try to remove the noise data and thus prevent the model from over- fitting. Dropout has three arguments and they are as follows: keras.layers.Dropout(rate, noise_shape=None, seed=None)  rate represent (batch_size, 32), then the output shape of the layer will be (batch_size, 16, 32) RepeatVector has one arguments and it is as follows: keras.layers.RepeatVector(n) A simple example to use RepeatVector layers squared before processing. RepeatVector has four arguments and it is as follows: keras.layers.Lambda(function, output_shape=None, mask=None, arguments=None)  function represent the lambda function

__________________________ Next, we initialize the HyperBand tuner. Noteworthy initialization arguments are hypermodel, objective, max_epochs and factor. The hypermodel argument is set to the build_hp_model explained shortly. class CNNCell(): """ It composes a cell based on the input configuration. Arguments: stride: A positive integer to represent the convolution strides. Normal cells use stride=1 and make_cell() is the entry method to the class that is called with the cell_config and the branches arguments. The cell_config argument is a numpy array of shape (5, 5) which contains 5 state choices for each

train_on_batch train_on_batch(self, x, y, class_weight=None, sample_weight=None) 一批样品的单次梯度更新。 Arguments • x: 输入数据，Numpy 数组或列表（如果模型有多输入）。 • y: 标签，Numpy 数组。 • class_weight: 将类别映射为权重的字典，用于在训练时缩放损失函数。 sample_weight=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None) 以固定数量的轮次（数据集上的迭代）训练模型。 Arguments • x: 训练数据的 Numpy 数组（如果模型只有一个输入），或者是 Numpy 数组的列表（如果 模型有多个输入）。如果模型中的输入层被命名，你也可以传递一个字典，将输入层名称 映射到 features)。 5.2.9 Lambda [source] keras.layers.Lambda(function, output_shape=None, mask=None, arguments=None) 将任意表达式封装为 Layer 对象。 例 # 添加一个 x -> x^2 层 model.add(Lambda(lambda x: x ** 2)) 关于 KERAS

contrib.learn 整个模块均已被废弃： 使用 Keras 加载 MNIST 数据集 tf.kera.datasets.mnist.load_data(path=‘mnist.npz’) Arguments: • path:本地缓存 MNIST 数据集(mnist.npz)的相对路径（~/.keras/datasets） Returns： Tuple of Numpy arrays: `(x_train

plot ( 0 : 4 9 , J2 ( 1 : 5 0 ) , ’ r−’ ) ; plot ( 0 : 4 9 , J3 ( 1 : 5 0 ) , ’k−’ ) ; The final arguments ‘b-’, ‘r-’, and ’k-’ specify different plot styles for the plots. Type help plot at the Matlab/Octave

loss=loss, metrics='accuracy' ) return model_for_pruning The below code prepares the input arguments to create a model for pruning. The prunable_blocks variable is the list of names of prunable convolution

= available_functions[function_name] function_args = json.loads(last_response['function_call']['arguments']) function_response = function_to_call( location=function_args.get('location'), unit=function_args

defining the shape of the output tensor. Can be a variable number of arguments or a collection like a list or tuple. Keyword arguments: 82 2. 预备知识 out (Tensor, optional): the output tensor. dtype (torch