神经元的TF实现

安装

版本： Python 2.7 tf 1.8.0 Linux

略

demo

神经网络的TF实现

# py36 tf 2.1.

# import tensorflow as tf

import tensorflow.compat.v1 as tf

tf.compat.v1.disable_eager_execution()

# https://blog.csdn.net/qq_39777550/article/details/104224296

import os

import pickle

import numpy as np

CIFAR_DIR = "./../../cifar-10-batches-py"

print(os.listdir(CIFAR_DIR))

# 读取数据

def load_data(filename):

    """read data from data file."""

    with open(filename, 'rb') as f:

        data = pickle.load(f, encoding='bytes')

        return data[b'data'], data[b'labels']

# tensorflow.Dataset.

class CifarData:

    def __init__(self, filenames, need_shuffle):

        all_data = []

        all_labels = []

        for filename in filenames:

            data, labels = load_data(filename)

            for item, label in zip(data, labels):

                if label in [0, 1]:

                    all_data.append(item)

                    all_labels.append(label)

        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):

        # [0,1,2,3,4,5] -> [5,3,2,4,0,1]

        p = np.random.permutation(self._num_examples)

        self._data = self._data[p]

        self._labels = self._labels[p]

    def next_batch(self, batch_size):

        """return batch_size examples as a batch."""

        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(CIFAR_DIR, 'data_batch_%d' % i) for i in range(1, 6)]

test_filenames = [os.path.join(CIFAR_DIR, 'test_batch')]

train_data = CifarData(train_filenames, True)

test_data = CifarData(test_filenames, False)

# 搭建计算图

x = tf.placeholder(tf.float32, [None, 3072])

# [None] None表示输入样本不确定 可调整

y = tf.placeholder(tf.int64, [None])

# (3072, 1)

w = tf.get_variable('w', [x.get_shape()[-1], 1],

                   initializer=tf.random_normal_initializer(0, 1))

# (1, )

b = tf.get_variable('b', [1],

                   initializer=tf.constant_initializer(0.0))

# [None, 3072] * [3072, 1] = [None, 1]

y_ = tf.matmul(x, w) + b

# [None, 1]

p_y_1 = tf.nn.sigmoid(y_)

# [None, 1]

y_reshaped = tf.reshape(y, (-1, 1))

y_reshaped_float = tf.cast(y_reshaped, tf.float32)

loss = tf.reduce_mean(tf.square(y_reshaped_float - p_y_1))

# bool

predict = p_y_1 > 0.5

# [1,0,1,1,1,0,0,0]

correct_prediction = tf.equal(tf.cast(predict, tf.int64), y_reshaped)

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 = 100000

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: %4.5f, acc: %4.5f' % (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: %4.5f' % (i+1, test_acc))

out:



[Train] Step: 500, loss: 0.20478, acc: 0.75000

[Train] Step: 1000, loss: 0.21155, acc: 0.75000

[Train] Step: 1500, loss: 0.43419, acc: 0.55000

[Train] Step: 2000, loss: 0.20231, acc: 0.80000

[Train] Step: 2500, loss: 0.29795, acc: 0.70000

[Train] Step: 3000, loss: 0.10000, acc: 0.90000

[Train] Step: 3500, loss: 0.20452, acc: 0.80000

[Train] Step: 4000, loss: 0.10001, acc: 0.90000

[Train] Step: 4500, loss: 0.19975, acc: 0.80000

[Train] Step: 5000, loss: 0.19999, acc: 0.80000

(2000, 3072)

(2000,)

[Test ] Step: 5000, acc: 0.79950

[Train] Step: 5500, loss: 0.24914, acc: 0.70000

[Train] Step: 6000, loss: 0.19750, acc: 0.80000

[Train] Step: 6500, loss: 0.14980, acc: 0.85000

[Train] Step: 7000, loss: 0.20100, acc: 0.80000

[Train] Step: 7500, loss: 0.15000, acc: 0.85000

[Train] Step: 8000, loss: 0.25308, acc: 0.75000

[Train] Step: 8500, loss: 0.10026, acc: 0.90000

[Train] Step: 9000, loss: 0.25647, acc: 0.75000

[Train] Step: 9500, loss: 0.23454, acc: 0.75000

[Train] Step: 10000, loss: 0.00000, acc: 1.00000

(2000, 3072)

(2000,)

[Test ] Step: 10000, acc: 0.80750

[Train] Step: 10500, loss: 0.13790, acc: 0.85000

[Train] Step: 11000, loss: 0.09943, acc: 0.90000

[Train] Step: 11500, loss: 0.29916, acc: 0.70000

[Train] Step: 12000, loss: 0.15001, acc: 0.85000

[Train] Step: 12500, loss: 0.25000, acc: 0.75000

[Train] Step: 13000, loss: 0.53574, acc: 0.45000

[Train] Step: 13500, loss: 0.20016, acc: 0.80000

[Train] Step: 14000, loss: 0.30000, acc: 0.70000

[Train] Step: 14500, loss: 0.20156, acc: 0.80000

[Train] Step: 15000, loss: 0.18963, acc: 0.80000

(2000, 3072)

(2000,)

[Test ] Step: 15000, acc: 0.82050

[Train] Step: 15500, loss: 0.12585, acc: 0.85000

[Train] Step: 16000, loss: 0.15000, acc: 0.85000

[Train] Step: 16500, loss: 0.10003, acc: 0.90000

[Train] Step: 17000, loss: 0.15028, acc: 0.85000

[Train] Step: 17500, loss: 0.20604, acc: 0.80000

[Train] Step: 18000, loss: 0.28701, acc: 0.70000

[Train] Step: 18500, loss: 0.28371, acc: 0.70000

[Train] Step: 19000, loss: 0.18576, acc: 0.80000

[Train] Step: 19500, loss: 0.17266, acc: 0.80000

[Train] Step: 20000, loss: 0.05000, acc: 0.95000

(2000, 3072)

(2000,)

[Test ] Step: 20000, acc: 0.82350

[Train] Step: 20500, loss: 0.05006, acc: 0.95000

[Train] Step: 21000, loss: 0.25004, acc: 0.75000

[Train] Step: 21500, loss: 0.10000, acc: 0.90000

[Train] Step: 22000, loss: 0.20000, acc: 0.80000

[Train] Step: 22500, loss: 0.25239, acc: 0.75000

[Train] Step: 23000, loss: 0.19881, acc: 0.80000

[Train] Step: 23500, loss: 0.15887, acc: 0.85000

[Train] Step: 24000, loss: 0.16960, acc: 0.80000

[Train] Step: 24500, loss: 0.14990, acc: 0.85000

[Train] Step: 25000, loss: 0.10783, acc: 0.90000

(2000, 3072)

(2000,)

[Test ] Step: 25000, acc: 0.82450

[Train] Step: 25500, loss: 0.06129, acc: 0.95000

[Train] Step: 26000, loss: 0.25004, acc: 0.75000

[Train] Step: 26500, loss: 0.05000, acc: 0.95000

[Train] Step: 27000, loss: 0.06949, acc: 0.90000

[Train] Step: 27500, loss: 0.15037, acc: 0.85000

[Train] Step: 28000, loss: 0.05000, acc: 0.95000

[Train] Step: 28500, loss: 0.13999, acc: 0.85000

[Train] Step: 29000, loss: 0.11145, acc: 0.90000

[Train] Step: 29500, loss: 0.16517, acc: 0.80000

[Train] Step: 30000, loss: 0.05007, acc: 0.95000

(2000, 3072)

(2000,)

[Test ] Step: 30000, acc: 0.82450

[Train] Step: 30500, loss: 0.20095, acc: 0.80000

[Train] Step: 31000, loss: 0.18170, acc: 0.80000

[Train] Step: 31500, loss: 0.19512, acc: 0.80000

[Train] Step: 32000, loss: 0.00000, acc: 1.00000

[Train] Step: 32500, loss: 0.20135, acc: 0.80000

[Train] Step: 33000, loss: 0.11704, acc: 0.85000

[Train] Step: 33500, loss: 0.05083, acc: 0.95000

[Train] Step: 34000, loss: 0.09994, acc: 0.90000

[Train] Step: 34500, loss: 0.11291, acc: 0.85000

[Train] Step: 35000, loss: 0.10000, acc: 0.90000

(2000, 3072)

(2000,)

[Test ] Step: 35000, acc: 0.82100

[Train] Step: 35500, loss: 0.09685, acc: 0.90000

[Train] Step: 36000, loss: 0.05111, acc: 0.95000

[Train] Step: 36500, loss: 0.15356, acc: 0.85000

[Train] Step: 37000, loss: 0.14997, acc: 0.85000

[Train] Step: 37500, loss: 0.08142, acc: 0.90000

[Train] Step: 38000, loss: 0.31816, acc: 0.65000

[Train] Step: 38500, loss: 0.15094, acc: 0.85000

[Train] Step: 39000, loss: 0.10831, acc: 0.90000

[Train] Step: 39500, loss: 0.14931, acc: 0.85000

[Train] Step: 40000, loss: 0.13410, acc: 0.85000

(2000, 3072)

(2000,)

[Test ] Step: 40000, acc: 0.82400

[Train] Step: 40500, loss: 0.25000, acc: 0.75000

[Train] Step: 41000, loss: 0.18313, acc: 0.80000

[Train] Step: 41500, loss: 0.25164, acc: 0.75000

[Train] Step: 42000, loss: 0.10002, acc: 0.90000

[Train] Step: 42500, loss: 0.20063, acc: 0.80000

[Train] Step: 43000, loss: 0.20210, acc: 0.80000

[Train] Step: 43500, loss: 0.14993, acc: 0.85000

[Train] Step: 44000, loss: 0.27625, acc: 0.70000

[Train] Step: 44500, loss: 0.09901, acc: 0.90000

[Train] Step: 45000, loss: 0.08543, acc: 0.90000

(2000, 3072)

(2000,)

[Test ] Step: 45000, acc: 0.82200

[Train] Step: 45500, loss: 0.15000, acc: 0.85000

[Train] Step: 46000, loss: 0.15736, acc: 0.85000

[Train] Step: 46500, loss: 0.10140, acc: 0.90000

[Train] Step: 47000, loss: 0.05000, acc: 0.95000

[Train] Step: 47500, loss: 0.14900, acc: 0.85000

[Train] Step: 48000, loss: 0.10006, acc: 0.90000

[Train] Step: 48500, loss: 0.25005, acc: 0.75000

[Train] Step: 49000, loss: 0.14953, acc: 0.85000

[Train] Step: 49500, loss: 0.00000, acc: 1.00000

[Train] Step: 50000, loss: 0.15218, acc: 0.85000

(2000, 3072)

(2000,)

[Test ] Step: 50000, acc: 0.82400

[Train] Step: 50500, loss: 0.15024, acc: 0.85000

[Train] Step: 51000, loss: 0.24747, acc: 0.75000

[Train] Step: 51500, loss: 0.10175, acc: 0.90000

[Train] Step: 52000, loss: 0.10000, acc: 0.90000

[Train] Step: 52500, loss: 0.10093, acc: 0.90000

[Train] Step: 53000, loss: 0.04343, acc: 0.90000

[Train] Step: 53500, loss: 0.25575, acc: 0.75000

[Train] Step: 54000, loss: 0.14995, acc: 0.85000

[Train] Step: 54500, loss: 0.05000, acc: 0.95000

[Train] Step: 55000, loss: 0.11438, acc: 0.90000

(2000, 3072)

(2000,)

[Test ] Step: 55000, acc: 0.82300

[Train] Step: 55500, loss: 0.15405, acc: 0.85000

[Train] Step: 56000, loss: 0.05102, acc: 0.95000

[Train] Step: 56500, loss: 0.00000, acc: 1.00000

[Train] Step: 57000, loss: 0.10003, acc: 0.90000

[Train] Step: 57500, loss: 0.10098, acc: 0.90000

[Train] Step: 58000, loss: 0.15034, acc: 0.85000

[Train] Step: 58500, loss: 0.20014, acc: 0.80000

[Train] Step: 59000, loss: 0.10171, acc: 0.90000

[Train] Step: 59500, loss: 0.20040, acc: 0.80000

[Train] Step: 60000, loss: 0.15008, acc: 0.85000

(2000, 3072)

(2000,)

[Test ] Step: 60000, acc: 0.82450

[Train] Step: 60500, loss: 0.10014, acc: 0.90000

[Train] Step: 61000, loss: 0.05013, acc: 0.95000

[Train] Step: 61500, loss: 0.05019, acc: 0.95000

[Train] Step: 62000, loss: 0.05106, acc: 0.95000

[Train] Step: 62500, loss: 0.31838, acc: 0.70000

[Train] Step: 63000, loss: 0.15213, acc: 0.85000

[Train] Step: 63500, loss: 0.21945, acc: 0.75000

[Train] Step: 64000, loss: 0.00000, acc: 1.00000

[Train] Step: 64500, loss: 0.14999, acc: 0.85000

[Train] Step: 65000, loss: 0.20631, acc: 0.80000

(2000, 3072)

(2000,)

[Test ] Step: 65000, acc: 0.82650

[Train] Step: 65500, loss: 0.10079, acc: 0.90000

[Train] Step: 66000, loss: 0.10001, acc: 0.90000

[Train] Step: 66500, loss: 0.10000, acc: 0.90000

[Train] Step: 67000, loss: 0.05000, acc: 0.95000

[Train] Step: 67500, loss: 0.06823, acc: 0.90000

[Train] Step: 68000, loss: 0.25001, acc: 0.75000

[Train] Step: 68500, loss: 0.24419, acc: 0.75000

[Train] Step: 69000, loss: 0.15000, acc: 0.85000

[Train] Step: 69500, loss: 0.15000, acc: 0.85000

[Train] Step: 70000, loss: 0.15198, acc: 0.85000

(2000, 3072)

(2000,)

[Test ] Step: 70000, acc: 0.82400

[Train] Step: 70500, loss: 0.13796, acc: 0.85000

[Train] Step: 71000, loss: 0.15036, acc: 0.85000

[Train] Step: 71500, loss: 0.24917, acc: 0.75000

[Train] Step: 72000, loss: 0.00000, acc: 1.00000

[Train] Step: 72500, loss: 0.16025, acc: 0.85000

[Train] Step: 73000, loss: 0.05124, acc: 0.95000

[Train] Step: 73500, loss: 0.05395, acc: 0.95000

[Train] Step: 74000, loss: 0.15262, acc: 0.85000

[Train] Step: 74500, loss: 0.11369, acc: 0.90000

[Train] Step: 75000, loss: 0.20410, acc: 0.80000

(2000, 3072)

(2000,)

[Test ] Step: 75000, acc: 0.82600

[Train] Step: 75500, loss: 0.20000, acc: 0.80000

[Train] Step: 76000, loss: 0.15000, acc: 0.85000

[Train] Step: 76500, loss: 0.15020, acc: 0.85000

[Train] Step: 77000, loss: 0.15000, acc: 0.85000

[Train] Step: 77500, loss: 0.15294, acc: 0.85000

[Train] Step: 78000, loss: 0.15832, acc: 0.85000

[Train] Step: 78500, loss: 0.25277, acc: 0.75000

[Train] Step: 79000, loss: 0.20001, acc: 0.80000

[Train] Step: 79500, loss: 0.12471, acc: 0.85000

[Train] Step: 80000, loss: 0.01566, acc: 1.00000

(2000, 3072)

(2000,)

[Test ] Step: 80000, acc: 0.82450

[Train] Step: 80500, loss: 0.14999, acc: 0.85000

[Train] Step: 81000, loss: 0.20000, acc: 0.80000

[Train] Step: 81500, loss: 0.10390, acc: 0.90000

[Train] Step: 82000, loss: 0.19998, acc: 0.80000

[Train] Step: 82500, loss: 0.20066, acc: 0.80000

[Train] Step: 83000, loss: 0.20394, acc: 0.80000

[Train] Step: 83500, loss: 0.10256, acc: 0.90000

[Train] Step: 84000, loss: 0.15013, acc: 0.85000

[Train] Step: 84500, loss: 0.15000, acc: 0.85000

[Train] Step: 85000, loss: 0.15002, acc: 0.85000

(2000, 3072)

(2000,)

[Test ] Step: 85000, acc: 0.82300

[Train] Step: 85500, loss: 0.10160, acc: 0.90000

[Train] Step: 86000, loss: 0.15183, acc: 0.85000

[Train] Step: 86500, loss: 0.10011, acc: 0.90000

[Train] Step: 87000, loss: 0.10062, acc: 0.90000

[Train] Step: 87500, loss: 0.10850, acc: 0.90000

[Train] Step: 88000, loss: 0.05128, acc: 0.95000

[Train] Step: 88500, loss: 0.19986, acc: 0.80000

[Train] Step: 89000, loss: 0.20000, acc: 0.80000

[Train] Step: 89500, loss: 0.20552, acc: 0.80000

[Train] Step: 90000, loss: 0.20248, acc: 0.80000

(2000, 3072)

(2000,)

[Test ] Step: 90000, acc: 0.82850

[Train] Step: 90500, loss: 0.15013, acc: 0.85000

[Train] Step: 91000, loss: 0.10157, acc: 0.90000

[Train] Step: 91500, loss: 0.05001, acc: 0.95000

[Train] Step: 92000, loss: 0.19992, acc: 0.80000

[Train] Step: 92500, loss: 0.15054, acc: 0.85000

[Train] Step: 93000, loss: 0.15037, acc: 0.85000

[Train] Step: 93500, loss: 0.05008, acc: 0.95000

[Train] Step: 94000, loss: 0.10047, acc: 0.90000

[Train] Step: 94500, loss: 0.20108, acc: 0.80000

[Train] Step: 95000, loss: 0.00000, acc: 1.00000

(2000, 3072)

(2000,)

[Test ] Step: 95000, acc: 0.82550

[Train] Step: 95500, loss: 0.12996, acc: 0.85000

[Train] Step: 96000, loss: 0.04520, acc: 0.95000

[Train] Step: 96500, loss: 0.12607, acc: 0.85000

[Train] Step: 97000, loss: 0.18041, acc: 0.80000

[Train] Step: 97500, loss: 0.10482, acc: 0.90000

[Train] Step: 98000, loss: 0.20055, acc: 0.80000

[Train] Step: 98500, loss: 0.10218, acc: 0.90000

[Train] Step: 99000, loss: 0.20037, acc: 0.80000

[Train] Step: 99500, loss: 0.15000, acc: 0.85000

[Train] Step: 100000, loss: 0.05154, acc: 0.95000

(2000, 3072)

(2000,)

[Test ] Step: 100000, acc: 0.82550

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