基于多层感知机的手写数字识别

""" 定义训练和测试流程，无需了解 """ import numpy as np import tensorflow.compat.v1 as tf tf.disable_eager_execution() TRAIN_NUM = 55000 # Training VAL_NUM = 5000 # Validation TEST_NUM = 10000 # Test def train(model, criterion, optimizer, dataset, max_epoch, batch_size, disp_freq): avg_train_loss, avg_train_acc = [], [] avg_val_loss, avg_val_acc = [], [] get_next = dataset.batch(TRAIN_NUM).make_one_shot_iterator().get_next() config = tf.ConfigProto() config.gpu_options.allow_growth = True # 允许 GPU 内存动态增长 with tf.Session(config=config) as sess: # split raw training data(60000) to train_set(55000) and val_set(5000) tmp1, tmp2 = sess.run(get_next) tmp1 = tf.data.Dataset.from_tensor_slices(tmp1) tmp2 = tf.data.Dataset.from_tensor_slices(tmp2) train_data = tf.data.Dataset.zip((tmp1, tmp2)).repeat(max_epoch).batch(batch_size) # prepare training batch loader train_get_next = train_data.make_one_shot_iterator().get_next() tmp3, tmp4 = sess.run(get_next) tmp3 = tf.data.Dataset.from_tensor_slices(tmp3) tmp4 = tf.data.Dataset.from_tensor_slices(tmp4) valid_data = tf.data.Dataset.zip((tmp3, tmp4)).repeat(max_epoch).batch(batch_size) # prepare validate batch loader valid_get_next = valid_data.make_one_shot_iterator().get_next() # Training process for epoch in range(max_epoch): batch_train_loss, batch_train_acc = train_one_epoch(model, criterion, optimizer, train_get_next, max_epoch, batch_size, disp_freq, epoch, sess) batch_val_loss, batch_val_acc = validate(model, criterion, valid_get_next, batch_size, sess) avg_train_acc.append(np.mean(batch_train_acc)) avg_train_loss.append(np.mean(batch_train_loss)) avg_val_acc.append(np.mean(batch_val_acc)) avg_val_loss.append(np.mean(batch_val_loss)) print() print('Epoch [{}]\t Average training loss {:.4f}\t Average training accuracy {:.4f}'.format( epoch, avg_train_loss[-1], avg_train_acc[-1])) print('Epoch [{}]\t Average validation loss {:.4f}\t Average validation accuracy {:.4f}'.format( epoch, avg_val_loss[-1], avg_val_acc[-1])) print() return model, avg_val_loss, avg_val_acc def train_one_epoch(model, criterion, optimizer, data_get_next, max_epoch, batch_size, disp_freq, epoch, sess): batch_train_loss, batch_train_acc = [], [] max_train_iteration = TRAIN_NUM // batch_size for iteration in range(max_train_iteration): # Get training data and label train_x, train_y = sess.run(data_get_next) # Forward pass logit = model.forward(train_x) criterion.forward(logit, train_y) # Backward pass delta = criterion.backward() model.backward(delta) # Update weights, see optimize.py optimizer.step(model) # Record loss and accuracy batch_train_loss.append(criterion.loss) batch_train_acc.append(criterion.acc) if iteration % disp_freq == 0: print("Epoch [{}][{}]\t Batch [{}][{}]\t Training Loss {:.4f}\t Accuracy {:.4f}".format( epoch, max_epoch, iteration, max_train_iteration, np.mean(batch_train_loss), np.mean(batch_train_acc))) return batch_train_loss, batch_train_acc def validate(model, criterion, data_get_next, batch_size, sess): batch_val_acc, batch_val_loss = [], [] max_val_iteration = VAL_NUM // batch_size for iteration in range(max_val_iteration): # Get validating data and label val_x, val_y = sess.run(data_get_next) # Only forward pass logit = model.forward(val_x) loss = criterion.forward(logit, val_y) # Record loss and accuracy batch_val_loss.append(criterion.loss) batch_val_acc.append(criterion.acc) return batch_val_loss, batch_val_acc def test(model, criterion, dataset, batch_size, disp_freq): print('Testing...') max_test_iteration = TEST_NUM // batch_size batch_test_acc = [] test_iter = dataset.batch(batch_size).make_one_shot_iterator() get_next = test_iter.get_next() config = tf.ConfigProto(device_count={'GPU': 0}) with tf.Session(config=config) as sess: for iteration in range(max_test_iteration): test_x, test_y = sess.run(get_next) # Only forward pass logit = model.forward(test_x) loss = criterion.forward(logit, test_y) batch_test_acc.append(criterion.acc) print("The test accuracy is {:.4f}.\n".format(np.mean(batch_test_acc))) return np.mean(batch_test_acc)