一个简单的卷积神经网络——trainer.py

# coding: utf-8 import sys, os sys.path.append(os.pardir) # 为了导入父目录的文件而进行的设定 import numpy as np from optimizer import * class Trainer: """ 进行神经网络的训练的类 """ def __init__(self, network, x_train, t_train, x_test, t_test, epochs=20, mini_batch_size=100, optimizer='SGD', optimizer_param={'lr': 0.01}, evaluate_sample_num_per_epoch=None, verbose=True): # verbose简洁的 self.network = network self.verbose = verbose self.x_train = x_train self.t_train = t_train self.x_test = x_test self.t_test = t_test self.epochs = epochs self.batch_size = mini_batch_size self.evaluate_sample_num_per_epoch = evaluate_sample_num_per_epoch # optimzer（优化） optimizer_class_dict = {'sgd': SGD, 'momentum': Momentum, 'nesterov': Nesterov, 'adagrad': AdaGrad, 'rmsprpo': RMSprop, 'adam': Adam} self.optimizer = optimizer_class_dict[optimizer.lower()](**optimizer_param) self.train_size = x_train.shape[0] self.iter_per_epoch = max(self.train_size / mini_batch_size, 1) self.max_iter = int(epochs * self.iter_per_epoch) self.current_iter = 0 self.current_epoch = 0 self.train_loss_list = [] self.train_acc_list = [] self.test_acc_list = [] def train_step(self): batch_mask = np.random.choice(self.train_size, self.batch_size) x_batch = self.x_train[batch_mask] t_batch = self.t_train[batch_mask] grads = self.network.gradient(x_batch, t_batch) self.optimizer.update(self.network.params, grads) loss = self.network.loss(x_batch, t_batch) self.train_loss_list.append(loss) if self.verbose: print("train loss:" + str(loss)) if self.current_iter % self.iter_per_epoch == 0: self.current_epoch += 1 x_train_sample, t_train_sample = self.x_train, self.t_train x_test_sample, t_test_sample = self.x_test, self.t_test if not self.evaluate_sample_num_per_epoch is None: t = self.evaluate_sample_num_per_epoch x_train_sample, t_train_sample = self.x_train[:t], self.t_train[:t] x_test_sample, t_test_sample = self.x_test[:t], self.t_test[:t] train_acc = self.network.accuracy(x_train_sample, t_train_sample) test_acc = self.network.accuracy(x_test_sample, t_test_sample) self.train_acc_list.append(train_acc) self.test_acc_list.append(test_acc) if self.verbose: print("=== epoch:" + str(self.current_epoch) + ", train acc:" + str(train_acc) + ", test acc:" + str( test_acc) + " ===") self.current_iter += 1 def train(self): for i in range(self.max_iter): self.train_step() test_acc = self.network.accuracy(self.x_test, self.t_test) if self.verbose: print("=============== Final Test Accuracy ===============") print("test acc:" + str(test_acc))