neuron-multi_output.py

import tensorflow as tf
import os
import numpy as np
import pandas as pd
import pickle
bath_path = r'C:\Users\ext_renqq\Desktop\cifar-10-batches-py'
# print(os.listdir(bath_path))
# 多分类 10个分类
def load_data(filename):
    with open(filename, 'rb') as f:
        data = pickle.load(f, encoding='bytes')
        data['data'] = data[b'data']
        data['labels'] = data[b'labels']
        data['batch_label'] = data[b'batch_label']
        data['filenames'] = data[b'filenames']
        return data['data'], data['labels']

class CifarData(object):
    def __init__(self, filenames, need_shuffle):
        all_data = []
        all_labels = []
        for filename in filenames:
            data, labels = load_data(filename)
            all_data.append(data)
            all_labels.append(labels)
        self._data = np.vstack(all_data)
        self._data = self._data / 127.5 - 1
        self._labels = np.hstack(all_labels)
        print(self._data.shape)
        print(self._labels.shape)

        self._num_examples = self._data.shape[0]
        self._need_shuffle = need_shuffle
        self._indicator = 0
        if self._need_shuffle:
            self._shuffle_data()

    def _shuffle_data(self):
        p = np.random.permutation(self._num_examples)
        self._data = self._data[p]
        self._labels = self._labels[p]

    def next_batch(self, batch_size):
        end_indicator = self._indicator + batch_size
        if end_indicator > self._num_examples:
            if self._need_shuffle:
                self._shuffle_data()
                self._indicator = 0
                end_indicator = batch_size
            else:
                raise Exception('have no more examples')
        if end_indicator > self._num_examples:
            raise Exception('batch size is larger than all examples')
        batch_data = self._data[self._indicator:end_indicator]
        batch_labels = self._labels[self._indicator:end_indicator]
        self._indicator = end_indicator
        return batch_data, batch_labels

train_filenames = [os.path.join(bath_path, 'data_batch_%d' % i) for i in range(1, 6)]
test_filenames = [os.path.join(bath_path, 'test_batch')]
train_data = CifarData(train_filenames, True)
test_data = CifarData(test_filenames, False)
x = tf.placeholder(tf.float32, [None, 3072])
y = tf.placeholder(tf.int64, [None])
hidden1 = tf.layers.dense(x, 100, activation=tf.nn.relu)
hidden2 = tf.layers.dense(hidden1, 100, activation=tf.nn.relu)
hidden3 = tf.layers.dense(hidden2, 50, activation=tf.nn.relu)
y_ = tf.layers.dense(hidden3, 10)
loss = tf.losses.sparse_softmax_cross_entropy(labels=y, logits=y_)
# 分为10类，在y_分出的10类中选出每行最大值的下标
predict = tf.argmax(y_, 1)
# correct_prediction [1, 0, 1, 0, 1, 1, 1]
correct_prediction = tf.equal(predict, y)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float64))

with tf.name_scope('train_op'):
    train_op = tf.train.AdamOptimizer(1e-3).minimize(loss)

init = tf.global_variables_initializer()
batch_size = 20
train_steps = 10000
test_steps = 100

with tf.Session() as sess:
    sess.run(init)
    for i in range(train_steps):
        batch_data, batch_labels = train_data.next_batch(batch_size)
        loss_val, acc_val, _ = sess.run(
            [loss, accuracy, train_op],
            feed_dict={
                x: batch_data,
                y: batch_labels
            }
        )
        if (i + 1) % 500 == 0:
            print('[Train] Step: %d, loss:%.5f, acc:%.5f'\
                  % (i + 1, loss_val, acc_val))
        if (i + 1) % 5000 == 0:
            test_data = CifarData(test_filenames, False)
            all_test_acc_val = []
            for j in range(test_steps):
                test_batch_data, test_batch_labels \
                 = test_data.next_batch(batch_size)
                test_acc_val = sess.run(
                    [accuracy],
                    feed_dict={
                        x: test_batch_data,
                        y:test_batch_labels
                    }
                )
                all_test_acc_val.append(test_acc_val)
            test_acc = np.mean(all_test_acc_val)
            print('[Test] Step: %d, acc: %.4f' % (i + 1, test_acc))