true; you can wait for other targets after an equal delimiter (compared after Unicode simple case folding, which is a more general form of case-insensitivity): oc wait --for=condition=Ready=false pod/busybox1

parameter to change the x-axis to log scale (GH2327) • Series arithmetic operators can now handle constant and ndarray input (GH2574) • ExcelFile now takes a kind argument to specify the file type (GH2613) “sparse” versions of the standard data structures for storing data that is mostly missing or mostly constant (some fixed value) • Moving window statistics (rolling mean, rolling standard deviation, etc.) I/O functions. For example, if you wanted to compare the Gross Domestic Products per capita in constant dollars in North America, you would use the search function: In [1]: from pandas.io import wb

parameter to change the x-axis to log scale (GH2327) • Series arithmetic operators can now handle constant and ndarray input (GH2574) • ExcelFile now takes a kind argument to specify the file type (GH2613) “sparse” versions of the standard data structures for storing data that is mostly missing or mostly constant (some fixed value) • Moving window statistics (rolling mean, rolling standard deviation, etc.) I/O functions. For example, if you wanted to compare the Gross Domestic Products per capita in constant dollars in North America, you would use the search function: In [1]: from pandas.io import wb

a dtype that utilizes memory based on the level size. In prior versions, the memory usage was a constant 8 bytes per element in each level. In addition, in prior versions, the reported memory usage was ewmcov() calculation of de-biasing factors when bias=False (the default). Previously an incorrect constant factor was used, based on adjust=True, ignore_na=True, and an infinite number of observations. Now 25 3 1.25 dtype: float64 Note that entry 0 is approximately 0, and the debiasing factors are a constant 1.25. By comparison, the following 0.15.0 results have a NaN for entry 0, and the debiasing factors

a dtype that utilizes memory based on the level size. In prior versions, the memory usage was a constant 8 bytes per element in each level. In addition, in prior versions, the reported memory usage was ewmcov() calculation of de-biasing factors when bias=False (the default). Previously an incorrect constant factor was used, based on adjust=True, ignore_na=True, and an infinite number of observations. Now 25 3 1.25 dtype: float64 Note that entry 0 is approximately 0, and the debiasing factors are a constant 1.25. By comparison, the following 0.15.0 results have a NaN for entry 0, and the debiasing factors

a dtype that utilizes memory based on the level size. In prior versions, the memory usage was a constant 8 bytes per element in each level. In addition, in prior versions, the reported memory usage was ewmcov() calculation of de-biasing factors when bias=False (the default). Previously an incorrect constant factor was used, based on adjust=True, ignore_na=True, and an infinite number of observations. Now analysis toolkit, Release 0.17.0 Note that entry 0 is approximately 0, and the debiasing factors are a constant 1.25. By comparison, the following 0.15.0 results have a NaN for entry 0, and the debiasing factors

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

“sparse” versions of the standard data structures for storing data that is mostly missing or mostly constant (some fixed value) • Moving window statistics (rolling mean, rolling standard deviation, etc.) substitute a fill_value for missing data in one of the inputs Parameters other : Series, DataFrame, or constant axis : {0, 1, ‘index’, ‘columns’} For Series input, axis to match Series index on fill_value substitute a fill_value for missing data in one of the inputs Parameters other : Series, DataFrame, or constant axis : {0, 1, ‘index’, ‘columns’} For Series input, axis to match Series index on fill_value

“sparse” versions of the standard data structures for storing data that is mostly missing or mostly constant (some fixed value) • Moving window statistics (rolling mean, rolling standard deviation, etc.) substitute a fill_value for missing data in one of the inputs Parameters other : Series, DataFrame, or constant axis : {0, 1, ‘index’, ‘columns’} For Series input, axis to match Series index on fill_value substitute a fill_value for missing data in one of the inputs Parameters other : Series, DataFrame, or constant axis : {0, 1, ‘index’, ‘columns’} For Series input, axis to match Series index on fill_value