weights Evaluate Model •Visualise Tensor • Tensor? • PyTorch Tensors are just like numpy arrays, but they can run on GPU. • Examples: And more operations like: Indexing, slicing, reshape, transpose grad_fn – history of operations for autograd • t.grad_fn Loading Data, Devices and CUDA • Numpy arrays to PyTorch tensors • torch.from_numpy(x_train) • Returns a cpu tensor! • PyTorch tensor to numpy

metrics import r2_score 基本建模流程 9 2.Scikit-learn主要用法 ✓Scikit-learn支持以NumPy的arrays对象、Pandas对象、SciPy的稀 疏 矩阵及其他可转换为数值型arrays的数据结构作为其输入，前提是 数据必须是数值型的 ✓sklearn.datasets模块提供了一系列加载和获取著名数据集如鸢尾 花、波士顿房价、Olive

Theano Theano is an open source deep learning library that allows you to evaluate multi- dimensional arrays effectively. We can easily install using the below command: pip install theano By default, keras 'dense_15/BiasAdd:0' shape=(?, 8) dtype=float32>]  model.get_weights - Returns all the weights as NumPy arrays.  model.set_weights(weight_numpy_array) - Set the weights of the model. Serialize the model categorical_accuracy Keras 63 Model Training Models are trained by NumPy arrays using fit(). The main purpose of this fit function is used to evaluate your model on training. This

History 对象。 异常 • RuntimeError: 如果模型从未编译。 例 def generate_arrays_from_file(path): while 1: f = open(path) for line in f: # create Numpy arrays of input data # and labels, from each line in the file file x, y = process_line(line) yield (x, y) 模型 47 f.close() model.fit_generator(generate_arrays_from_file('/my_file.txt'), steps_per_epoch=1000, epochs=10) 4.2.3.9 evaluate_generator evaluate_generator(self utils.Sequence) 实 例同用。 • initial_epoch: 开始训练的轮次（有助于恢复之前的训练）。 返回 一个 History 对象。 例 def generate_arrays_from_file(path): while 1: f = open(path) for line in f: # 从文件中的每一行生成输入数据和标签的 numpy 数组， x1, x2

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, y_train), (x_test, y_test)`. MNIST 数据集 样例可视化 MNIST Softmax 网络介绍 感知机模型 1957年，受 Warren

__getitem__(2) __getitem__(3) __getitem__(4) 0 1 2 3 4 Tensors ● High-dimensional matrices (arrays) 1-D tensor e.g. audio 2-D tensor e.g. black&white images 3-D tensor e.g. RGB images Tensors

否希望数据迭代器对象在每个迭代周期内打乱数据。 def load_array(data_arrays, batch_size, is_train=True): #@save """构造一个PyTorch数据迭代器""" dataset = data.TensorDataset(*data_arrays) return data.DataLoader(dataset, batch_size, num_steps) data_arrays = (src_array, src_valid_len, tgt_array, tgt_valid_len) (continues on next page) 362 9. 现代循环神经网络 (continued from previous page) data_iter = d2l.load_array(data_arrays, batch_size)