function。当使用 Tensor- Flow 后端时，这些参数被传递到 tf.Session.run。 异常 • ValueError: 如果 optimizer, loss, metrics 或 sample_weight_mode 这些参数不合法。

nlpaug.augmenter import word as naw from nlpaug.augmenter import char as nac from nlpaug import flow as naf [nltk_download(item) for item in ['punkt', 'wordnet']] aug_args = dict(aug_p=0.3, aug_max=40) ='crop', **aug_args), naw.RandomWordAug(action='substitute', **aug_args), nac.KeyboardAug() ] flow = naf.Sometimes(chain, pipeline_p=0.3) The nlpaug_fn() function just wraps up the augmentation calls kick-off a run. tds = train500_ds.shuffle(500, reshuffle_each_iteration=True) tds = tds.map(nlpaug_fn(flow)).batch(16).map(vectorize_fn) vds = val_ds.batch(64).map(vectorize_fn) # Reset the model state before

process until the best algorithm (model) is found. The above steps can be represented using below flow chart: Keras 16 Keras 17 Keras provides a complete framework do some computation and finally output the transformed information. The output of one layer will flow into the next layer as its input. Let us learn complete details about layers in this chapter. Introduction

comparison Model Fusion Gray Update Auto Evaluation Log Server Graph Abstraction Data Flow API Manager Pipeline AVA 弹性深度学习平 台 L1 L2 L3 L4 L5 原子API 基础模型 感知层1 API 感知层2 API Vision

境 • 良好的用户体验 • 完善的客户端工具 • 任务进度微信提醒 SACC2017 proto model graph. pb 深度网络计算图 caffe Tensor Flow 公共计算库 X86 优化 Android 优化 iOS 优化 GPU 优化 内存池 硬件设备 xx-arm-gpu-sdk.c xx-android-arm.c xx-randroid-sdk

hidden word. Refer to Figure 4-9 for a visual depiction of the above training method. Figure 4-9: A flow depicting the final step of training the embedding table. In the beginning, the embedding table has

6.3.3 优化目标 一般把神经网络从输入到输出的计算过程叫做前向传播(Forward Propagation)或前向计 算。神经网络的前向传播过程，也是数据张量(Tensor)从输入流动(Flow)至输出层的过程， 即从输入数据开始，途径各个隐藏层，直至得到模型输出并计算误差，这也是另一个深度 学习框架 TensorFlow 的名字由来。PyTorch 框架是继承了 Torch 框架的设计理念，并采用

et al., 2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., & others (2001). Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. [Hochreiter & Schmidhuber,