Promising results both visually and quantitatively Fully Scalable Arbitrary input size Arbitrary scale factor Arbitrary temporal frames Our Method 44 45 Data from Vid4 [Ce Liu et al.] Motion Estimation ???? = ???????????? + 1 skip connections Input size: Fully convolutional Decoder Arbitrary Scale Factors 50 2× 3× 4× Parameter Free ???????????????????????? ???????????? ????????????0 ????? hour / frame Scale Factor: 4× Frames: 31 MFSR [Ma et al, 2015] Running Time 65 10 min / frame Scale Factor: 4× Frames: 31 DESR [Liao et al, 2015] Running Time 66 / frame Scale Factor: 4× Frames:

random_uniform_variable(shape, mean, scale) Here,  shape - denotes the rows and columns in the format of tuples.  mean - mean of uniform distribution.  scale - standard deviation of uniform distribution with the specified scale. from keras.models import Sequential from keras.layers import Activation, Dense from keras import initializers my_init = initializers.VarianceScaling(scale=1.0, mode='fan_in' add(Dense(512, activation='relu', input_shape=(784,), kernel_initializer=my_init)) where,  scale represent the scaling factor  mode represent any one of fan_in, fan_out and fan_avg values 

divided equally amongst the range [xmin, xmax], each bin covers a range of (where, s is referred to as scale). Hence, [xmin, xmin + s) will map to the bin 0, [xmin + s, xmin + 2s) will map to bin 1, [xmin + # numpy is one of the most useful libraries for ML. import numpy as np def get_scale(x_min, x_max, b): # Compute scale as discussed. return (x_max - x_min ) * 1.0 / (2**b) """Quantizing the given vector minimum(x, x_max) x = np.maximum(x, x_min) # Compute scale as discussed. scale = get_scale(x_min, x_max, b) x_q = np.floor((x - x_min) / scale) # Clamping the quantized value to be less than (2^b -

Building Intelligent Systems with Large Scale Deep Learning 1990s��������������� Jeff Dean, Google Brain Team, Building Intelligent Systems with Large Scale Deep Learning ������������������ Jeff Dean Team, Building Intelligent Systems with Large Scale Deep Learning ����� Google ��� Jeff Dean, Google Brain Team, Building Intelligent Systems with Large Scale Deep Learning TensorFlow � Jeff Dean ���� Intel • Uber • �� • �� • ... TensorFlow ����� DistBelief - Google ��������������� Jeff Dean, Large Scale Distributed Deep Networks, NIPS 2012 TensorFlow - Google ��������������� • ���������� • �����������

create a tensor with a normal (gaussian) distribution. x = np.random.normal(size=50000, loc=0.0, scale=1.0) num_clusters = 8 x_decoded, centroids, reconstruction_error = simulate_clustering( x, num_clusters number of clusters ( ). Figure 5-7 (b) shows the plot. Note that both the x and y axes are in log-scale. Finally, figure 5-7 (c) compares the reconstruction errors between quantization and clustering on clusters (both x and y axes are in log scale). (c) Comparison of reconstruction errors for both clustering and quantization with the same x. Again, both axes are in log scale. Now that we have verified how

BatchNormalization [source] keras.layers.BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', movi 移动均值和移动方差的动量。 • epsilon: 增加到方差的小的浮点数，以避免除以零。 • center: 如果为 True，把 beta 的偏移量加到标准化的张量上。如果为 False，beta 被忽略。 • scale: 如果为 True，乘以 gamma。如果为 False，gamma 不使用。当下一层为线性层（或者 例如 nn.relu），这可以被禁用，因为缩放将由下一层完成。 • beta_initializer: 返回值 一个 Keras Model 对象。 参考文献 预训练模型 APPLICATIONS 164 • Very Deep Convolutional Networks for Large-Scale Image Recognition：如果在研究中使用了 VGG，请引用该论文。 License 预训练权值由 VGG at Oxford 发布的预训练权值移植而来，基于 Creative Commons

我们现在可以创建一个函数来可视化这些样本。 def show_images(imgs, num_rows, num_cols, titles=None, scale=1.5): #@save """绘制图像列表""" figsize = (num_cols * scale, num_rows * scale) _, axes = d2l.plt.subplots(num_rows, num_cols, figsize=figsize) # labels的维度:(n_train+n_test,) labels = np.dot(poly_features, true_w) labels += np.random.normal(scale=0.1, size=labels.shape) 同样，存储在poly_features中的单项式由gamma函数重新缩放，其中Γ(n) = (n − 1)!。从生成的数据集中查 看一下前2个 1)中，ˆµB是小批量B的样本均值，ˆσB是小批量B的样本标准差。应用标准化后，生成的小批量的平均 值为0和单位方差为1。由于单位方差（与其他一些魔法数）是一个主观的选择，因此我们通常包含 拉伸参数 （scale）γ和偏移参数（shift）β，它们的形状与x相同。请注意，γ和β是需要与其他模型参数一起学习的参数。 由于在训练过程中，中间层的变化幅度不能过于剧烈，而批量规范化将每一层主动居中，并将它们重新调整

using heuristics like the top N most-frequent words in the corpus. It is often straightforward to scale up or down the model quality by increasing or decreasing these two parameters respectively. The exact your models for fewer epochs. However, we should discuss a couple of follow-up topics around how to scale them to NLP applications and beyond. My embedding table is huge! Help me! While embedding tables architecture itself. The embedding table approach removes this bottleneck, at which point we can scale the embedding model’s quality and footprint metrics as discussed. We can combine other ideas from

framework TFX - �� TensorFlow ���������� Baylor, Denis, et al. "Tfx: A tensorflow-based production-scale machine learning platform." Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge 2017. TFX - �� TensorFlow ���������� Baylor, Denis, et al. "Tfx: A tensorflow-based production-scale machine learning platform." Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge

应用：使用 RetinaNet 检测货架商品 “Hello TensorFlow” Try it！ 扩展：目标检测常用数据集综述 通用目标检测数据集 • The ImageNet Large Scale Visual Recognition Challenge ILSVRC • The PASCAL Visual Object Classes (VOC) Challenge Pascal VOC ‘sports ball’, ‘kite’, ‘baseball bat’, ‘baseball glove’, ‘skateboard’, ‘surfboard’,…] IMAGENET Large Scale Visual Recognition Challenge (ILSVRC) ILSVRC 具体信息： 识别小类: 21841 图像总数: 1400万+ 带有 Bounding box 的图像总数: