tensorflow2.1 MNIST handwritten digit recognition dataset

1. View the dataset

code:

import tensorflow as tf
from matplotlib import pyplot as plt

mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# 可视化训练集输入特征的第一个元素
plt.imshow(x_train[0], cmap='gray')  # 绘制灰度图
plt.show()

# 打印出训练集输入特征的第一个元素
print("x_train[0]:\n", x_train[0])
# 打印出训练集标签的第一个元素
print("y_train[0]:\n", y_train[0])

# 打印出整个训练集输入特征形状
print("x_train.shape:\n", x_train.shape)
# 打印出整个训练集标签的形状
print("y_train.shape:\n", y_train.shape)
# 打印出整个测试集输入特征的形状
print("x_test.shape:\n", x_test.shape)
# 打印出整个测试集标签的形状
print("y_test.shape:\n", y_test.shape)

result:

2. Realization of digital identification

1. The method of sequentially constructing the network layer model:


import tensorflow as tf

mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
              metrics=['sparse_categorical_accuracy'])

model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test), validation_freq=1)
model.summary()

result:

2. The method of constructing network layer model by class function:

import tensorflow as tf
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras import Model

mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0


class MnistModel(Model):
    def __init__(self):
        super(MnistModel, self).__init__()
        self.flatten = Flatten()
        self.d1 = Dense(128, activation='relu')
        self.d2 = Dense(10, activation='softmax')

    def call(self, x):
        x = self.flatten(x)
        x = self.d1(x)
        y = self.d2(x)
        return y


model = MnistModel()

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
              metrics=['sparse_categorical_accuracy'])

model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test), validation_freq=1)
model.summary()

result:

Reference link:

https://www.icourse163.org/learn/PKU-1002536002?tid=1452937471#/learn/content

In addition, two implementations of fashion clothing recognition classification are attached:

################################################################################################

# import tensorflow as tf
# from tensorflow.keras.layers import Dense, Flatten
# from tensorflow.keras import Model
#
# fashion = tf.keras.datasets.fashion_mnist
# (x_train, y_train),(x_test, y_test) = fashion.load_data()
# x_train, x_test = x_train / 255.0, x_test / 255.0
#
#
# class MnistModel(Model):
#     def __init__(self):
#         super(MnistModel, self).__init__()
#         self.flatten = Flatten()
#         self.d1 = Dense(128, activation='relu')
#         self.d2 = Dense(10, activation='softmax')
#
#     def call(self, x):
#         x = self.flatten(x)
#         x = self.d1(x)
#         y = self.d2(x)
#         return y
#
#
# model = MnistModel()
#
# model.compile(optimizer='adam',
#               loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
#               metrics=['sparse_categorical_accuracy'])
#
# model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test), validation_freq=1)
# model.summary()

################################################################################################

# import tensorflow as tf
#
# fashion = tf.keras.datasets.fashion_mnist
# (x_train, y_train),(x_test, y_test) = fashion.load_data()
# x_train, x_test = x_train / 255.0, x_test / 255.0
#
# model = tf.keras.models.Sequential([
#     tf.keras.layers.Flatten(),
#     tf.keras.layers.Dense(128, activation='relu'),
#     tf.keras.layers.Dense(10, activation='softmax')
# ])
#
# model.compile(optimizer='adam',
#               loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
#               metrics=['sparse_categorical_accuracy'])
#
# model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test), validation_freq=1)
# model.summary()


################################################################################################

tensorflow2.1 MNIST handwritten digit recognition dataset

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