tensorflow.keras import layers # 导入常见网络层类 然后创建 Softmax 层，并调用__call__方法完成前向计算： In [1]: x = tf.constant([2.,1.,0.1]) # 创建输入张量 layer = layers.Softmax(axis=-1) # 创建 Softmax 层 out = layer(x) # 调用 tensorflow.keras import layers # 导入常见网络层类 然后创建 Softmax 层，并调用__call__方法完成前向计算： In [1]: x = tf.constant([2.,1.,0.1]) # 创建输入张量 layer = layers.Softmax(axis=-1) # 创建 Softmax 层 out = layer(x) # 调用 tf.range(16)+1 x = tf.reshape(x,[1,4,4,1]) x = tf.cast(x, tf.float32) # 创建 3x3 卷积核 w = tf.constant([[-1,2,-3.],[4,-5,6],[-7,8,-9]]) w = tf.expand_dims(w,axis=2) w = tf.expand_dims(w,axis=3)

"tensorflow" } Here,  image_data_format represent the data format.  epsilon represents numeric constant. It is used to avoid DivideByZero error.  floatx represent the default data type float32. You add(Dense(512, activation='relu', input_shape=(784,), kernel_initializer=my_init)) Constant Generates a constant value (say, 5) specified by the user for all input data. Keras 29 initializers my_init = initializers.Constant(value=0) model.add(Dense(512, activation='relu', input_shape=(784,), kernel_initializer=my_init)) where, value represent the constant value RandomNormal Generates

assuming X is a random variable, we have f[E(X)] ≤ E[f(X)] (25) The equality holds if X is a constant. When f is a concave function, the Jensen’s inequality can be re-written as f( N � i=1 λixi) inequality, the equality holds if p(x(i), z(i); θ)/Qi(z(i)) is a constant. Assume p(x(i), z(i); θ) Qi(z(i)) = c (33) where c is a constant. Since � z(i)∈Ω Qi(z(i)) = 1, we have � z(i)∈Ω p(x(i), z(i);

probability distribution on sample space S, we have f [E(X)] ≤ E[f (X)] The equality holds if X is a constant Feng Li (SDU) GDA, NB and EM September 27, 2023 89 / 122 Jensen’s inequality for Concave Function The equality in the Jensen’s inequality holds if p(x(i), z(i); θ) Qi(z(i)) = c where c is a constant Feng Li (SDU) GDA, NB and EM September 27, 2023 96 / 122 The EM Algorithm (Contd.) Tighten the The equality in the Jensen’s inequality holds if p(x(i), z(i); θ) Qi(z(i)) = c where c is a constant Since � z(i)∈Ω Qi(z(i)) = 1 we have � z(i)∈Ω p(x(i), z(i); θ) = c � z(i)∈Ω Qi(z) = c Feng Li

Ones [source] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226 15.2.4 Constant [source] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226 15.2.5 RandomNormal • channel_shift_range: 浮点数。随机通道转换的范围。 • fill_mode: {“constant”, “nearest”, “reflect” or “wrap”} 之一。默认为 ‘nearest’。输入边界以外的 点根据给定的模式填充： – ‘constant’: kkkkkkkk|abcd|kkkkkkkk (cval=k) – ‘nearest’: aaaaaaaa|abcd|dddddddd abcddcba|abcd|dcbaabcd – ‘wrap’: abcdabcd|abcd|abcdabcd • cval: 浮点数或整数。用于边界之外的点的值，当 fill_mode = "constant" 时。 • horizontal_flip: 布尔值。随机水平翻转。 • vertical_flip: 布尔值。随机垂直翻转。 • rescale: 重缩放因子。默认为 None。如果是

models where we cannot get a better quality while holding the latency constant, or we cannot get better latency while holding quality constant, we call just models pareto-optimal, and the set of these pareto-optimal

y(i)(ωT x(i) + b) ≥ γ∥ω∥, ∀i Note that scaling ω and b (e.g., by multiplying both ω and b by the same constant) does not change the hyperplane. Hence, we scale (ω, b) such that min i {y(i)(ωT x(i) + b)} = 1 problem is given min ω,b 1 2∥ω∥2 (30) s.t. y(i)(ωT x(i) + b) ≥ 1, ∀i (31) where we introduce a constant 1/2 so as to simplify our later derivations. 6 Theorem 4. The dual optimization problem of the

of the training set to create training and validation splits. You'll notice a baseline_accuracy constant in DATASET_PARAMS. It will be used later on to compute the initial reward signal. Next, we define which is responsible for spawning child networks, training them, and computing rewards. The layers constant defined in the class indicates the stacking order of the cells. Each element of layers is a pair

地依赖预定义的神经网 络层。 到目前为止，我们网络中的所有操作都对网络的激活值及网络的参数起作用。然而，有时我们可能希望合并 既不是上一层的结果也不是可更新参数的项，我们称之为常数参数（constant parameter）。例如，我们需要 一个计算函数 f(x, w) = c · w⊤x的层，其中x是输入，w是参数，c是某个在优化过程中没有更新的指定常量。 因此我们实现了一个FixedHiddenMLP类，如下所示： init_constant(m): if type(m) == nn.Linear: nn.init.constant_(m.weight, 1) nn.init.zeros_(m.bias) (continues on next page) 5.2. 参数管理 201 (continued from previous page) net.apply(init_constant) net[0] nn.Linear: nn.init.xavier_uniform_(m.weight) def init_42(m): if type(m) == nn.Linear: nn.init.constant_(m.weight, 42) net[0].apply(init_xavier) net[2].apply(init_42) print(net[0].weight.data[0])

(PyTorch commit: c60465873c5cf8f1a36da39f7875224d4c48d7ca), all batch norm multiplier is initialized as constant 1, instead of uniformly distributed between 0 and 1, as it was previously. This has caused accuracy (PyTorch commit: c60465873c5cf8f1a36da39f7875224d4c48d7ca), all batch norm multiplier is initialized as constant 1, instead of uniformly distributed between 0 and 1, as it was previously. This has caused accuracy (PyTorch commit: c60465873c5cf8f1a36da39f7875224d4c48d7ca), all batch norm multiplier is initialized as constant 1, instead of uniformly distributed between 0 and 1, as it was previously. This has caused accuracy