基于深度学习的USPS手写体识别模型.zip

import tensorflow as tf import xlrd import USPS_forward import os BATCH_SIZE = 200 LEARNING_RATE_BASE = 0.1 LEARNING_RATE_DECAY = 0.99 REGULARIZER = 0.0001 STEPS = 50000 TEST_NUM = 600 MOVING_AVERAGE_DECAY = 0.99 MODEL_SAVE_PATH="./model/" MODEL_NAME="USPS_model" FILE_NAME="USPS_Classification.xlsx" def backward(data, label): x = tf.placeholder(tf.float32, shape = (None, USPS_forward.INPUT_NODE)) y_ = tf.placeholder(tf.float32, shape = (None, USPS_forward.OUTPUT_NODE)) y = USPS_forward.forward(x, REGULARIZER) global_step = tf.Variable(0, trainable=False) ce = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1)) cem = tf.reduce_mean(ce) loss = cem + tf.add_n(tf.get_collection('losses')) learning_rate = tf.train.exponential_decay( LEARNING_RATE_BASE, global_step, len(data) / BATCH_SIZE, LEARNING_RATE_DECAY, staircase=True) train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step) ema = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step) ema_op = ema.apply(tf.trainable_variables()) with tf.control_dependencies([train_step, ema_op]): train_op = tf.no_op(name='train') saver = tf.train.Saver() with tf.Session() as sess: init_op = tf.global_variables_initializer() sess.run(init_op) ckpt = tf.train.get_checkpoint_state(MODEL_SAVE_PATH) if ckpt and ckpt.model_checkpoint_path: saver.restore(sess, ckpt.model_checkpoint_path) for i in range(STEPS): start = (i*BATCH_SIZE)%TEST_NUM end = start+BATCH_SIZE _, loss_value, step = sess.run([train_op, loss, global_step], feed_dict={x: data[start:end], y_: label[start:end]}) if i % 10 == 0: print("After %d training step(s), loss on training batch is %g." % (step, loss_value)) saver.save(sess, os.path.join(MODEL_SAVE_PATH, MODEL_NAME), global_step=global_step) def main(): wb = xlrd.open_workbook(filename=FILE_NAME) Train_sheet = wb.sheet_by_name('Train Feature') Label_sheet = wb.sheet_by_name('Train Label') data = [] label = [] temp = [0,0,0,0,0,0,0,0,0,0] for i in range(TEST_NUM): data.append(Train_sheet.row_values(i)) temp[int(Label_sheet.cell_value(i, 0)) - 1] = 1 label.append(temp) temp = [0,0,0,0,0,0,0,0,0,0] print(data) print(label) backward(data, label) if __name__ == '__main__': main()