trainer.py

mnist-master/common/trainer.py

@@ -0,0 +1,78 @@
+# 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 common.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):
+        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
+        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))
+