tensorflow 基本应用学习报告

# -*- coding: utf-8 -*-
"""
Created on Mon Apr 25 00:23:34 2022

@author: 又双叒叕莹
"""

import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
train_images.shape
len(train_labels)
train_labels
test_images.shape
len(test_labels)
plt.figure()
plt.imshow(train_images[0])
plt.colorbar()
plt.grid(False)
plt.show()
train_images = train_images / 255.0
test_images = test_images / 255.0
plt.figure(figsize=(10,10))
for i in range(25):
    plt.subplot(5,5,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(train_images[i], cmap=plt.cm.binary)
    plt.xlabel(class_names[train_labels[i]])
plt.show()
model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(10)
])
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=10)

 

 

第五章课后习题：

1. tensorflow和pytorch

2. 直接给变量赋值初始化 和 使用初始化函数初始化

3. 序贯式 和 函数式

序贯式一：

import tensorflow as tf
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(32, activation='relu', input_dim=784))
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dense(10, activation='softmax'))

序贯式二：

import tensorflow as tf
imput_layer = tf.keras.layers.Input(shape=(784,))
hid1_layer = tf.keras.layers.Dense(32, activation='relu')
hid2_layer = tf.keras.layers.Dense(128, activation='relu')
output_layers = tf.keras.layers.Dense(10, activation='softmax') #将层的列表传给Sequential的构造函数
model = tf.keras.Sequential(layers=[imput_layer, hid1_layer, hid2_layer, output_layers])

函数式：

import tensorflow as tf
#创建一个模型，包含一个输入层和三个全连接层
inputs = tf.keras.layers.Input(shape=(4))
x=tf.keras.layers.Dense(32,activation='relu')(inputs)
x=tf.keras.layers.Dense(64,activation='relu')(x)
outputs=tf.keras.layers.Dense(3,activation='softmax')(x)
model=tf.keras.Model(inputs=inputs,outputs =outputs)

4.

import torch
data=torch.Tensor(3,5)
print(dat

5. Keras,Caffe,MXNet，Sonnet