Tensorflow2官方demo跑mnist数据集_tensorflow2 如何调用mnist

内容来源于网上教程，做个记录。

1.tensorflow1和tensorflow2的对比

2.在Pytorch和tensorflow中卷积后的矩阵尺寸大小计算公式区别

Pytorch中：

Tensorflow中：

在默认情况下，Padding=VALID。
当需要用Padding补0时，此时Padding=SAME，这样就不需要去操作Padding的过程，由tensorflow自动完成。
如若想自己控制Padding的过程，可以去调用tensoflow提供的Padding函数。

3.实例

①先下载mnist数据集，可视化查看下部分数据图像

import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np

mnist = tf.keras.datasets.mnist # 手写数字识别数据集
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# 数据归一化处理
x_train, x_test = x_train / 255.0, x_test / 255.0
imgs = x_test[:3] # 查看测试集前3张图像
labs = y_test[:3] # 查看标签
print(labs)
# np.hstack():将图像在水平方向上平铺
plot_imgs = np.hstack(imgs)
plt.imshow(plot_imgs,cmap = 'gray')
plt.show()
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输出：


②模型model.py

from tensorflow.keras.layers import Dense, Flatten, Conv2D
from tensorflow.keras import Model

class MyModel(Model):
    def __init__(self):
        super(MyModel, self).__init__()
        self.conv1 = Conv2D(32, 3, activation='relu') # 卷积核个数32，卷积核大小3*3，激活函数ReLU
        self.flatten = Flatten() # 将维度展平
        self.d1 = Dense(128, activation='relu') # 全连接层1
        self.d2 = Dense(10, activation='softmax') # 全连接层2

    '''正向传播'''
    def call(self, x):
        x = self.conv1(x)      # input[batch, 28, 28, 1] output[batch, 26, 26, 32]
        x = self.flatten(x)    # output [batch, 26*26*32=21632]
        x = self.d1(x)         # output [batch, 128]
        return self.d2(x)      # output [batch, 10]
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③main.py
a.tensorflow官网API查询
b.

@tf.function
# 作用：装饰器，构造高效的python代码
# 在@tf.function下面的代码变为静态图，不能进行断点调试，但运行速度快，提高运算性能。
# 相当于Pytorch中的with torch.no_grad()
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c.

from __future__ import absolute_import, division, print_function, unicode_literals

import tensorflow as tf
from model import MyModel

mnist = tf.keras.datasets.mnist # 手写数字识别数据集

# 下载和载入数据
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

# 增加一个通道的维度
x_train = x_train[..., tf.newaxis]
x_test = x_test[..., tf.newaxis]

# 创建数据生成器
train_ds = tf.data.Dataset.from_tensor_slices(
    (x_train, y_train)).shuffle(10000).batch(32)
test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)

# 创建模型
model = MyModel()

# 定义损失函数
loss_object = tf.keras.losses.SparseCategoricalCrossentropy()
# 定义优化器
optimizer = tf.keras.optimizers.Adam()

# 定义训练集的损失函数和准确率
train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')

# 定义测试集的损失函数和准确率
test_loss = tf.keras.metrics.Mean(name='test_loss')
test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')


# 定义训练函数：计算损失、梯度和准确率
@tf.function
def train_step(images, labels):
    with tf.GradientTape() as tape:
        predictions = model(images)
        loss = loss_object(labels, predictions)
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))

    train_loss(loss)
    train_accuracy(labels, predictions)


# 定义测试函数：计算损失、梯度和准确率
@tf.function
def test_step(images, labels):
    predictions = model(images)
    t_loss = loss_object(labels, predictions)

    test_loss(t_loss)
    test_accuracy(labels, predictions)


EPOCHS = 5

for epoch in range(EPOCHS):
    # 每迭代一轮清空历史信息
    train_loss.reset_states()
    train_accuracy.reset_states()
    test_loss.reset_states()
    test_accuracy.reset_states()

    for images, labels in train_ds:
        train_step(images, labels)

    for test_images, test_labels in test_ds:
        test_step(test_images, test_labels)

    template = 'Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, Test Accuracy: {}'
    print(template.format(epoch + 1,
                          train_loss.result(),
                          train_accuracy.result() * 100,
                          test_loss.result(),
                          test_accuracy.result() * 100))
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输出：